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SPECIAL REPORT

Improving the Early Diagnosis of Dementia and the Role of Artificial Intelligence and Machine Learning Solutions

Data-Driven Clinical Decision Support for Differential Diagnosis in Dementia The Impact of Dementia on Health Services Early Detection: Very Beneficial but Extremely Challenging Artificial Intelligence: Can it Transform Healthcare? How Big Data Can Revolutionize Dementia Care

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Published by Global Business Media

Published by Global Business Media Global Business Media Limited 62 The Street Ashtead Surrey KT21 1AT United Kingdom Switchboard: +44 (0)1737 850 939 Fax: +44 (0)1737 851 952 Email: info@globalbusinessmedia.org Website: www.globalbusinessmedia.org Publisher Kevin Bell

Challenges in Dementia Diagnosis Quantitative Evaluation of MRI Brain Images Combining All Data in a Clinical Decision Support System Integration into Clinical Workflow Impact on Costs and Patient Management

The Impact of Dementia on Health Services

Tom Cropper, Editor

Dementia: The Rising Tide Impact on Lives

Business Development Director Marie-Anne Brooks

The Drive to Improve Detection

Editor Tom Cropper

Early Detection: Very Beneficial 10 but Extremely Challenging

Senior Project Manager Steve Banks Advertising Executives Michael McCarthy Abigail Coombes Production Manager Paul Davies For further information visit: www.globalbusinessmedia.org The opinions and views expressed in the editorial content in this publication are those of the authors alone and do not necessarily represent the views of any organisation with which they may be associated. Material in advertisements and promotional features may be considered to represent the views of the advertisers and promoters. The views and opinions expressed in this publication do not necessarily express the views of the Publishers or the Editor. While every care has been taken in the preparation of this publication, neither the Publishers nor the Editor are responsible for such opinions and views or for any inaccuracies in the articles. ÂŠ 2019. The entire contents of this publication are protected by copyright. Full details are available from the Publishers. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical photocopying, recording or otherwise, without the prior permission of the copyright owner.

Jo Roth, Staff Writer

The Benefits of Early Detection Primary Care Using Biomarkers Turning to Artificial Intelligence

Artificial Intelligence: Can it Transform Healthcare?

James Butler, Staff Writer

The Potential of AI Future Promise Early Developments AI in Dementia Care

How Big Data Can Revolutionize Dementia Care

Tom Cropper, Editor

The Age of Big Data Scaling the Data Mountain Objective Analysis

References 16

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Foreword

very three seconds someone is diagnosed

As Jo Roth discovers elsewhere in the Report, early

with dementia according to Alzheimerâ€™s

diagnosis may not be able to halt the condition or

UK1. One in three people born in the year 2015

even slow it down, but it is possible to help patients

will go on to develop dementia . The population

to make plans and manage their lifestyles.

is aging and so the number of people living with

AI is key to this. But this is a technology which is

degenerative neurological diseases is increasing.

struggling to make headway. James Butler looks

These are striking figures and a reminder that

at how AI is being used in the healthcare sector

the battle against dementia will be one of the key

and whether it can truly achieve everything health

issues for the future of healthcare.

services hope for.

Dementia has a profound impact on the lives of

The answer will lie in the rise of big data.

both people living with the condition and their

Technology is dramatically increasing the amount

families. As the number of people living with the

of data being captured by the NHS. Unfortunately,

condition increases, it also has a profound impact

using it in a secure and efficient manner is far from

on resources. To meet these challenges, there is a

straightforward. AI solutions will be indispensable in

growing focus on the need for a fresh approach to

sorting out the insights from all that data.

diagnosis and treatment.

The future is exciting. Few technologies can really

One area which is seen as offering particular

be said to be truly disruptive, but this is certainly

promise is artificial intelligence. This rapidly

one of them. It can improve the diagnosis process,

developing technology has enormous potential for the

save time and money and also help patients achieve

future and is already driving significant improvements.

a good quality of life. Small wonder itâ€™s getting so

Our opening article comes from Finnish company,

much attention.

Combinostics, who have developed a number of ground-breaking AI solutions designed to enhance the decision making of clinicians at the diagnostics stage. By so doing, they hope to facilitate early diagnosis of the condition.

Tom Cropper Editor

Tom Cropper, has produced articles and reports on various aspects of global business over the past 15 years. He has also worked as a copywriter for some of the largest corporations in the world, including ANZ Bank, ING and KPMG.

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Data-Driven Clinical Decision Support for Differential Diagnosis in Dementia Combinostics Ltd

Quantitative MRI

ESPITE RECENT advances in new biomarkers and improved imaging methods, diagnosis of patients with suspected cognitive disorders remains challenging. As part of the diagnostic workup, patients typically undergo cognitive testing, basic laboratory testing and brain magnetic resonance imaging (MRI). Additional testing is often performed depending on the suspected cause of symptoms, such as biomarkers from cerebrospinal fluid (CSF), positron emission tomography (PET) imaging or comprehensive neuropsychological assessment. It goes without saying that reviewing all this multisource data, and mentally picturing whether single tests or combination of tests points to a certain diagnosis, is a daunting task. It is further complicated by the fact that there are multiple possible diagnoses and other reasons (or diseases) that may cause

the symptoms of the patient. The relative importance of single tests or biomarkers depends on which diagnosis is being considered. The interpretation of test results depends also on background factors, such as the patient’s age or education level. Finally, economic constraints may define which tests are available and afforded. To help overcome the difficulties in dementia diagnosis, Combinostics has developed cNeuro®, a cloud-based software tool with modules for advanced image analytics and for data-driven clinical decision support. The first module, cNeuro® cMRI, computes imaging biomarkers from MRI T1 and FLAIR images. The second module, cNeuro® cDSI combines the imaging biomarkers from cMRI with other data from the patient. cDSI is a machine learning system that has been trained with a large set of data from previously diagnosed patients,

Assess atrophy pattern and amount of white matter hyperintensities and get a summary report at your ﬁngertips

www.cneuro.com

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

The task for the diagnosing clinician, is to use all this disparate information when making the diagnosis. However, it is virtually impossible for the clinician to make use of all the information that is inherent in the data

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with diagnosis confirmed through longitudinal follow-up. cDSI visualizes the comparison of the patient’s data with previous patients in an intuitive and comprehensive way. It allows the clinician to see how the patient’s data fits to corresponding data from previous patients. The clinician gets support for differential diagnosis and for prediction of progression in patients with mild symptoms.

Challenges in Dementia Diagnosis Early and accurate diagnosis of patients presenting with memory difficulties is important but is challenging. The underlying cause of the symptoms may be due to depression or other reversible conditions. The patient may also be found to perform within the normal range for a person of this age and is hence labelled as a, so called, subjective cognitive decline (SCD). If there is objective evidence of impairment on cognitive testing but no substantial decline in functional abilities, the patient may be diagnosed as having mild cognitive impairment (MCI). Patients with an MCI diagnosis may remain stable for several years (sMCI) or may progress to dementia within just a few years (pMCI). Finally, the patient may have dementia. The most common causes of dementia include Alzheimer’s disease (AD), vascular dementia (VaD), dementia with Lewy bodies (DLB), and frontotemporal lobe dementia (FTD). Irrespectively of whether the patient’s symptoms are due to a reversible condition or not, early and precise diagnosis is important for optimal patient management. Historically, brain imaging was used to rule out other pathologies, but current guidelines advise the use of MRI to find evidence for underlying patterns of neurodegeneration. Although Computed Tomography (CT) is still quite common, MRI has become the most important tool in clinical diagnostics of cognitive disorders. Medial temporal lobe atrophy is often seen in typical AD, while young-onset patients with an atypical presentation show more frequent parietal atrophy. In FTD, atrophy is focused on frontal and temporal regions, but hippocampal and overall global atrophy is also present with increasing age. In VaD, white matter hyperintensities (WMHs) are essential; however, WMHs become more abundant with increasing age. DLB patients typically show little atrophy on MRI. In addition, different dementing diseases may co-occur, e.g., mixed dementia of AD and VaD is common, complicating image interpretation. These patterns of neurodegeneration are typically visually assessed by radiologists and findings are summarized in a report. Cognitive testing typically involves the use of a standardized test battery, with tests assessing the patient’s

memory and executive function as well as questionnaires used to obtain information on the presence of psychopathology in the patient. Finally, other tests, such as CSF biomarkers of beta-amyloid 42 and tau may be collected. CSF-biomarker values are typically compared to cutoff values and the patient’s data is reduced to being either positive or negative and whether the patient’s value is close to the cutoff (i.e., weak) or far from the cutoff (i.e., strong) is completely lost. The task for the diagnosing clinician, is to use all this disparate information when making the diagnosis. However, it is virtually impossible for the clinician to make use of all the information that is inherent in the data. This is further complicated by the fact that the radiology report seldom includes detailed information about atrophy in various brain areas. Another challenge is the additional cost for obtaining CSF-biomarkers. To keep cost down, these biomarkers should only be obtained for patients where the addition impacts the diagnosis. However, it is difficult for a clinician to decide for which patients these biomarkers are needed. cNeuro addresses all these challenges and helps to better exploit all data acquired as further described below.

Quantitative Evaluation of MRI Brain Images cNeuro ® cMRI provides fully automated quantification of MRI T1 and FLAIR images. Based on T1 brain images, cMRI computes volumes of 102 cortical parcellations and 31 subcortical structures. Extracted volumes are compared to a normative database, percentiles are computed, and results are presented in the cMRI user interface with structures having volumes outside the normal range being highlighted. Based on FLAIR images, cMRI segments white matter hyperintensities (WMH) and results are compared to a normative WMH database. In addition to the volumes of structures and WMHs, cMRI computes some specific dementia related imaging biomarkers. An anterior-posterior index is used to compare atrophy in the anterior versus the posterior parts of the brain and has been shown to be useful for differentiating AD from FTD. Furthermore, many radiologists, especially in Europe, report atrophy and existence of WMHs using the visual scoring scales MTA, GCA and Fazekas. cMRI provides computed MTA, GCA (global & lobes) and Fazekas scales. There are several advantages of the computed scales over the visual scoring scales; the computed scales are objective; they use a continuous scale and they are compared to normative data. In addition, a recent study shows that computed scales outperform visual ratings in diagnostics of memory diseases.

IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Clinical Decision Support

THE FIGURE SHOWS STRUCTURES SEGMENTED USING T1 IMAGES (UPPER ROW) AND VASCULAR CHANGES IDENTIFIED IN FLAIR IMAGES (LOWER ROW).

All quantitative information is corrected for age, gender and head size and results are presented in tabular form or as age and gender corrected plots. cMRI provides interactive review of images with the ability to toggle overlays on/off for easy assessment of segmentation and allows the user to assess the volume with comparison to normal for any of the 133 brain structures. A summary view shows the key features of interest for evaluation of dementia patients and an “Atrophy and lesion” PDF report is automatically generated. In summary cNeuro® cMRI is a product that helps radiologist provide high quality reporting in shorter time. cNeuro® cMRI is CE marked and FDA 510(k) cleared.

Combining All Data in a Clinical Decision Support System cNeuro® cMRI is a product on its own, providing information about how a patient’s images differ from what is expected in an age and gender matched healthy individual. However, for the diagnosing clinician (i.e., neurologist or geriatrician), imaging information is just one

piece of the puzzle. Furthermore, the neurologist is not primarily interested in whether the patient differs from normal or not, but what the underlying disease is. This is where cDSI comes in. cDSI is a machine learning system that has been trained with data from a large number of previously diagnosed patients where diagnosis is confirmed through longitudinal follow up. Data sources supported include imaging biomarkers from cMRI, various clinical and neuropsychological tests such as MMSE, RAVLT/ CERAD recall & delayed recall, TMT A&B, Animal fluency and NPI, as well as CSF biomarkers and APOE status. cDSI compares the data from the patient being evaluated with all previous data, and for each biomarker / test result, a disease state index (DSI) is computed measuring the similarity of the patient to previously diagnosed patients. DSI is a scalar index from zero to one indicating the most likely condition when comparing the patient data to the reference data of two different groups of conditions, e.g., FTD versus AD. The DSI from different tests are then combined in a hierarchical way hence producing one DSI that represents all data combined. cDSI provides two

Use all available data to get clinical decision support about etiology and risk for progression by comparing the patient’s data to previous patients

www.cneuro.com

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

cDSI is a tool for the diagnosing clinician and it helps clinical decision making by providing an explicit comparison of the patient’s data to data from previous patients, both on the single biomarker level, on an intermediate level and on a level with all data combined THE ETIOLOGY DECISION MODEL HAS BEEN SELECTED AND THE SYSTEM INDICATES THAT THE PATIENT’S DATA IS MOSTLY SIMILAR TO DATA FROM PREVIOUS PATIENTS WITH ALZHEIMER DISEASE DIAGNOSIS (UPPER PART). THE USER CAN REVIEW HOW ALL DATA CONTRIBUTE TO THIS SUGGESTION IN THE DISEASE STATE FINGERPRINT WHICH HAS BEEN SELECTED TO COMPARE ALZHEIMER’S DISEASE (RED) VERSUS FRONTOTEMPORAL DEMENTIA DEMENTIA (BLUE).

decision models, etiology and progression. The etiology model presents information about which underlying etiology is the most likely. Supported etiologies include AD, FTD, VaD and cognitive normal (CN). The progression model compares the patient’s data to data from sMCI and pMCI and provides information about similarity of an MCI patient to one of these two groups. A graphical tree structure, called the disease state fingerprint (DSF) visualizes how each test or biomarker contributes to the overall DSI classification. This allows the clinician to see, with one glance, how different test results or groups of test results affect the patient’s similarity to a certain disease. This transparency of the system is critical for acceptance of these types of tools in clinical routine. In summary cNeuro® cDSI is a tool for the diagnosing clinician and it helps clinical decision making by providing an explicit comparison of the patient’s data to data from previous patients, both on the single biomarker level, on an intermediate level and on a level with all data combined. cNeuro® cDSI is CE marked and in the US, it is excluded from the definition of a device based on section 520(o)(1)(E) of the Food Drug and Cosmetic Act.

Integration into Clinical Workflow cNeuro is cloud based and the user interface runs in a standard web-browser without the need for any software to be installed. Integration with the hospital’s PACS is provided through a cNeuro gateway. The gateway provides data pseudomization ensuring that no patient identifying information leaves the hospital network. cMRI reports are automatically pushed back to the PACS. A user can access a patient case either by logging into the cNeuro portal, or by clicking a link in the pdf report.

Impact on Costs and Patient Management

THE USER CAN INSPECT INDIVIDUAL BIOMARKER OR TEST RESULTS. IN THIS CASE BETA-AMYLOID 42 IN CSF HAS BEEN SELECTED AND THE FIGURE SHOWS DISTRIBUTIONS OF THE BETA-AMYLOID 42 CSF BIOMARKER IN PREVIOUS PATIENTS DIAGNOSED WITH ALZHEIMER DISEASE (RED) AND FRONTOTEMPORAL DEMENTIA (BLUE). THE VALUE MEASURED FOR THE CURRENT PATIENT IS INDICATED BY THE YELLOW LINE.

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The use of cNeuro with the quantitative analysis of brain images provided by cMRI, and the clinical decision support given by cDSI, allows physicians to diagnose memory clinic patients with increased confidence. Increased confidence enables earlier diagnosis and fewer follow-up visits. This is crucial for early identifying patients where the symptoms are due to a reversible condition. Moreover, even today´s symptomatic drugs have shown to delay admittance to nursing homes, emphasizing the importance of early diagnosis. This leads to direct cost savings for the clinic, indirect cost savings to society, and improved quality of life for patients and their caregivers. Another important cost saving aspect concerns the use of expensive testing. CSF biomarkers provide important information for diagnosis

IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

and patient management but are invasive and expensive. Some clinics do not obtain CSFbiomarkers on any patients, while other obtain them for all patients. There are also clinics where CSF-biomarkers are obtained only for patients that are difficult to diagnose, but where “difficult” is based on a subjective decision. Clearly what is needed is an objective method to identify patients who benefit most from having CSF biomarkers. This is available in cDSI. cDSI allows clinicians to adopt a step-wise approach to dementia diagnostics, where MRI and neuropsychology are used first. cDSI will then indicate which patients that would benefit from having also CSF biomarkers. A recent study showed this cDSI stepwise approach enables decreasing the number of CSF measurements significantly without compromising diagnostic accuracy.

What is needed is an objective method to identify patients who benefit most from having CSF biomarkers. This is available in cDSI

Quantitative MRI

Assess atrophy pattern and amount of white matter hyperintensities and get a summary report at your ﬁngertips

www.cneuro.com

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

The Impact of Dementia on Health Services Tom Cropper, Editor Dementia rates are increasing, and health services are struggling to keep up with demand. How can they improve the lives of people living with this terrible condition?

“Dementia is the biggest health and care crisis of our time and these new statistics showing that it causes more than one in eight deaths should be a stark wake-up call for urgent action. A million people in the UK will have dementia by 2021 and we are simply not ready for that challenge” Jeremy Hughes, Chief Executive at the Alzheimer’s Society

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N MANY ways, healthcare in the developed world has become a victim of its own success. Hundreds of years of development and technological advances have continuously improved the standard of care people can expect. Today, conditions which might once have been fatal can often be resolved relatively easily. But this success comes at a price. People are living longer which means more of them are coping with long-term and complicated conditions such as dementia.

Dementia: The Rising Tide The number of people living with dementia is set to triple by 2050 according to the World Health Organisation3. It has become the leading killer in the UK with around 850,000 people in the country thought to be living with the condition4. According to analysis by actuary firm Munich Re, the main causes are an aging population in which the number of people in high risk categories is increasing, changes to medical coding which have seen more people classified as having dementia and increased diagnosis rates from medical practitioners5. The increase, they suggest, is largely because of the success of health services in prolonging life and dealing with the condition. Indeed, they go so far as to argue that the underlying rates of dementia may be declining. Even so, dementia is growing and will continue to present a serious challenge for health services. Statistics for England and Wales show that dementia is the leading cause of death in the UK. As Jeremy Hughes, Chief Executive at the Alzheimer’s Society said: “Dementia is the biggest health and care crisis of our time and these new statistics showing that it causes more than one in eight deaths should be a stark wake-up call for urgent action. A million people in the UK will have dementia by 2021 and we are simply not ready for that challenge. With the NHS and social care already struggling to meet rising demand, and families affected by dementia paying the price emotionally and

financially, I urge the Government to end this injustice and use the Budget to properly fund the system so that everyone can get the care they need.”6 This comes at a time when health services are struggling to cope with the capacity demands being placed upon them. Cuts to budgets have had an impact on the provision of care to older people. A report from the Kings Fund highlights the problem. Care provisions, they say, increasingly vary from region to region in the UK after six years of cuts to local authority budgets7. The situation in the UK is made more complicated by Brexit. It has already had an impact on the retention of staff from the EU. The Kings Fund states that there is currently a shortage of more than 100,000 staff (or one in 11 posts) but, potentially, this could rise to more than 250,000 by 20308. The issue has often been neglected, but social care has moved to the forefront of political debate in the past couple of years. In April 2019, Labour announced a £2.8bn social care package to improve services for people living with dementia. People would get extra help with daily tasks such as bathing and shopping. The measures would, they claimed, help around 50,000 people living with dementia who currently receive no social services support9.

Impact on Lives Where health services are struggling to cope, the wellbeing of people living with dementia suffers. Regardless of the type of dementia or to what degree its advance can be slowed or reversed, quality of life will vary depending on how much support is given, when the condition is identified and how it is managed. People with early or moderate stages, may continue to live relatively fulfilling and independent lives if they are given the right support. Alternatively, those who do not receive support can deteriorate rapidly. The impact goes beyond individual patients and extends into their wider social circles. Caring for people with dementia takes a toll on both physical and mental health.

IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Clinical Decision Support If nothing else, this represents a cost issue for health services. Those individuals who do not receive adequate care in the community may require more intensive and expensive care in a residential setting. An investigation by the Alzheimerâ€™s Society found that poor community care provisions had contributed to an increase in the number of people with dementia attending A&E10. These services are themselves stretched to breaking point. Equally, if carers and family members suffer mental health issues such as depression or anxiety, this can have a further drain on primary care services. A survey run by Mental Health and Money Advice found that 73% of the 400 care givers surveyed said they had developed mental health issues as a result of giving care to someone, with 36% of people acknowledging that it had affected both their mental and physical health. In addition, 84% of people said it had put a strain on their family relationships11.

The Drive to Improve Detection Improving the provision of care and support to people affected by dementia is therefore not just about improving their quality of life, but also balancing the books. Dementia is one of the biggest health and social care challenges health services face and it is likely to become even more serious in the future. The drain on resources is putting the entire health and social care system under pressure. So, how can this pressure be reduced? The key is to improve the early identification and management of dementia. Early identification may not always be able to resolve the condition, but it can dramatically improve the wellbeing of patients, allowing them to retain more independence for longer.

Regardless of the type of dementia or to what degree its advance can be slowed or reversed, quality of life will vary depending on how much support is given, when the condition is identified and how it is managed

Use all available data to get clinical decision support about etiology and risk for progression by comparing the patientâ€™s data to previous patients

www.cneuro.com

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Early Detection: Very Beneficial but Extremely Challenging Jo Roth, Staff Writer The earlier health services can detect neurological conditions such as dementia, the better the treatment they can provide. But conventional methods can prove extremely challenging.

Almost two thirds of the people surveyed said they feared the prospect of an Alzheimer’s diagnosis . Understandable it may be, but this fear leads many people to put off testing for the condition which means they may ignore tell-tale symptoms

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ARLY DETECTION of dementia can have a profound effect on the experience of patients and healthcare resources. It can enable forward planning, improve independence and postpone the point at which patients require full-time residential care. Unfortunately, it is very challenging. As health services seek to step up the fight against dementia, early identification will become incredibly important.

The Benefits of Early Detection The Alzheimer’s Disease International (ADI) World Alzheimer’s report details the key benefits of early diagnosis of Alzheimer’s. These, it says, include: • The ability to plan ahead: If people receive a diagnosis early, they have more time to plan ahead, assess the available options and make plans for their future care provisions. •A ccess to medication: A patient will need a diagnosis in order to receive pharmaceutical and non-pharmaceutical therapies which may slow the progression of the condition and improve their quality of life. • Participate in research: If signs of the condition are identified earlier, it may give people the chance to participate in research which can lead to further advances in the treatment of the condition12. An early diagnosis can also help people get the right support network around them. Conditions such as Alzheimer’s may have relatively few symptoms in the early stages, but people may still experience problems such as shortterm memory loss or disorientation. Having help and support at this stage can minimize the impact on people’s daily lives. Early identification, said the report, has been shown to deliver interventions early which can slow the condition down, improve cognitive function, increase independence and improve the mental wellbeing of both the patient and care giver.

The report looks at the ways in which health services around the world have sought to improve early detection. Efforts have tended to focus on increasing awareness within the general population, better training for professionals, the role of GPs and developing an effective pathway of care. Even so, one of the biggest hurdles is fear within the general population. There is a saying that what you can’t see can’t hurt you and, according to a study from the Alzheimer’s Society, many people in the UK appear to agree. Almost two thirds of the people surveyed said they feared the prospect of an Alzheimer’s diagnosis13. Understandable it may be, but this fear leads many people to put off testing for the condition which means they may ignore tell-tale symptoms. The longer they do so, the longer it will take them to get the help that they need.

Primary Care The front line of the battle against dementia will be the place where patients may first report some of the problems they are experiencing – their GPs. There are several tests which can be carried out to test cognitive ability either by the GP or a specialist memory clinic. These tests will not conclusively point to dementia, but they may highlight an issue with memory which could merit further investigation. Test scores may be influenced by a person’s own level of cognitive ability or education. For example, a person may score within an acceptable range, but still exhibit a deterioration from their own cognitive ability. Their relatively high score in these tests can hide dementia in the earliest stages. Likewise, someone with a lower level of education might score poorly, but that doesn’t mean the test would point to dementia. The subjective nature of the tests and the difficulty of producing a clear diagnosis

IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Quantitative MRI means GPs are often uncomfortable taking on responsibility for identifying or treating the condition. A scheme in the West Midlands sought to expand the role of GPs in the process by offering an incentive of £150 per patient diagnosed and treated. It is an attempt to relieve the pressure on specialist memory assessment clinics which, according to a study by the Government, were being overwhelmed with demand14. Even with incentives such as these, diagnosis is challenging. Symptoms may relate to other reversible conditions such as depression or anxiety. Indeed, dementia is often misinterpreted as anxiety in the early stages, which means many patients will be given inappropriate treatment.

Using Biomarkers A number of biomarkers can be used to identify dementia years before symptoms such as memory loss set in. These can include: •B lood tests: These search for proteins which originate in the brain. These are currently normally used by researchers. •G enetic testing: Problems with DNA can indicate a heightened risk of Alzheimer’s. •L umbar puncture: Also known as a ‘spinal tap’, fluid is extracted from the spinal cord via a syringe. They test for beta-amyloid 42, tau, and phospho-tau. In patients with Alzheimer’s, beta amyloid 42 levels are low while tau levels are high. Imaging techniques such as MRI and CT scans can also be used to detect changes in the makeup of the brain such as size, structure, chemistry and activity. A shrinkage in the brain, for example, may be an indication of Alzheimer’s. The difficulty is that these tests can produce a huge amount of information – more than individual clinicians can ever work through manually. Analysis may also depend on the skill

Imaging techniques such as MRI and CT scans can also be used to detect changes in the make-up of the brain such as size, structure, chemistry and activity. A shrinkage in the brain, for example, may be an indication of Alzheimer’s

and opinion of the individual clinician, which can lead to subjectivity in assessments.

Assess atrophy pattern and amount of white matter hyperintensities and get a summary report at your ﬁngertips

www.cneuro.com

Turning to Artificial Intelligence Early diagnosis, then, can be extremely important in overall treatment. It can help patients make plans, and it can empower them to live a more independent life for longer. Quality of life improves and expensive interventions may be avoided or postponed. The biggest challenge is that the entire process can be difficult. Symptoms may relate to many other conditions. To add certainty into the process, many clinicians are turning to next generation technologies such as artificial intelligence. This is a relatively new, but rapidly evolving area as machine learning enables systems to analyze much more data in a shorter space of time than human operators. This can lead to more certainty in diagnosis and help identify conditions earlier.

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Artificial Intelligence: Can it Transform Healthcare? James Butler, Staff Writer Artificial intelligence (AI) is being touted across the healthcare system. But exactly how effective can this technology be?

AI a wide umbrella encompassing everything from relatively simple systems which save time and money in work processes to highly advanced and futuristic systems which could transform the horizons of what the health service can achieve

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F YOU scour business titles, the chances are you’ll come across a number of articles which all start the same way: “Artificial intelligence can transform the industry.” Healthcare is no different. A quick search on the internet will reveal a library’s worth of articles describing the benefits of exciting technologies such as AI and machine learning in the healthcare sector. However, we’ve had hype like this for innovations before. Just look at Google’s much postponed contact lenses for detecting diabetes15. Generally speaking, when an innovation appears which promises the world in healthcare, we should resist the temptation to get too excited. So, is this the case with AI?

The Potential of AI The answer is a bit of both. AI’s potential is vast, yet many of the most exciting developments are stuck some way off in the future. In its basic form, AI refers to complex algorithms which automate many tasks that would otherwise be performed by humans. Using machine learning it can analyze vast amounts of data to reduce administration time and identify trends which would otherwise go unnoticed. AI a wide umbrella encompassing everything from relatively simple systems which save time and money in work processes to highly advanced and futuristic systems which could transform the horizons of what the health service can achieve. For example, Deep Mind, one of the world leaders in AI research, has partnered with Moorfield’s Eye Hospital in London to deliver a system which identifies early signs of eye disease16. Using data from more than 15,000 patients, the algorithm uses optical coherence tomography data to spot signs of eye disease. Experts would require a considerable amount of time to analyze this data and deliver an accurate diagnosis. AI promises to do this much more quickly, saving time, effort and money. However, this is still in its early stages. The developers hope it will soon reach the point where doctors can use it for those patients who are in urgent need of treatment.

Future Promise Casting an eye further into the future, nanotechnology and AI have enormous potential to transform healthcare. One innovative approach comes from a firm called Nanovision which has developed a nanotech solution to combat the rise of antibiotic resistant superbugs. These, according to a report, have the potential to kill 10 million people a year by 202517. Nanovision is developing a chip which can mint its own cryptocurrency to assign real value to molecular data. This could lead to a decentralized economic system which creates a global molecular dataset to aid medical discovery. Anyone would be able to use the chip which could generate data from individuals to citizen scientists, hospitals and researchers. This, they hope, would deliver real-time and realworld datasets which can assist in developing solutions against superbugs18. It combines some of the most exciting health centered technologies including AI, nanotechnology and the blockchain to address the most pressing questions facing global healthcare. All of these solutions require radical and bold approaches to technological development which is why so much is being invested into the future of AI. A report from Global Markets Insight shows the AI sector achieving annual growth rates of approximately 40%. By 2025, the global market could reach $13bn, driven by adoption across the sector19.

Early Developments AI, then, can achieve great things in the future, but much of this technology is still in the developmental stage. The reality of today’s AI is somewhat more mundane. These may use machine learning and robotic intelligence but are still some way short of true AI as we would like to imagine it – namely, AI which can truly imitate human intelligence by questioning and learning as it goes. This is where AI currently meets its limitations. It can evolve and develop, but it never asks the question ‘why’ which makes it difficult to create a true logic path for its decision making.

IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Clinical Decision Support This could make a system unreliable and reduce trust among patients and doctors alike. AI, then, is a developing technology which has almost limitless potential. Whether that will be fully realized remains to be seen, but right now AI is already delivering major benefits. It is being used in a range of settings including: • Mobile coaching apps which can use AI algorithms to monitor health and provide medical advice to help people manage their own conditions. • Drug discovery: Algorithms can analyze datasets much more quickly than humans, aiding in the development of new and sophisticated treatments. • Personalized medical plans: Analysis of large quantities of personal medical datasets can greatly improve the quality of care. • Cyber security: The health sector is one of the main targets of cyber criminals. AI algorithms are being developed to counter the rapidly evolving threat.

AI in Dementia Care In the realm of dementia care, artificial intelligence is being used to improve infection control and, crucially, to improve early detection. Research from the Alzheimer’s Society suggests that AI technology, such as smartphone apps, could potentially identify signs of dementia years earlier20. Meanwhile, Manor Hospital in Coventry is using a system to monitor patients with conditions such as dementia. An infrared sensor detects microblushes on the skin to constantly monitor vital signs and issue alerts if it detects a problem. For example, if someone moves out of bed, the system will detect it and start an alert. “We’ve never had this ability as a species to constantly get heart rate, breathing rate, activity

and routine data,” Says Hugh Lloyd Dukes CEO of Oxyhealth. “There’s no reason, as we combine the data using artificial intelligence, that we can’t detect the onset of data or even it getting worse. We can see a future where we detect problems earlier so you can stay in your own home, or in the setting you’re most comfortable in, without coming into hospital, and that will save a huge amount of time21.” Elsewhere, AI is being used to detect infections among patients living with dementia. Researchers at Surrey University have developed a system which can reduce the occurrence of urinary tract infections. It uses a machine learning system to detect signs of a possible urinary tract infection based on data which already exists. The study was part of the Second Stage of the Technology Integrated Health Management for dementia project led by Surrey and Borders Partnership NHS Foundation Trust. It was awarded an extra £1million from NHS England and the Offices for Life Sciences last year after an initial trial showed a significant reduction in neuropsychiatric symptoms among the patients studied22. These are just some of the projects using artificial intelligence which aim to improve the prospects for people living with dementia. They help to avoid complications, improve independence and make it easier for staff to identify problems as and when they occur. Obstacles remain to more widespread adoption, but these are being overcome. The key will be the use of data. As health services start to gather more data and get better at using it, they are opening the door to more sophisticated AI systems. This is a rapidly developing space and, while some of the hype may be exaggerated, the potential is very real indeed.

Use all available data to get clinical decision support about etiology and risk for progression by comparing the patient’s data to previous patients

www.cneuro.com

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

How Big Data Can Revolutionize Dementia Care Tom Cropper, Editor Health services are processing vast quantities of data which can improve healthcare in many different ways. But capturing it is a challenge.

Decision making can also be hit and miss. Clinicians will often examine the data in front of them visually and make their diagnosis. The trouble is there is a lot of information for them to process.

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OT SO long ago, talk of ‘gigabytes’ in computing circles was the stuff of fantasies. These days we’re thinking in the realm of zettabytes as society begins to generate data on a scale never before imagined. The is the world of big data and it promises to revolutionize dementia care.

The Age of Big Data The big data revolution has consumed us with frightening speed. The next few years could see the amount of data we produce multiplying ten-fold with data levels reaching 163 Zettabytes by 202523. Researchers have predicted healthcare data to rise at an annualized rate of 36% through to 2025. This is faster than both the manufacturing and finance sectors which have both been among the heaviest users of big data24. New technologies such as mobile healthcare apps, remote monitoring of patients, connected devices and the sharing of medical records contribute to an explosion in the amount of data available to healthcare providers. This can have enormous benefits in dementia care as can be found in a review of the available literature on big data in healthcare. A review by Frontiers in Medicine states: “Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories25.” However, despite this promising outlook, many of these benefits have not yet been realized due to the difficulty of data management. The problem is partly one of attitude and partly one of infrastructure and technology. Adoption of technology is sporadic across the health sector. Despite attempts to increase dramatically the adoption of digital technology, progress is still

slower than hoped. Some bodies have more effective IT systems and data management capacities than others. Equally, the sector also suffers from legacy issues and the segmentation of different departments into silos, which makes it difficult to share data to the extent that is required. Professionals can also be reluctant to embrace technology for a number of reasons. In some cases, services meet with resistance from staff on the front line who have become accustomed to working in certain ways. If they feel the existing system has been working, they may be suspicious of change. The rise of big data also creates understandable concerns regarding security and privacy. This problem is not unique to healthcare. All sectors are weighing the benefits a world of data mobility can bring against the risks. Cybercrime is booming. It has become a multi-billion-dollar industry with attacks evolving rapidly. Criminals look for two big characteristics in potential targets: organizations which handle large quantities of sensitive data and those which might be slow to raise adequate defenses. For both these reasons, healthcare providers are tempting targets and there have already been a number of high-profile breaches such as the Wannacry virus in 2018 which temporarily locked the NHS out of its own systems costing it an estimated £92million26. The stakes are made much higher thanks to the introduction of GDPR which places enhanced requirements on organizations to safeguard any personal data they provide. This even applies when using a third party organization. A key issue for healthcare providers in the future will be how they ensure that both they, and any third-party providers, can manage all the data passing through their systems securely. To do so they will need secure end-to-end encryption for all devices, systems and platforms. If something does go wrong, they will also need redundancy measures in place to ensure operations can continue and that they comply with all their regulatory obligations.

IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

Scaling the Data Mountain

Objective Analysis

Capacity is also a problem. All that data is only as useful as an organization’s ability to extract insights from it. The process of analyzing information can be laborious and will always be prone to human error, which is where artificial intelligence comes into its own. The rise of big data makes it possible to power AI and machine learning technologies. These require vast quantities of data in order to make fast, accurate and reliable assessments. In turn, they make it possible to harness that data and draw useful insights from it. Decision making can also be hit and miss. Clinicians will often examine the data in front of them visually and make their diagnosis. The trouble is there is a lot of information for them to process. Some signs may be subtle and difficult to identify. In dementia care, this can often make diagnosis of neurological conditions challenging. Many clinicians utilize visual assessment methods such as MTA, GCA and Fazekas, but these can be imperfect. Human visual assessment is subjective and depends on the perception of individual clinicians. Computed MTA, GCA and Fazekas scales are more objective. Combining different assessment methods can increase the reliability of the diagnosis. A study published in European Radiology looked at the benefits of using a form of brain imaging in conjunction with visual rating scales. The study assessed 135 patients and found that the diagnosis changed after neuroimaging in 23.7% of cases. The confidence level of geriatricians also increased significantly when imaging was added to the process27.

New AI tools can address some of these challenges. Last year, CNeuro cMRI was cleared for use by the FDA in the USA28. This image quantitative software analyses MRI T1 and FLAIR images and enables clinicians to gain objective measurements of atrophy in different brain regions as well as information about white matter hyperintensities. It combines imaging analysis with computed MTA, GCA and Fazekas scales and can highlight potential causes for concern. “AI is a hot topic promising major changes in medicine. Fully automatic image quantification, as provided by cNeuro cMRI, is part of this change,” commented Dr Jyrki Lötjönen, Chief Scientific Officer at Combinostics. “Today, interpretation of radiology images is very much based on visual assessment alone. However, identifying subtle changes is difficult and tools like cNeuro cMRI help radiologists create high quality reports in a shorter time.” Meanwhile, cDSI, also from Combinostics draws on stored clinical data to compare it with patients’ data. It provides a clear and intuitive visualization of where similarities occur and can be used as a decision support tool for clinicians when making diagnosis. It is cloud-based and can be used very simply via a web browser, meaning it requires no hardware and is compatible with existing systems. Both systems serve as additional pairs of eyes, picking up details which might otherwise be missed, providing objective analytics and enabling clinicians to draw insights from all the data they have available to them. This show what can be achieved when artificial intelligence is combined with the power of big data. There is much more to come from artificial intelligence but, while some can be tempted to dismiss it as the stuff of science fiction, it is already transforming healthcare in many ways. It is supporting decision making, reducing costs and, most importantly of all, enabling people living with dementia to enjoy a much better quality of life.

Quantitative MRI

Assess atrophy pattern and amount of white matter hyperintensities and get a summary report at your ﬁngertips

www.cneuro.com

Combinostics Oy

www.combinostics.com

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IMPROVING THE EARLY DIAGNOSIS OF DEMENTIA AND THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SOLUTIONS

References: Dementia Statistics: https://www.alz.co.uk/research/statistics

1 2

One in Three People Born in 2015 Will Develop Dementia: https://www.alzheimersresearchuk.org/one-in-three-2015-develop-dementia/

Dementia Cases Set to Triple: https://www.who.int/mediacentre/news/releases/2012/dementia_20120411/en/

Dementia, Facts for the Media: https://www.alzheimers.org.uk/about-us/news-and-media/facts-media

Is Dementia Really on the Rise: https://www.theactuary.com/features/2017/03/is-dementia-really-on-the-rise/

Rise in Deaths Due to Dementia: https://www.alzheimers.org.uk/news/2018-10-23/rise-deaths-due-dementia

Older People are Paying the Price for Social Care Cuts: https://www.kingsfund.org.uk/press/press-releases/older-people-social-care-cuts

Staffing Shortfall of more than 250,000 by 2030: https://www.kingsfund.org.uk/press/press-releases/staffing-shortfall-major-risk-nhs-long-term-plan

Labour Unveils Plan for £2.8bn Social Care Fund: https://www.politicshome.com/news/uk/health-and-care/social-care/news/103446/labour-unveils-plan-%C2%A328bn-social-care-fund-help 9

Rising Admissions to A&E for People with Dementia: https://www.alzheimers.org.uk/news/2018-07-20/rising-admissions-ae-people-dementia-due-threadbare-social-care-system 10

Being a Carer Can Affect Your Mental Health and Money: https://www.mentalhealthandmoneyadvice.org/en/top-tips/being-a-carer-can-affect-your-mental-health-and-money/ 11

National Dementia Plans: https://www.alz.co.uk/sites/default/files/pdfs/national-dementia-plans-early-detection-and-diagnosis.pdf

Over Half of People Fear Dementia Diagnosis: https://www.alzheimers.org.uk/news/2018-05-29/over-half-people-fear-dementia-diagnosis-62-cent-think-it-means-life-over 13

GPs to be Paid £150 per Diagnosis: http://www.pulsetoday.co.uk/clinical/clinical-specialties/neurology/gps-to-be-paid-150-to-diagnose-and-treat-dementia/20033565.article 14

What Happened to Plans for Smart Contact Lenses? https://labiotech.eu/features/contact-lens-glucose-diabetes/

Case Study: Moorfields Eye Hospital: https://deepmind.com/applied/deepmind-health/working-partners/health-research-tomorrow/moorfields-eye-hospital-nhs-foundation-trust/ 16

Superbugs Could Kill 10 Million a Year: https://www.iflscience.com/health-and-medicine/superbugs-could-kill-10-million-each-year-2050-overtaking-cancer/ 17

AI and Nanotechnology are Revolutionising Healthcare: https://e27.co/ai-nanotechnology-innovating-healthcare-20180115/

Global Healthcare AI Market to Hit $13bn by 2025: https://www.prnewswire.com/news-releases/healthcare-artificial-intelligence-market-to-hit-13-billion-by-2025-global-market-insights-inc-300797066.html

AI Improves the Ability of Brain Imaging to Detect Alzheimer’s Years Earlier: https://www.alzheimers.org.uk/news/2018-11-06/artificial-intelligence-predicts-alzheimers-years-diagnosis 20

Innovative Ways to Ease the Workload of Doctors and Nurses: https://www.oxehealth.com/news/bbc-click-innovative-ways-to-ease-the-workload-of-nurses-and-doctors 21

Artificial Intelligence Spots Infections Among Dementia Patients: https://www.digitalhealth.net/2019/01/artificial-intelligence-infection-dementia-surrey/

What Will We Do When Data Hits 163 Zettabytes? https://www.forbes.com/sites/andrewcave/2017/04/13/what-will-we-do-when-the-worlds-data-hits-163-zettabytes-in-2025/ 23

Big Data to See Explosive Growth Challenging Healthcare Organisations: https://healthitanalytics.com/news/big-data-to-see-explosive-growth-challenging-healthcare-organizations 24

Frontiers, Big Data and Dementia. Charting the Route Ahead for Research, Ethics and Policy: https://www.frontiersin.org/articles/10.3389/fmed.2018.00013/full 25

Wannacry Cyber Attack Cost the NHS £92 Million: https://www.telegraph.co.uk/technology/2018/10/11/wannacry-cyber-attack-cost-nhs-92m-19000-appointments-cancelled/ 26

The Impact of MRI Combined with Visual Rating Scales: https://www.researchgate.net/publication/281363738_The_impact_of_MRI_combined_with_visual_rating_scales_on_the_clinical_diagnosis_of_dementia_a_prospective_study FDA Clears Combinostics’ MRI Quantification Software: https://www.cneuro.com/2018/01/15/fda-clears-combinostics-mri-quantification-software-cneuro-cmri/

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