EMERGENCY ULTRASOUND SECTION
Artificial Intelligence and POCUS: The Pros and Cons Shawn Sethi, DO FAAEM and Rebecca Theophanous, MD MHSc FAAEM*
I
ntroduction
Artificial intelligence (AI) involves developing computer systems to perform tasks that normally require human intelligence, including visual perception, image or speech recognition, decision-making, and language translation.1,2 AI is emerging as a current hot topic in society, from applications such as facial recognition to self-driving vehicles.3,4 These models can automate repetitive tasks and make processes more efficient. In medicine, AI is being incorporated into various ultrasound imaging applications, such as detection of B lines in lung ultrasound, measurements of the inferior vena cava, or estimation of bladder volume.5-7 Machine learning (ML) is a subcategory of AI that passes presented data through algorithms to adapt and learn.2 There are two main forms of machine learning, supervised learning and unsupervised learning. In supervised learning, scientists provide data inputs along with a set of labels and the ML algorithm determines which inputs match which labels. Unsupervised learning involves a set of unlabeled inputs.7 ML uses small data sets, is moderately accurate, and requires low processing power.8 Deep learning (DL) is a subset of machine learning. Unlike ML, it requires significant processing power to solve problems simultaneously, is highly accurate, and is useful in large data sets such as medical imaging.8 Like the complex neuron network of the human brain, DL uses large multi-layer neural networks to solve problems.2
POCUS interpretation accuracy, make complex measurements, and assist in reducing time to completing examinations.6 We aim to highlight the pros and cons of AI in POCUS. Pros
Artificial intelligence has potential to transform the future of POCUS. By using both novel machine learning and deep learning models, we can train large data sets with still images and video clips to assist sonographers in making rapid and accurate diagnoses. AI has demonstrated accuracy in various POCUS applications. For example, a study of 56 subjects with COVID showed excellent agreement when comparing CT scan to an AI-based automated pneumonia detection method.5 In another study, automatic left ventricular ejection fraction, left ventricular outflow tract velocity time integral, and IVC collapsibility were assessed. The authors found good agreement between the automated tool measurements and the POCUS expert calculations.6 Similarly, Fiedler et al used an AI model to detect lung sliding and found high sensitivity for identifying pneumothorax when compared to expert consensus.9 Literature continues to mount with positive results in other POCUS applications such as measuring bladder volume, fetal heart rate detection, and in pre-hospital use. Although data is still preliminary, the use of AI POCUS models are broad in scope and demonstrate promising accuracy when compared to gold standards.5-7 These models can aid the sonographer in bridging the gap between novice and expert users.
Machines can make diagnostic errors as AI is a relatively new technology.”
Incorporating artificial intelligence with point-ofcare ultrasound use
Although AI is a newer technology, studies show that it can recognize images with excellent accuracy. Point-of-care ultrasound (POCUS) is a useful bedside tool for expedited medical diagnosis, yet it is operator dependent, and training POCUS experts requires time and resources.6 There is hope that AI tools built into ultrasound machines can improve
As POCUS expands both within our specialty and beyond, there will be a growing need for education across a wide variety of settings including academic centers, community hospitals, medical schools, and low-resource settings.8 Machine learning tools such as automated labeling, real-time scanning guidance, and grading of image quality have the potential to revolutionize how we teach POCUS, especially in settings with low teacher to learner ratios.
COMMON SENSE MAY/JUNE 2024
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