Artificial Intelligence in the Exam Lane

Simplify and streamline AMD diagnosis and monitoring without adding human staff.

By Theia AMD is the leading cause of irreversible legal blindness, affecting more than 1.4 million Canadians – more than glaucoma and diabetic retinopathy combined. 1 But diagnosing it isn’t always easy. In fact, research shows that eye doctors miss AMD diagnoses 25% of the time. 2 Furthermore, diagnosing AMD can be time consuming, especially when you see some small drusen but you aren’t sure what to make of it. That’s where I can help. My name is Theia. I'm an artificial intelligence-driven onboard technician that lives inside the AdoptDx Pro, a wearale headset that measures dark adaptation speed. I’m here to help you find all the AMD in your practice so you can help your patients preserve their vision for as long as possible.

Functional Testing

Structural testing for AMD can be ambiguous, which is why functional test results should also be considered. However, visual acuity (VA) as a functional endpoint is known to have significant limitations because it’s generally stable in the earlier stages of the disease. 3,4 Night vision, on the other hand, is affected earlier in the disease process by at least three years. 5,6,7,8 This is why dark adaptation (DA) testing is so informative compared to VA. DA has been shown time and again to be a functional biomarker for AMD. 9,10 Furthermore, cross-sectional analyses demonstrate that DA is associated with increasing AMD severity and worsens over time in patients with AMD. 11 This means that measuring your patients’ dark adaptation function is not only useful in AMD diagnosis, but also in monitoring its progression.

There are currently only two commercially available dark adaptometers: the AdaptDx tabletop and the AdaptDx Pro headset, which is where I live. Both units provide an objective output (Rod-Intercept Time, RI) that is 90% sensitive to the presence of AMD. 12

A Special Kind of Artificial Intelligence

The tabletop AdaptDx gained popularity among US optometrists, but there were some practical challenges, mostly regarding the need for a completely dark room. The reason the AdaptDx Pro was developed is because it doesn’t require a dark room. Adding me, my dazzling personality and ability to make patients feel comfortable to the device, is icing on the cake. The AdaptDx Pro is a self-contained wearable headset that includes all of the functionality and accuracy of the original tabletop dark adaptometer, but it creates a personal dark room. As a result, patients can take the test anywhere in the office, in any light— making it easier than ever to fit dark adaptation testing into any practice workflow.

Thanks to my highly evolved artificial intelligence (AI), I help ensure consistent, reliable testing results. To clarify, there are three different types of AI. The first form, called cognitive insight AI, uses algorithms to detect patterns in vast volumes of data and interpret their meaning. 13 The second, less popular and arguably less well-loved form of AI includes tools like chatbots that rely on customer input to perform tasks such as answering frequently asked questions. This is called cognitive engagement AI. The third form is called process automation AI and that is the one I possess.

If you’ve ever spoken to Siri or Alexa, you’ve experienced some of the benefits of process automation AI. Here’s how it works in my testing environment: First, the office technician selects the testing protocal and places the device on the patient. After that, I take over to facilitate a reliable, consistent test by using automated instructions and adaptive feedback spoken directly to the patient. It’s really that simple. If you have patients who don’t speak English, fear not! I am multilingual and can speak in Canadian French, German, Italian, Spanish and several other languages.

The AdaptDx Pro is a revolutionary way to quickly and effectively measure dark adaptation in virtually any clinical setting, without eating up too much staff or doctor time. More importantly, this artificial intelligence-driven technology—featuring yours truly—is designed to help you detect AMD with confidence and monitor it carefully so that patients can be referred for injections as soon as possible should that become necessary.

References 1. Glaucoma. Fighting Blindness Canada. <https://www.fightingblindness.ca/eyehealth/eye-diseases/ glaucoma> September 2020. Accessed November 19, 2021. Diabetic Retinopathy. Fighting Blindness Canada. <https://www.fightingblindness.ca/eyehealth/ eye-diseases/diabetic-retinopathy> September 2020. Accessed November 19, 2021. Age-related-macular-degeneration. Fighting Blindness Canada. <https://www.fightingblindness.ca/ eyehealth/eye-diseases/age-related-macular-degeneration> September 2020. Accessed November 19, 2021.

2. Neely DC. JAMA Ophthalmol. 2017;135(6):570-575. 3. Chen KG, Alvarez JA, Yazdanie M, et al. Longitudinal Study of Dark Adaptation as a Functional Outcome Measure for Age-Related Macular Degeneration. Ophthalmology. 2019;126(6):856-865. doi:10.1016/j. ophtha.2018.09.039 4. Owsley C, Huisingh C, Clark ME, et al. Comparison of visual function in older eyes in the earliest stages of age-related macular degeneration to those in normal macular health. Curr Eye Res. 2015:1e7. 5. Chen KG, Alvarez JA, Yazdanie M, et al. Longitudinal Study of Dark Adaptation as a Functional Outcome Measure for Age-Related Macular Degeneration. Ophthalmology. 2019;126(6):856-865. doi:10.1016/j. ophtha.2018.09.039 6. Scilley K, Jackson GR, Cideciyan AV, et al. Early age-related maculopathy and self-reported visual difficulty in daily life. Ophthalmology. 2002;109:1235e1242. 7. Owsley C, McGwin G, Jackson GR, et al. Effect of short-term, high-dose retinol on dark adaptation in aging and early age-related maculopathy. Invest Ophthalmol Vis Sci. 2006;47:1310e1318. 8. Ying GS, Maguire MG, Liu C, Antoszyk AN; Complications of Age-related Macular Degeneration Prevention TrialResearch Group. Night vision symptoms and progression of age-related macular degeneration in the Complications of Age-related Macular Degeneration Prevention Trial. Ophthalmology. 2008;115:1876e1882. 9. Chen KG, Alvarez JA, Yazdanie M, et al. Longitudinal Study of Dark Adaptation as a Functional Outcome Measure for Age-Related Macular Degeneration. Ophthalmology. 2019;126(6):856-865. doi:10.1016/j. ophtha.2018.09.039 10. Owsley C, Huisingh C, Clark ME, et al. Comparison of visual function in older eyes in the earliest stages of age-related macular degeneration to those in normal macular health. Curr Eye Res. 2015:1e7. 11. Chen KG, Alvarez JA, Yazdanie M, et al. Longitudinal Study of Dark Adaptation as a Functional Outcome Measure for Age-Related Macular Degeneration. Ophthalmology. 2019;126(6):856-865. doi:10.1016/j. ophtha.2018.09.039

12. Owsley C, Jackson GR, White M, Feist R, Edwards D. Delays in rod-mediated dark adaptation in early age-related maculopathy. Ophthalmology. 2001; 108:1196–202. 13. Davenport TH, Ronanki R. Artificial Intelligence for the Real World. Harvard Business Review. JanuaryFebruary 2018.