Foundational Considerations for Artificial Intelligence Using Ophthalmic Images

Grenon, S. Marlene; Manrai, Arjun K.; Ko, Justin; Chew, Emily Y.; Myung, David; Schuman, Joel S; Shields, Carol L.; Abràmoff, Michael D.; Eydelman, Malvina B.; Cunningham, Brad; Patel, Bakul; Goldman, Karen A.; Char, Danton; Sakamoto, Taiji; Blodi, Bárbara A.; Wolf, Risa M.; Gassee, Jean--Louis; Leng, Theodore; Roman, D. R.; Satel, Sally L.; Fong, Donald S.; Rhew, David C.; Wei, Henry; Willingham, Michael; Chiang, Michael F.; Blumenkranz, Mark S.

doi:10.1016/j.ophtha.2021.08.023

Cited by 45 publications

(59 citation statements)

References 62 publications

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“…Recent diagnostic test accuracy meta-analyses have provided very promising accuracy estimates for machine-learning-based teleretinal screening programmes for DR. 38 39 Future studies should assess the diagnostic accuracy of automated systems using artificial intelligence and deep-learning algorithms in teleophthalmology screening programmes for ocular diseases. 40 Lastly, the focus of this review was on teleretinal screening for the most common retinal pathologies. As more data become available, future investigations should assess the utility of teleglaucoma screening programmes.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of teleretinal screening for detection of diabetic retinopathy and age-related macular degeneration: a systematic review and meta-analysis

et al. 2022

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ObjectiveTo evaluate the diagnostic accuracy of teleretinal screening compared with face-to-face examination for detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD).Methods and analysisThis study adhered to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA). A comprehensive search of OVID MEDLINE, EMBASE and Cochrane CENTRAL was performed from January 2010 to July 2021. QUADAS-2 tool was used to assess methodological quality and applicability of the studies. A bivariate random effects model was used to perform the meta-analysis. Referrable DR was defined as any disease severity equal to or worse than moderate non-proliferative DR or diabetic macular oedema (DMO).Results28 articles were included. Teleretinal screening achieved a sensitivity of 0.91 (95% CI: 0.82 to 0.96) and specificity of 0.88 (0.74 to 0.95) for any DR (13 studies, n=7207, Grading of Recommendations, Assessment, Development and Evaluation (GRADE) low). Accuracy for referrable DR (10 studies, n=6373, GRADE moderate) was lower with a sensitivity of 0.88 (0.81 to 0.93) and specificity of 0.86 (0.79 to 0.90). After exclusion of ungradable images, the specificity for referrable DR increased to 0.95 (0.90 to 0.98), while the sensitivity remained nearly unchanged at 0.85 (0.76 to 0.91). Teleretinal screening achieved a sensitivity of 0.71 (0.49 to 0.86) and specificity of 0.88 (0.85 to 0.90) for detection of AMD (three studies, n=697, GRADE low).ConclusionTeleretinal screening is highly accurate for detecting any DR and DR warranting referral. Data for AMD screening is promising but warrants further investigation.PROSPERO registration numberCRD42020191994.

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Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of teleretinal screening for detection of diabetic retinopathy and age-related macular degeneration: a systematic review and meta-analysis

et al. 2022

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“…With the collective goal of improving patient care and fostering equity as mentioned above, and with the potential of AI to substantially transform the management of patients, the Collaborative Community on Ophthalmic Imaging (CCOI) was founded in 2019. Experienced experts of the CCOI operate under the values of teamwork, transparency, innovation, and efficiency in a patient-centered approach, strive to resolve any potential issues in eye imaging, and establish the best strategies for the practical use of software in ophthalmology in a way that respects the basic principles of bioethics (325)(326)(327)(328).…”

Section: Artificial Intelligence and Integrated Machine Learningmentioning

confidence: 99%

“…Models can also substantially assist physicians in establishing diagnosis of AMD. This can sometimes be challenging, as AMD findings can potentially go unnoticed or appear similar to other retinal conditions (polypoidal choroidal vasculopathy, macular dystrophies, CSR, and others) (328,384). For this purpose, a variety of algorithms has been offered, focusing on different imaging modalities (fundus pictures, OCT and OCT-A, FA) to detect multiple pathological findings (drusen and pseudodrusen, intra-and subretinal fluid, GA); similar to DR, structural and vascular biomarkers are also utilized for AMD (387)(388)(389)(390)(391)(392) (394).…”

Section: Artificial Intelligence and Age-related Macular Degenerationmentioning

confidence: 99%

“…Up to 25% of patients with AMD can remain undiagnosed by primary care providers, and the use of AI would not require an advanced skillset to operate or retinal expertise ( 328 , 382 ). An ideal method of AMD screening should be able to efficiently detect AMD and distinguish it from other similar diseases, be cost-effective, and autonomous.…”

Section: Artificial Intelligence and Integrated Machine Learningmentioning

confidence: 99%

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The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

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“…During the development, validation, and implementation of IDx-DR (Digital Diagnostics Inc), we started with an ethical framework built on the principles of non-maleficence, autonomy, and justice, which continue to be developed in various publications (2)(3)(4). This framework made it possible to track metrics around safety, equity, efficiency, transparency, validability, and accountability, allowing AI to be done the right way.…”

Section: Ethics In Healthcare Aimentioning

confidence: 99%

Commentary: Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

2021

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Foundational Considerations for Artificial Intelligence Using Ophthalmic Images

Cited by 45 publications

References 62 publications

Diagnostic accuracy of teleretinal screening for detection of diabetic retinopathy and age-related macular degeneration: a systematic review and meta-analysis

Diagnostic accuracy of teleretinal screening for detection of diabetic retinopathy and age-related macular degeneration: a systematic review and meta-analysis

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

Commentary: Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

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