Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges

Lee, Eric B.; Wang, Sophia Y.; Chang, Robert

doi:10.1097/apo.0000000000000395

Cited by 16 publications

(24 citation statements)

References 64 publications

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“…For example, establishing a database that can integrate data from different sources and address fragmented data could be useful for further diabetic research ( 45 ). Second, the research scope is required to be comprised of more common disease types, such as glaucoma and age-related macular degeneration ( 46 , 47 ). Third, a unified output standard for DR disease diagnosis is required to guarantee the versatility of various systems.…”

Section: Discussionmentioning

confidence: 99%

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Wang

Zuo

et al. 2022

Front. Endocrinol.

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BackgroundArtificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI’s general applications and research frontiers in the detection and gradation of DR.MethodsCitation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software.ResultsOverall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled “automated detection” was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning.ConclusionInitial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.

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Section: Discussionmentioning

confidence: 99%

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Wang

Zuo

et al. 2022

Front. Endocrinol.

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“…Our OCT-based approach, or similar other methodologies, bridge the gap between the present day and the future arrival of artificial intelligence-assisted decision making. [12][13][14] In summary, the use of the term "glaucoma suspect" creates a difficult predicament for both the patient and health care systems and is counterproductive. Patients suffer from the inconvenience, psychological impact and financial implications of a glaucoma suspect diagnosis and health care systems need to cope with provider, technology and cost constraints.…”

Section: How Can This Be Accomplished?mentioning

confidence: 99%

The Glaucoma Suspect Problem: Ways Forward

Leshno

Liebmann

2022

Asia-Pacific Journal of Ophthalmology

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The diagnosis of glaucoma depends upon indentification of characteristic damage to the optic nerve and retinal fiber layer. In many cases, however, clinicians find it difficult to ascertain whether glaucomatous damage is present or absent. These patients are often labeled as "glaucoma suspects," which creates a subpopulation of individuals without clear-cut disease who nonetheless must remain under surveillance. Most will never go on to develop glaucoma, yet the need for ongoing monitoring burdens clinics and health care systems. In this perspective, we illustrate possible directions and novel approaches that can be used to remedy this situation by integrating current technologies into clinical practice. In particular, we suggest that optical coherence tomography be better utilized to methodologically classify these eyes into glaucomatous and healthy categories.

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“…2 The explosion in health care AI research, specifically machine learning (ML) and deep learning (DL) have definite clinical relevance in ophthalmology. [3][4] In 2018, IDx-DR received approval from the US Food and Drug Administration (FDA) for its autonomous AI for diabetic retinopathy (DR) screening. EyeArt's approval followed shortly.…”

mentioning

confidence: 99%

Promoting Transparency and Standardization in Ophthalmologic Artificial Intelligence: A Call for Artificial Intelligence Model Card

Chen

Modi

Al-Aswad

2022

Asia-Pacific Journal of Ophthalmology

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O ver the last decade, artificial intelligence (AI) has become ubiquitous; it is in our smartphones and homes (Siri and Alexa), our emails (spam filters), Netflix recommendations, and ride-share apps. On a societal level, it is used for predictive policing and credit lending; and in medicine, it promises to transform the shape and scope of health care.But for its seemingly endless benefits, AI comes with ethical challenges and potential harm. 1 Most AI algorithms are supervised, meaning training inputs and outputs are defined by humans. This makes it susceptible to the same socioeconomic, racial, and gender biases that shape our world. Unintended bias can occur at any level: training, algorithm design, and implementation. There are many examples of how AI software, from facial recognition to natural language processing, have displayed encoded racism, noticed only after commercial deployment. 2 The explosion in health care AI research, specifically machine learning (ML) and deep learning (DL) have definite clinical relevance in ophthalmology. [3][4] In 2018, IDx-DR received approval from the US Food and Drug Administration (FDA) for its autonomous AI for diabetic retinopathy (DR) screening. EyeArt's approval followed shortly. Both set impressively high standards for the validation of AI clinical support decision tools. However, as AI in ophthalmology becomes increasingly commercially available, we will have to contend with the same issues of unintended bias as other industries. Identifying and guarding against these consequences begins with adequate transparency.To highlight theoretical concepts related to bias in ophthalmologic AI, we examine 2 use cases: DR screening using fundus photos and AI utilizing optical coherence tomography (OCT), a burgeoning area of research. In doing so, we raise questions regarding bias and propose the adoption of a reporting tool, model cards, to promote standardization and transparency.

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Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges

Abstract: Supplemental Digital Content is available in the text

Cited by 16 publications

References 64 publications

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

The Glaucoma Suspect Problem: Ways Forward

Promoting Transparency and Standardization in Ophthalmologic Artificial Intelligence: A Call for Artificial Intelligence Model Card

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