Will AI Replace Ophthalmologists?

Korot, Edward; Wagner, Siegfried; Faes, Livia; Liu, Xiaoxuan; Huemer, Josef; Ferraz, Daniel; Keane, Pearse A.; Balaskas, Konstantinos

doi:10.1167/tvst.9.2.2

Cited by 22 publications

(13 citation statements)

References 25 publications

(23 reference statements)

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“…In this regard it seems we have not quite reached the tipping point. In the past few years, the question whether or when AI will replace human experts has been pondered in many areas of biomedical imaging [132] , [268] , [267] , [269] , [266] , [314] , [315] . It goes without saying that decision making in biomedicine is more critical and risk-averse than in most other technological domains.…”

Section: Discussionmentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

108

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Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

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Section: Discussionmentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

108

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“…The first question was addressed in a recent article by Korot el al, stating that although current AI systems may be good at dealing with high volume data related tasks; however, a clinician´s ability to interpret the complex and multivariate data-driven AI recommendations should be the next step for ophthalmologists. 140 The black box problem refers to the interpretability of AI systems, and those end-to-end solutions will not provide insight to the clinicians regarding. 141 As these DL systems are providing interesting results, clinicians as well as public health providers would be interested to be able to interpret, and scale the newly discovered insights, for example, individually stratifying certain risk factors.…”

Section: Health Economicsmentioning

confidence: 99%

<p>The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence</p>

Huemer¹,

Wagner²,

Sim³

2020

OPTH

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As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approachesautomated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.

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“…The discovery of quantitative relationships between retinal appearance and systemic pathophysiology readily aligns with pre-established conceptions of microvascular and degenerative tissue-level insults 20 . However, deep learning has shown that these algorithms demonstrate capability in tasks which were not previously thought possible 21 . Harnessing this power, new insights into relationships between retinal structure and systemic pathophysiology could expand existing knowledge of disease mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…A study by Poplin et al demonstrated a deep-learning learning algorithm which could accurately predict cardiovascular risk factors from fundus photos 22 ; More surprising to ophthalmologists was the successful prediction of demographic information such as age and gender, the latter with an area under the curve (AUC) of 0.97. Here, the physiologic cause and effect relationships are not readily apparent to domain experts 21 . Predicting gender from fundus photos, previously inconceivable to those who spent their careers looking at retinas, also withstood external validation on an independent dataset of patients with different baseline demographics 23 .…”

Section: Introductionmentioning

confidence: 99%

Predicting sex from retinal fundus photographs using automated deep learning

Korot

Pontikos

Liu

et al. 2021

Sci Rep

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Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

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Will AI Replace Ophthalmologists?

Abstract: This work is licensed under a Creative Commons Attribution 4.0 International License.

Cited by 22 publications

References 25 publications

A bird’s-eye view of deep learning in bioimage analysis

A bird’s-eye view of deep learning in bioimage analysis

<p>The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence</p>

Predicting sex from retinal fundus photographs using automated deep learning

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