Incidence of Referable Retinal Disease in Diabetic Patients at a Primary Care Practice

Lee, Terry; Amason, Joshua; Risco, Amanda Del; Kim, Joon-Bom; Cousins, Scott W.; Hadziahmetovic, Majda

doi:10.1177/24741264211044223

Cited by 3 publications

References 41 publications

(57 reference statements)

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Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

Martin,

Cook,

Frost

et al. 2024

Eye

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Background/Objectives Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. Subjects/Methods Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. Results Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. Conclusions The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.

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Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

Martin,

Cook,

Frost

et al. 2024

Eye

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The Use of Optical Coherence Tomography for Early Glaucoma Screening in a Population of Patients with Diabetes

Johnson

Gupta

Lee

et al. 2023

Ophthalmic Epidemiology

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Ocular Biomarkers: Useful Incidental Findings by Deep Learning Algorithms in Retinal Photographs

Martin

Cook²,

Frost³

et al. 2023

Preprint

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Background/Objectives: Ocular biomarkers can provide immediate and non-invasive indications of ocular and systemic health but are underutilised due to the paucity and inequitable distribution of eyecare professionals. Deep learning analysis of colour fundus photographs has task shifting potential to efficiently differentiate ocular biomarkers, as well as providing earlier diagnosis, additional reach via telehealth, and ultimately improving population health. The study aimed to explore the clinical implications arising from deep learning detection of non-target retinal biomarkers in colour fundus photographs. Subjects/Methods: Patients referred for treatment-resistant hypertension were imaged between 2016 and 2022 at a specialty clinic in Perth, Australia. The same 45° colour retinal photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. All positive results for diabetic retinopathy in non-diabetic participants were graded by two expert retinal specialists. Results: A total of 29 non-diabetic participants were flagged as positive for diabetic retinopathy by deep learning algorithms. Of these, 28 (96.6%) had clinically significant non-target retinal pathology likely to benefit from early intervention. The algorithms with more target diseases captured less incidental disease. All three algorithms demonstrated a correlation between false positive diabetic retinopathy results and severity of hypertensive retinopathy. Conclusions: The findings indicate that existing deep learning models can identify additional pathologies likely to benefit from early intervention within an at-risk, hypertensive cohort, and have potential for immediate clinical application in other populations. The findings also support a pivotal pathway toward autonomous comprehensive screening.

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Incidence of Referable Retinal Disease in Diabetic Patients at a Primary Care Practice

Cited by 3 publications

References 41 publications

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

The Use of Optical Coherence Tomography for Early Glaucoma Screening in a Population of Patients with Diabetes

Ocular Biomarkers: Useful Incidental Findings by Deep Learning Algorithms in Retinal Photographs

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