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Pinal Bavishi

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    Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians
    Mark Chia
    Fred Hersch
    Pearse Keane
    Angus Turner
    British Journal of Ophthalmology, vol. 108 (2024), pp. 268-273
    Preview abstract Background/aims: Deep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness. Methods: We performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR. The validation set consisted of 1682 consecutive, single-field, macula-centred retinal photographs from 864 patients with diabetes (mean age 54.9 years, 52.4% women) at an Indigenous primary care service in Perth, Australia. Three-person adjudication by a panel of specialists served as the reference standard. Results: For mtmDR detection, sensitivity of the DLS was superior to the retina specialist (98.0% (95% CI, 96.5 to 99.4) vs 87.1% (95% CI, 83.6 to 90.6), McNemar’s test p<0.001) with a small reduction in specificity (95.1% (95% CI, 93.6 to 96.4) vs 97.0% (95% CI, 95.9 to 98.0), p=0.006). For vtDR, the DLS’s sensitivity was again superior to the human grader (96.2% (95% CI, 93.4 to 98.6) vs 84.4% (95% CI, 79.7 to 89.2), p<0.001) with a slight drop in specificity (95.8% (95% CI, 94.6 to 96.9) vs 97.8% (95% CI, 96.9 to 98.6), p=0.002). For all-cause referable DR, there was a substantial increase in sensitivity (93.7% (95% CI, 91.8 to 95.5) vs 74.4% (95% CI, 71.1 to 77.5), p<0.001) and a smaller reduction in specificity (91.7% (95% CI, 90.0 to 93.3) vs 96.3% (95% CI, 95.2 to 97.4), p<0.001). Conclusion: The DLS showed improved sensitivity and similar specificity compared with a retina specialist for DR detection. This demonstrates its potential to support DR screening among Indigenous Australians, an underserved population with a high burden of diabetic eye disease. View details
    Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective Study
    Ashish Bora
    Sunny Virmani
    Rayman Huang
    Ilana Traynis
    Lily Peng
    Avinash Varadarajan
    Warisara Pattanapongpaiboon
    Reena Chopra
    Dr. Paisan Raumviboonsuk
    Translational Vision Science & Technology (2023)
    Preview abstract Purpose: Real-world evaluation of a deep learning model that prioritizes patients based on risk of progression to moderate or worse (MOD+) diabetic retinopathy (DR). Methods: This nonrandomized, single-arm, prospective, interventional study included patients attending DR screening at four centers across Thailand from September 2019 to January 2020, with mild or no DR. Fundus photographs were input into the model, and patients were scheduled for their subsequent screening from September 2020 to January 2021 in order of predicted risk. Evaluation focused on model sensitivity, defined as correctly ranking patients that developed MOD+ within the first 50% of subsequent screens. Results: We analyzed 1,757 patients, of which 52 (3.0%) developed MOD+. Using the model-proposed order, the model's sensitivity was 90.4%. Both the model-proposed order and mild/no DR plus HbA1c had significantly higher sensitivity than the random order (P < 0.001). Excluding one major (rural) site that had practical implementation challenges, the remaining sites included 567 patients and 15 (2.6%) developed MOD+. Here, the model-proposed order achieved 86.7% versus 73.3% for the ranking that used DR grade and hemoglobin A1c. Conclusions: The model can help prioritize follow-up visits for the largest subgroups of DR patients (those with no or mild DR). Further research is needed to evaluate the impact on clinical management and outcomes. Translational Relevance: Deep learning demonstrated potential for risk stratification in DR screening. However, real-world practicalities must be resolved to fully realize the benefit. View details
    Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
    Laura Anne Culp
    Jan Freyberg
    Basil Mustafa
    Sebastien Baur
    Simon Kornblith
    Ting Chen
    Patricia MacWilliams
    Sara Mahdavi
    Megan Zoë Walker
    Aaron Loh
    Cameron Chen
    Scott Mayer McKinney
    Zach William Beaver
    Fiona Keleher Ryan
    Mozziyar Etemadi
    Umesh Telang
    Lily Hao Yi Peng
    Geoffrey Everest Hinton
    Mohammad Norouzi
    Nature Biomedical Engineering (2023)
    Preview abstract Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such ‘out of distribution’ performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for ‘Robust and Efficient Medical Imaging with Self-supervision’), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1–33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging. View details
    Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study
    Xinle Sheila Liu
    Tayyeba Ali
    Ami Shah
    Scott Mayer McKinney
    Paisan Ruamviboonsuk
    Angus W. Turner
    Pearse A. Keane
    Peranut Chotcomwongse
    Variya Nganthavee
    Mark Chia
    Josef Huemer
    Jorge Cuadros
    Rajiv Raman
    Lily Hao Yi Peng
    Avinash Vaidyanathan Varadarajan
    Reena Chopra
    Ophthalmology Retina (2022)
    Preview abstract Purpose To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from two-dimensional color fundus photography (CFP), where the reference standard for retinal thickness and fluid presence is derived from three-dimensional optical coherence tomography (OCT). Design Retrospective validation of a DLS across international datasets. Participants Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using datasets from Thailand, the United Kingdom (UK) and the United States and validated using 3,060 unique eyes from 1,582 patients across screening populations in Australia, India and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the UK with mild DR and suspicion of DME based on CFP. Methods The DLS was trained using DME labels from OCT. Presence of DME was based on retinal thickening or intraretinal fluid. The DLS’s performance was compared to expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated integration of the current DLS into an algorithm trained to detect DR from CFPs. Main Outcome Measures Superiority of specificity and non-inferiority of sensitivity of the DLS for the detection of center-involving DME, using device specific thresholds, compared to experts. Results Primary analysis in a combined dataset spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity compared to expert graders who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (p=0.008) and non-inferior sensitivity (p<0.001). In the UK dataset the DLS had a specificity of 80% (p<0.001 for specificity > 50%) and a sensitivity of 100% (p=0.02 for sensitivity > 90%). Conclusions The DLS can generalize to multiple international populations with an accuracy exceeding experts. The clinical value of this DLS to reduce false positive referrals, thus decreasing the burden on specialist eye care, warrants prospective evaluation. View details
    Predicting OCT-derived DME grades from fundus photographs using deep learning
    Arunachalam Narayanaswamy
    Avinash Vaidyanathan Varadarajan
    Dr. Paisan Raumviboonsuk
    Dr. Peranut Chotcomwongse
    Jorge Cuadros
    Lily Hao Yi Peng
    Pearse Keane
    Nature Communications (2020)
    Preview abstract Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF therapies, it has become increasingly important to detect center-involved DME (ci-DME). However, ci-DME is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites. Instead, screening programs rely on the detection of hard exudates as a proxy for DME on color fundus photographs, but this often results in a fair number of false positive and false negative calls. We trained a deep learning model to use color fundus images to directly predict grades derived from OCT exams for DME. Our OCT-based model had an AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, the ophthalmology graders had sensitivities ranging from 82%-85% and specificities ranging from 44%-50%. These metrics correspond to a PPV of 61% (95% CI: 56%-66%) for the OCT-based algorithm and a range of 36-38% (95% CI ranging from 33% -42%) for ophthalmologists. In addition, we used multiple attention techniques to explain how the model is making its prediction. The ability of deep learning algorithms to make clinically relevant predictions that generally requires sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging. View details
    Predicting the risk of developing diabetic retinopathy using deep learning
    Ashish Bora
    Siva Balasubramanian
    Sunny Virmani
    Akinori Mitani
    Guilherme De Oliveira Marinho
    Jorge Cuadros
    Dr. Paisan Raumviboonsuk
    Lily Hao Yi Peng
    Avinash Vaidyanathan Varadarajan
    Lancet Digital Health (2020)
    Preview abstract Background: Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years. Methods: We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes. Findings: The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0·79 (95% CI 0·77–0·81) in the internal validation set. Assessment of the external validation set—which contained only one-field colour fundus photographs—with the one-field deep-learning system gave an AUC of 0·70 (0·67–0·74). In the internal validation set, the AUC of available risk factors was 0·72 (0·68–0·76), which improved to 0·81 (0·77–0·84) after combining the deep-learning system with these risk factors (p<0·0001). In the external validation set, the corresponding AUC improved from 0·62 (0·58–0·66) to 0·71 (0·68–0·75; p<0·0001) following the addition of the deep-learning system to available risk factors. Interpretation: The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes. View details
    Scientific Discovery by Generating Counterfactuals using Image Translation
    Arununachalam Narayanaswamy
    Lily Hao Yi Peng
    Dr. Paisan Raumviboonsuk
    Avinash Vaidyanathan Varadarajan
    Proceedings of MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)
    Preview abstract Visual recognition models are increasingly applied toscientific domains such as, drug studies and medical diag-noses, and model explanation techniques play a critical rolein understanding the source of a model’s performance andmaking its decisions transparent. In this work we investi-gate if explanation techniques can also be used as a mech-anism for scientific discovery. We make two contributions,first we propose a framework to convert predictions from ex-planation techniques to a mechanism of discovery. Secondwe show how generative models in combination with black-box predictors can be used to generate hypotheses (withouthuman priors) that can be critically examined. With thesetechniques we study classification models on retinal fundusimages predicting Diabetic Macular Edema (DME). Essen-tially deep convolutional models on 2D retinal fundus im-ages can do nearly as well as ophthalmologists looking at3D scans, making this an interesting case study of clinicalrelevance. Our work highlights that while existing expla-nation tools are useful, they do not necessarily provide acomplete answer. With the proposed framework we are ableto bridge the gap between model’s performance and humanunderstanding of the underlying mechanism which is of vi-tal scientific interest. View details
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