Applications of interpretability in deep learning models for ophthalmology

Hanif, AM; Beqiri, Sara; Keane, Pearse A.; Campbell, J. Peter

doi:10.1097/icu.0000000000000780

Cited by 18 publications

(15 citation statements)

References 67 publications

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“…In addition, Weinreb RN contributed significantly to the research of glaucoma disease and made a breakthrough in the use of AI in the classification and progress prediction of glaucoma in multiple dimensions and methods ( 23 , 24 ). Keane PA focused on the diversity and interpretability of AI models, as well as the transformation and clinical application of AI ( 25 , 26 ). Sun Yat-sen University’s Lin HT has made significant contributions to the advancement of AI ophthalmology in China.…”

Section: Discussionmentioning

confidence: 99%

International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis

Jiang¹,

Xie²,

Ma³

et al. 2023

Ann Transl Med

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Background The literature on artificial intelligence (AI)-related topics has been expanding rapidly over the last two decades, showing that AI is a crucial force in advancing ophthalmology. This analysis aims to provide a dynamic and longitudinal bibliometric analysis of AI-related ophthalmic papers. Methods The Web of Science was searched to retrieve papers regarding the application of AI in ophthalmology published in the English language up to May 2022. The variables were analyzed using Microsoft Excel 2019 and GraphPad Prism 9. Data visualization was performed using VOSviewer and CiteSpace. Results In this study, a total of 1,686 publications were analyzed. Recently, AI-related ophthalmology research has increased exponentially. China was the most productive country in this research field, with 483 articles, but the United States of America (446 publications) contributed most to the sum of citations and the H-index. The League of European Research Universities, Ting DSW, and Daniel SW were the most prolific institution and researchers. This field is primarily concerned with diabetic retinopathy (DR), glaucoma, optical coherence tomography, and the classification and diagnosis of fundus pictures. Current hotspots in AI research include deep learning, diagnosing and predicting systemic disorders by fundus images, incidence and progression of ocular diseases, and outcome prediction. Conclusions This analysis thoroughly reviews AI-related research in ophthalmology to help academics better comprehend the growth and possible practice consequences of AI. The association between eye and systemic biomarkers, telemedicine, real-world studies, and the development and application of new AI algorithms, such as visual converters, will continue to be research hotspots over the next few years.

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Section: Discussionmentioning

confidence: 99%

International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis

Jiang¹,

Xie²,

Ma³

et al. 2023

Ann Transl Med

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“… 19–21 However, as models increase in complexity, such models are not only inherently challenging to audit for quality but also limit the user’s ability to “debug” or make informed adjustments to the underlying algorithm. 22 Nomogram-based clinical modelling is a reliable statistical and straightforward visual tool. A comprehensive analysis of all related risk factors can accurately calculate and predict disease occurrence, development, prognosis, and survival by nomogram.…”

Section: Discussionmentioning

confidence: 99%

A Prediction Nomogram for Recurrent Retinal Detachment

Zhou¹,

Lu²,

Chen³

et al. 2023

RMHP

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Purpose Recurrent retinal detachment (re-RD) is one of the complications in rhegmatogenous retinal detachment patients who underwent surgical treatment. We investigated the risk factors for re-RD and developed a nomogram for estimating clinical risk. Methods Univariate and multivariable logistic regression models were performed to determine the association between variables and re-RD, and a nomogram was then developed for re-RD. The nomogram performance was assessed based on its discrimination, calibration, and clinical usefulness. Results This study analyzed 15 potential variables of re-RD in 403 rhegmatogenous retinal detachment patients who underwent initial surgical treatment. Axial length, inferior breaks, retinal break diameter, and surgical methods were independent risk factors for re-RD. A clinical nomogram incorporating these four independent risk factors was constructed. The diagnostic performance of the nomogram was excellent (area under the curve = 0.892, 95% CI: 0.831–0.953). Our study further validated this nomogram by bootstrapping for 500 repetitions. The area under the curve of the bootstrap model was 0.797 (95% CI: 0.712–0.881). This model showed good calibration curve fitting and a positive net benefit in decision curve analysis. Conclusion Axial length, inferior breaks, retinal break diameter, and surgical methods could be risk factors for re-RD. We have developed a prediction nomogram of re-RD for rhegmatogenous retinal detachment following initial surgical treatment.

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“…Interpretability and transparency are crucial to achieving augmented intelligence. There have been increasing innovative efforts in exploring approaches to improve AI's interpretability [50] and explaining how the prediction is derived. Interpretability should be inherently considered in model construction for bedside clinical use, guiding the choice of algorithm to train a model.…”

Section: Interpretabilitymentioning

confidence: 99%