Indian journal of ophthalmology · 2022
Development and validation of an offline deep learning algorithm to detect vitreoretinal abnormalities on ocular ultrasound.
СтатьяНазвание и аннотация: Перевод в очереди
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Adithya Venkatesh Krishna, Baskaran Prabu, Aruna S, Mohankumar Arthi, Hubschman Jean Pierre, Shukla Aakriti Garg, Venkatesh Rengaraj
Аннотация
PURPOSE: We describe our offline deep learning algorithm (DLA) and validation of its diagnostic ability to identify vitreoretinal abnormalities (VRA) on ocular ultrasound (OUS).
METHODS: Enrolled participants underwent OUS. All images were classified as normal or abnormal by two masked vitreoretinal specialists (AS, AM). A data set of 4902 OUS images was collected, and 4740 images of satisfactory quality were used. Of this, 4319 were processed for further training and development of DLA, and 421 images were graded by vitreoretinal specialists (AS and AM) to obtain ground truth. The main outcome measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristic (AUROC).
RESULTS: Our algorithm demonstrated high sensitivity and specificity in identifying VRA on OUS ([90.8%; 95% confidence interval (CI): 86.1-94.3%] and [97.1% (95% CI: 93.7-98.9%], respectively). PPV and NPV of the algorithm were also high ([97.0%; 95% CI: 93.7-98.9%] and [90.8%; 95% CI: 86.2-94.3%], respectively). The AUROC was high at 0.939, and the intergrader agreement was nearly perfect with Cohen's kappa of 0.938. The model demonstrated high sensitivity in predicting vitreous hemorrhage (100%), retinal detachment (97.4%), and choroidal detachment (100%).
CONCLUSION: Our offline DLA software demonstrated reliable performance (high sensitivity, specificity, AUROC, PPV, NPV, and intergrader agreement) for predicting VRA on OUS. This might serve as an important tool for the ophthalmic technicians who are involved in community eye screening at rural settings where trained ophthalmologists are not available.
METHODS: Enrolled participants underwent OUS. All images were classified as normal or abnormal by two masked vitreoretinal specialists (AS, AM). A data set of 4902 OUS images was collected, and 4740 images of satisfactory quality were used. Of this, 4319 were processed for further training and development of DLA, and 421 images were graded by vitreoretinal specialists (AS and AM) to obtain ground truth. The main outcome measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristic (AUROC).
RESULTS: Our algorithm demonstrated high sensitivity and specificity in identifying VRA on OUS ([90.8%; 95% confidence interval (CI): 86.1-94.3%] and [97.1% (95% CI: 93.7-98.9%], respectively). PPV and NPV of the algorithm were also high ([97.0%; 95% CI: 93.7-98.9%] and [90.8%; 95% CI: 86.2-94.3%], respectively). The AUROC was high at 0.939, and the intergrader agreement was nearly perfect with Cohen's kappa of 0.938. The model demonstrated high sensitivity in predicting vitreous hemorrhage (100%), retinal detachment (97.4%), and choroidal detachment (100%).
CONCLUSION: Our offline DLA software demonstrated reliable performance (high sensitivity, specificity, AUROC, PPV, NPV, and intergrader agreement) for predicting VRA on OUS. This might serve as an important tool for the ophthalmic technicians who are involved in community eye screening at rural settings where trained ophthalmologists are not available.