Retina (Philadelphia, Pa.) · 2022
CYTOMEGALOVIRUS RETINITIS SCREENING USING MACHINE LEARNING TECHNOLOGY.
СтатьяНазвание и аннотация: Перевод в очереди
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Srisuriyajan Pitchapa, Cheewaruangroj Nontawat, Polpinit Pattarawit, Laovirojjanakul Wipada
Аннотация
PROPOSE: A screening protocol for cytomegalovirus retinitis (CMVR) by fundus photography was generated, and the diagnostic accuracy of machine learning technology for CMVR screening in HIV patients was investigated.
METHODS: One hundred sixty-five eyes of 90 HIV-positive patients were enrolled and evaluated for CMVR with binocular indirect ophthalmoscopy. Then, a single central field of the fundus image was recorded from each eye. All images were then interpreted by both machine learning models, generated by using the Keras application, and by a third-year ophthalmology resident. Diagnostic performance of CMVR screening using a machine learning model and the third-year ophthalmology resident were analyzed and compared.
RESULTS: Machine learning model, Keras application (VGG16), provided 68.8% (95% confidence interval [CI] = 50%-83.9%) sensitivity and 100% (95% CI = 97.2%-100%) specificity. The program provided accuracy of 93.94%. However, the sensitivity and specificity for the third-year ophthalmology grading were 67.7% (95% CI = 48.6%-83.3%) and 98.4% (95% CI = 94.5%-99.8%). The accuracy for CMVR classification was 89.70%. When considering for sight-threatening retinitis in Zone 1 and excluded Zones 2 and 3, the machine learning model provided high sensitivity of 88.2% (95% CI = 63.6%-98.5%) and high specificity of 100% (95% CI = 97.2%-100%).
CONCLUSION: This study demonstrated the benefit of the machine learning model VGG16, which provided high sensitivity and specificity for detecting sight-threatening CMVR in HIV-positive patients. This model is a useful tool for ophthalmologists in clinical practice for preventing blindness from CMVR, especially during the Coronavrus Disease 2019 pandemic.
METHODS: One hundred sixty-five eyes of 90 HIV-positive patients were enrolled and evaluated for CMVR with binocular indirect ophthalmoscopy. Then, a single central field of the fundus image was recorded from each eye. All images were then interpreted by both machine learning models, generated by using the Keras application, and by a third-year ophthalmology resident. Diagnostic performance of CMVR screening using a machine learning model and the third-year ophthalmology resident were analyzed and compared.
RESULTS: Machine learning model, Keras application (VGG16), provided 68.8% (95% confidence interval [CI] = 50%-83.9%) sensitivity and 100% (95% CI = 97.2%-100%) specificity. The program provided accuracy of 93.94%. However, the sensitivity and specificity for the third-year ophthalmology grading were 67.7% (95% CI = 48.6%-83.3%) and 98.4% (95% CI = 94.5%-99.8%). The accuracy for CMVR classification was 89.70%. When considering for sight-threatening retinitis in Zone 1 and excluded Zones 2 and 3, the machine learning model provided high sensitivity of 88.2% (95% CI = 63.6%-98.5%) and high specificity of 100% (95% CI = 97.2%-100%).
CONCLUSION: This study demonstrated the benefit of the machine learning model VGG16, which provided high sensitivity and specificity for detecting sight-threatening CMVR in HIV-positive patients. This model is a useful tool for ophthalmologists in clinical practice for preventing blindness from CMVR, especially during the Coronavrus Disease 2019 pandemic.