CYTOMEGALOVIRUS RETINITIS SCREENING USING MACHINE LEARNING TECHNOLOGY.

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.