Practical Application of Artificial Intelligence Technology in Glaucoma Diagnosis

Abstract

Purpose. By comparing the performance of different models between artificial intelligence (AI) and doctors, we aim to evaluate and identify the optimal model for future usage of AI. Methods. A total of 500 fundus images of glaucoma and 500 fundus images of normal eyes were collected and randomly divided into five groups, with each group corresponding to one round. The AI system provided diagnostic suggestions for each image. Four doctors provided diagnoses without the assistance of the AI in the first round and with the assistance of the AI in the second and third rounds. In the fourth round, doctor B and doctor D made diagnoses with the help of the AI and the other two doctors without the help of the AI. In the last round, doctor A and doctor B made diagnoses with the help of AI and the other two doctors without the help of the AI. Results. Doctor A, doctor B, and doctor D had a higher accuracy in the diagnosis of glaucoma with the assistance of AI in the second ( p = 0.036 , p = 0.003 , and p ≤ 0.000 ) and the third round ( p = 0.021 , p ≤ 0.000 , and p ≤ 0.000 ) than in the first round. The accuracy of at least one doctor was higher than that of AI in the second and third rounds, in spite of no detectable significance ( p = 0.283 , p = 0.727 , p = 0.344 , and p = 0.508 ). The four doctors’ overall accuracy ( p = 0.004 and p ≤ 0.000 ) and sensitivity ( p = 0.006 and p ≤ 0.000 ) as a whole were significantly improved in the second and third rounds. Conclusions. This “Doctor + AI” model can clarify the role of doctors and AI in medical responsibility and ensure the safety of patients, and importantly, this model shows great potential and application prospects.