Using artificial neural networks for early diagnosis of glaucoma

Abstract

Purpose: to summarize the experience of the development and application of artificial neural networks (ANW) in early diagnosis of primary open-angle glaucoma (POAG).Material and methods. A total of 690 patients (918 eyes) were tested. The training clinical group consisted of 459 clinical examples (459 eyes), of which 369 eyes had an initial stage of POAG and 90 eyes had no glaucoma. The testing clinical group was represented by 131 examples (131 eyes), of which 110 eyes belonged to patients with POAG and 21 eyes were without glaucoma. The final diagnostic testing using ANW was conducted on 328 eyes with the diagnosis unknown to the researchers, which belonged to people with suspected POAG. The diagnostic complex included an optimally necessary set of research techniques.Results. ANW identified glaucoma in 198 eyes out of those with suspected glaucoma (60.4 %) with 100 % certainty. 76 eyes (23.2 %) were classified as non-glaucoma, or “healthy”; 54 eyes of the suspected glaucoma patients were identified as “doubtful”, whereupon they were retested by a neural network pool consisting of 5 neural networks. According to the results of the retesting, 28 eyes, or 51.9 % of the “doubtful” ones were identified as having glaucoma, whereas 26 eyes (48.1 %) were identified as non-glaucomatous, i. e. healthy.Conclusion. Our experience suggests that artificial neural networks pose no danger to the doctor or the patient and can be viewed as a very convenient tool for early POAG diagnostics.