An intelligent decision-making system for early diagnosis of macular pathology

Purpose: to develop a software technology based on artificial intelligence methods aimed at analyzing large volumes of optical coherent tomography (OCT) data in order to identify early symptoms of age-related macular degeneration (AMD) and the transition from dry AMD to neovascular AMD (nAMD).

Material and methods. Patients with dry AMD (1125 eyes), wet nAMD (1200 eyes) and subjects without ophthalmic pathology (1205 eyes) underwent a standard ophthalmological examination and macular OCT (Cirrus HD-OCT 4000, Carl Zeiss Meditec AG, Germany) according to the MacularCube scanning protocol with a standard ETDRS macular map. The thickness of the retina from the inner limiting membrane to the retinal pigment epithelium and the presence and location of fovea configuration changes were determined.

Results. Two ultra-precise artificial neural networks (ANN) were created: one intended to identify patients with early signs of dry AMD and the other to identify those with signs of nAMD. By the time when 1000 training images were processed, the image interpretation decision-making accuracy in the first ANN increased to reach 97.6%, in the second ANN, to 96.8%, which shows the high efficiency of this technology. The deep learning processes were run on an Amazon Web Service EC2 GPU. The trained ANN model was also tested on healthy eyes at each estimated probability value. The correspondence between the doctor's diagnosis and the decision taken by the ANN was assessed.

Conclusions. A software technology based on artificial intelligence methods has been developed, which answered the need to process a large amount of OCT data. The technology proved effective in identifying early symptoms of dry and wet forms of AMD and in early diagnosis of the transition of the dry form of AMD into neovascular form, the latter requiring immediate treatment.

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