Application and observation of artificial intelligence in clinical practice of fundus screening for diabetic retinopathy with non-mydriatic fundus photography: a retrospective observational study of T2DM patients in Tianjin, China

Abstract

Objective: To observe the consistency of a preliminary report of artificial intelligence (AI) in the clinical practice of fundus screening for diabetic retinopathy (DR) using non-mydriatic fundus photography. Methods: Patients who underwent DR screening in the Metabolic Disease Management Center (MMC) of our hospital were selected as research participants. The degree of coincidence of the AI preliminary report and the ophthalmic diagnosis was compared and analyzed, and the kappa value was calculated. Fundus fluorescein angiography (FFA) was performed in patients referred to the out-of-hospital ophthalmology department, and the consistency between fluorescein angiography and AI diagnosis was evaluated. Results: In total, 6146 patients (12,263 eyes) completed the non-mydriasis fundus examination. The positive DR screening rate was 24.3%. When considering moderate nonproliferative retinopathy as the cut-off point, the kappa coefficient was 0.75 ( p < 0.001), the sensitivity was 0.973, and the precision was 0.642, which was shown in the precision–recall curve. Fifty-nine patients referred to receive FFA were compared with non-mydriatic AI diagnoses. The kappa coefficient was 0.53, and the coincidence rate was 66.9%. Conclusion: Non-mydriasis fundus examination combined with AI has a medium-high consistency with ophthalmologists in DR diagnosis, conducive to early DR screening. Combining diagnosis and treatment modes with the Internet can promote the development of telemedicine, alleviate the shortage of ophthalmology resources, and promote the process of blindness prevention and treatment projects.