Artificial intelligence-based screening for diabetic retinopathy at community hospital

Abstract

Objectives: The purpose of this study is to assess the accuracy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and to explore the feasibility of applying AI-based technique to community hospital for DR screening.

Methods: Nonmydriatic fundus photos were taken for 889 diabetic patients who were screened in community hospital clinic. According to DR international classification standards, ophthalmologists and AI identified and classified these fundus photos. The sensitivity and specificity of AI automatic grading were evaluated according to ophthalmologists' grading.

Results: DR was detected by ophthalmologists in 143 (16.1%) participants and by AI in 145 (16.3%) participants. Among them, there were 101 (11.4%) participants diagnosed with referable diabetic retinopathy (RDR) by ophthalmologists and 103 (11.6%) by AI. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 90.79% (95% CI 86.4-94.1), 98.5% (95% CI 97.8-99.0) and 0.946 (95% CI 0.935-0.956), respectively. For detecting RDR, the sensitivity, specificity and AUC of AI were 91.18% (95% CI 86.4-94.7), 98.79% (95% CI 98.1-99.3) and 0.950 (95% CI 0.939-0.960), respectively.

Conclusion: AI has high sensitivity and specificity in detecting DR and RDR, so it is feasible to carry out AI-based DR screening in outpatient clinic of community hospital.

Fig. 1

Examples of the heatmap generated by AI on different severity levels of DR

Fig. 2

Comparison of diabetic retinopathy (DR) grading between ophthalmologist and AI

Fig. 3

Venn diagram showed the overlap comparison of RDR between human and automated grading