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Objective • To evaluate the accuracy and efficiency of the automated supervised machine-learning algorithm for microaneurysm lesion detection in seven-field color fundus photography. Methods • A total of 616 seven-field color fundus photographs were obtained from 44 patients with diabetic retinopathy (DR) from Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine from 2014 to 2016. Using the microaneurysm detection algorithm developed in this study, the automated identification and labeling of microaneurysm lesions in the standard seven-field color photography of DR were performed. The results were compared with manual labeling by ophthalmologists to evaluate the sensitivity and efficiency of the automated algorithm. Results • In the standard seven-field fundus color photographic image library, the automated algorithm achieved sensitivity of 94.15% in total and 93.09% in the optic disc field (Fl), 94.84% in the macula field (F2), 95.16% in the temporal to macula field (F3)> 94.99% in the superior temporal field (F4), 93.77% in the inferior temporal field (F5), 92.40% in the superior nasal field (F6) and 93.75% in the inferior nasal field (F7), and specificity of 98.05% in total and 98.02% in Fl, 98.06% in F2, 97.97% in F3, 97.91% in F4, 98.07% in F5, 98.03% in F6 and 98.23% in F7. The cost of time per image was (9.2± 0.6) s, 93.2% less time than manual labeling. Conclusion • The automated microaneurysm detection algorithm can accurately and efficiently identify microaneurysm lesions in color fundus photography. [ABSTRACT FROM AUTHOR]