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Middle East African Journal of Ophthalmology Middle East African Journal of Ophthalmology
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ORIGINAL ARTICLE
Year : 2021  |  Volume : 28  |  Issue : 2  |  Page : 81-86

Validation of artificial intelligence algorithm in the detection and staging of diabetic retinopathy through fundus photography: An automated tool for detection and grading of diabetic retinopathy


1 Department of Ophthalmology, St. Johns Medical College, Bengaluru, Karnataka, India
2 Department of Ophthalmology, LEBEN CARE Technologies Pvt. Ltd, Singapore

Correspondence Address:
Dr. Bhargavi Pawar
Department of Ophthalmology, St. Johns Medical College, Bengaluru, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/meajo.meajo_406_20

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PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of vision loss globally, and early detection plays a significant role in the prognosis. Several studies have been done on the single field fundus photography and artificial intelligence (AI) in DR screening using standardized data sets in urban outpatient settings. This study was carried out to validate AI algorithm in the detection of DR severity using fundus photography in real-time rural setting. METHODS: This cross-sectional study was carried out among 138 patients who underwent routine ophthalmic examination, irrespective of their diabetic status. The participants were subjected to a single field color fundus photography using nonmydriatic fundus camera. The images acquired were processed by AI algorithm for image quality, presence and refer ability of DR. The results were graded by four ophthalmologists. Interobserver variability between the four observers was also calculated. RESULTS: Of the 138 patients, 26 patients (18.84%) had some stage of DR, represented by 47 images (17.03%) positive for signs of DR. All 26 patients were immoderate or severe stage. About 6.5% of the images were considered as not gradable due to poor optical quality. The average agreement between pairs of the four graders was 95.16% for referable DR (RDR). The AI showed 100% sensitivity in detecting DR while the specificity for RDR was 91.47%. CONCLUSION: AI has shown excellent sensitivity and specificity in RDR detection, at par with the performance of individual ophthalmologists and is an invaluable tool for DR screening.


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