ORIGINAL ARTICLE |
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Year : 2020 | Volume
: 68
| Issue : 2 | Page : 391-395 |
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Medios– An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy
Bhavana Sosale1, Aravind R Sosale1, Hemanth Murthy2, Sabyasachi Sengupta3, Muralidhar Naveenam2
1 Department of Diabetology, Diacon Hospital, Retina Institute of Karnataka, Karnataka, India 2 Department of Vitreo-Retinal Surgery, Retina Institute of Karnataka, Karnataka, India 3 Department of Vitreo-Retinal Surgery, Future Vision Eye Care, Mumbai, Maharashtra, India
Correspondence Address:
Dr. Bhavana Sosale 360, Diacon Hospital, 19th Mail, 1st Block, Rajajinagar, Bengaluru - 560 010, Karnataka India
Source of Support: None, Conflict of Interest: None | Check |
DOI: 10.4103/ijo.IJO_1203_19
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Purpose: An observational study to assess the sensitivity and specificity of the Medios smartphone-based offline deep learning artificial intelligence (AI) software to detect diabetic retinopathy (DR) compared with the image diagnosis of ophthalmologists. Methods: Patients attending the outpatient services of a tertiary center for diabetes care underwent 3-field dilated retinal imaging using the Remidio NM FOP 10. Two fellowship-trained vitreoretinal specialists separately graded anonymized images and a patient-level diagnosis was reached based on grading of the worse eye. The images were subjected to offline grading using the Medios integrated AI-based software on the same smartphone used to acquire images. The sensitivity and specificity of the AI in detecting referable DR (moderate non-proliferative DR (NPDR) or worse disease) was compared to the gold standard diagnosis of the retina specialists. Results: Results include analysis of images from 297 patients of which 176 (59.2%) had no DR, 35 (11.7%) had mild NPDR, 41 (13.8%) had moderate NPDR, and 33 (11.1%) had severe NPDR. In addition, 12 (4%) patients had PDR and 36 (20.4%) had macular edema. Sensitivity and specificity of the AI in detecting referable DR was 98.84% (95% confidence interval [CI], 97.62–100%) and 86.73% (95% CI, 82.87–90.59%), respectively. The area under the curve was 0.92. The sensitivity for vision-threatening DR (VTDR) was 100%. Conclusion: The AI-based software had high sensitivity and specificity in detecting referable DR. Integration with the smartphone-based fundus camera with offline image grading has the potential for widespread applications in resource-poor settings.
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