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Medios– An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy
| Content Provider | Scilit |
|---|---|
| Author | Sosale, Bhavana Sosale, Aravind R. Murthy, Hemanth Naveenam, Muralidhar Sengupta, Sabyasachi |
| Copyright Year | 2020 |
| Abstract | 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. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003589/pdf http://www.ijo.in/article.asp?issn=0301-4738;year=2020;volume=68;issue=2;spage=391;epage=395;aulast=Sosale;type=2 |
| Ending Page | 395 |
| Page Count | 5 |
| Starting Page | 391 |
| File Format | XHTML |
| ISSN | 03014738 |
| e-ISSN | 19983689 |
| DOI | 10.4103/ijo.ijo_1203_19 |
| Journal | Indian Journal of Ophthalmology |
| Issue Number | 2 |
| Volume Number | 68 |
| Language | English |
| Publisher | Medknow |
| Access Restriction | Open |
| Subject Keyword | Ophthalmology Artificial Intelligence Deep Learning Diabetic Retinopathy Indian Journal of Ophthalmology, Volume 68, Issue 2 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Ophthalmology |
