Medios– An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy

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. Patients attending the outpatient services of a terti...

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Veröffentlicht in:Indian journal of ophthalmology Jg. 68; H. 2; S. 391 - 395
Hauptverfasser: Sosale, Bhavana, Sosale, AravindR, Murthy, Hemanth, Sengupta, Sabyasachi, Naveenam, Muralidhar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: India Medknow Publications and Media Pvt. Ltd 01.02.2020
Medknow Publications & Media Pvt. Ltd
Wolters Kluwer - Medknow
Schlagworte:
Algorithms
Ambulatory care facilities
Artificial intelligence
Cameras
Computer programs
Diabetes
Diabetes mellitus
Diabetes therapy
Diabetic retinopathy
Diagnosis
Edema
Family medical history
Hospitals
Internet access
Neural networks
Ophthalmology
Original
Patients
Performance evaluation
Quality
Retina
Retinopathy
Smart phones
Smartphones
Software
Tropicamide
India
Singapore
diabetic retinopathy
deep learning
Artificial intelligence
ISSN:0301-4738, 1998-3689, 1998-3689
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Zusammenfassung: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. 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 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%. 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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ISSN:0301-4738
1998-3689
1998-3689
DOI:10.4103/ijo.IJO_1203_19