Image Quality
Cross-source consensus on Image Quality from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Preparation
Risks & contraindications
Comparisons
Highlighted claims
- Image quality was a central determinant of diabetic retinopathy screening performance. — Evaluation of non-ophthalmologist-led and offline AI-assisted models for diabetic retinopathy screening in India: a pragmatic diagnostic accuracy study
- Home-based community screening had much higher ungradability than darkroom-controlled health and wellness centre screening. — Evaluation of non-ophthalmologist-led and offline AI-assisted models for diabetic retinopathy screening in India: a pragmatic diagnostic accuracy study
- Ungradable images can weaken screening effectiveness by increasing false negatives and reducing sensitivity. — Evaluation of non-ophthalmologist-led and offline AI-assisted models for diabetic retinopathy screening in India: a pragmatic diagnostic accuracy study
- Cataracts, small pupils, older age, longer diabetes duration, macular scars and miosis contributed to ungradable images. — Evaluation of non-ophthalmologist-led and offline AI-assisted models for diabetic retinopathy screening in India: a pragmatic diagnostic accuracy study
- Staged mydriasis could be considered for ungradable images, but it was not implemented in this study. — Evaluation of non-ophthalmologist-led and offline AI-assisted models for diabetic retinopathy screening in India: a pragmatic diagnostic accuracy study