Diagnostic Accuracy of IDX-DR for Detecting Diabetic Retinopathy: A Systematic Review and Meta-Analysis
Abstract
Purpose Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, making early detection critical to prevent blindness. IDX-DR, an FDA-approved autonomous artificial intelligence (AI) system, has emerged as an innovative solution to improve access to DR screening. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of IDX-DR in detecting diabetic retinopathy. Design Systematic review and meta-analysis. Methods A comprehensive literature search was conducted across PubMed, Embase, Scopus and Web of Science, identifying studies published through October 5, 2024. Studies involving adult patients with Type 1 or Type 2 diabetes and reporting diagnostic metrics such as sensitivity and specificity were included. The primary outcomes were pooled sensitivity and specificity of IDX-DR. A bivariate random-effects model was used for meta-analysis, and summary receiver operating characteristic (SROC) curves were generated to assess diagnostic performance. Statistical analyses were performed using MetaDisc software version 2.0. Results Thirteen studies involving 13,233 participants met the inclusion criteria. IDX-DR's pooled sensitivity was 0.95 (95% CI: 0.82-0.99), and its pooled specificity was 0.91 (95% CI: 0.84-0.95). The SROC curve confirmed IDX-DR's high diagnostic accuracy in detecting diabetic retinopathy across various clinical environments. The AUC value of 0.95 demonstrated high sensitivity and specificity, indicating a robust diagnost…
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Autonomous point-of-care, centralized AI or teleophthalmology, and hybrid AI-plus-human grading models cannot be universally ranked because each offers a different balance of immediacy, specialist oversight, infrastructure requirements, and workflow complexity.
“Autonomous point-of-care, centralized AI or teleophthalmology, and hybrid AI-plus-human grading models should not be ranked universally, because each offers a different balance of immediacy, specialist oversight, infrastructure requirements, and workflow complexity.”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [14]
Product-specific evidence for IDx-DR suggests strong pooled accuracy, but performance is tied to the device, protocol, target disease definition, and intended-use population.
“Product-specific evidence for IDx-DR also suggests strong pooled accuracy, but performance is tied to the device, protocol, target disease definition, and intended-use population (14).”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [14]
Autonomous AI systems can provide same-visit results and reduce dependence on asynchronous grading; centralized AI/teleophthalmology models can support multisite quality assurance and specialist oversight; hybrid AI-plus-human models retain human review for selected or uncertain cases.
“Autonomous systems can provide same-visit results and reduce dependence on asynchronous grading, whereas centralized models may support multisite quality assurance and specialist oversight; hybrid models retain human review for selected or uncertain cases (8,10,11,14,17,21).”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [14]
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Almufareh, M. F., Kausar, S., Humayun, M., Tehsin, S., & Abbasi, S. S. (2026) · World Journal of Diabetes · doi:10.4239/wjd.125158
- Authors
- KHAN, Z., GAIDHANE, A. M., SINGH, M., GANESAN, S., KAUR, M., SHARMA, G. C., RANI, P., SHARMA, R., THAPLIYAL, S., KUSHWAHA, M., KUMAR, H., AGARWAL, R. K., SHABIL, M., VERMA, L., SIDHU, A., MANAN, N. B. A., BUSHI, G., MEHTA, R., SAH, S., SATAPATHY, P., & SAMAL, S. K.
- Affiliations
- Datta Meghe Institute of Higher Education and Research (IN)
- Saveetha University (IN)
- Jain University (IN)
- Vivekananda Global University (IN)
- NIMS University (IN)
- Uttaranchal University (IN)
- Graphic Era University (IN)
- Noida Institute of Engineering and Technology Pharmacy Institute (IN)
- Chitkara University (IN)
- Chitkara University, Himachal Pradesh (IN)
- Lovely Professional University (IN)
- Manav Rachna International Institute of Research and Studies (IN)
- Dr. D. Y. Patil Medical College, Hospital and Research Centre (IN)
- Chandigarh University (IN)
- Karolinska Institutet (SE)
- Corresponding author
- SAMAL, SHAILESH KUMAR
- ORCID
- Venue
- American Journal of Ophthalmology
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 273
- Pages
- 192-204
- ISSN
- 0002-9394 (ISSN-L)
- OpenAlex
- W4407792609
- Cited by
- 49
- References
- 30
- Citations by year
- 2026: 27 · 2025: 22
- FWCI
- 7.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- American Journal of Ophthalmology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- diabetic retinopathy, artificial intelligence, automated retinal analysis, diagnostic accuracy, sensitivity and specificity, diabetic retinopathy screening, type 1 diabetes, type 2 diabetes
- MeSH terms
- Artificial Intelligence, Diabetic Retinopathy, Humans, ROC Curve, Sensitivity and Specificity, Reproducibility of Results
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