Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis
Ong, Zun Zheng Sadek, Youssef Qureshi, Riaz Liu, Su-Hsun Li, Tianjing Liu, Xiaoxuan Takwoingi, Yemisi Sounderajah, Viknesh Ashrafian, Hutan Ting, Daniel S W
Ong, Zun Zheng
Sadek, Youssef
Qureshi, Riaz
Liu, Su-Hsun
Li, Tianjing
Liu, Xiaoxuan
Takwoingi, Yemisi
Sounderajah, Viknesh
Ashrafian, Hutan
Ting, Daniel S W
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Affiliation
Sandwell and West Birmingham NHS Trust; University of Birmingham; University of Colorado; University Hospitals Birmingham NHS Foundation Trust; Health Data Research UK; Imperial College London; Singapore Eye Research Institute; Duke-NUS Medical School; University of Nottingham; Queen's Medical Centre; London School of Hygiene and Tropical Medicine; Moorfields Eye Hospital NHS Foundation Trust; UCL Institute of Ophthalmology
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Publication date
2024-10-18
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Abstract
Background: Infectious keratitis (IK) is the leading cause of corneal blindness globally. Deep learning (DL) is an emerging tool for medical diagnosis, though its value in IK is unclear. We aimed to assess the diagnostic accuracy of DL for IK and its comparative accuracy with ophthalmologists.
Methods: In this systematic review and meta-analysis, we searched EMBASE, MEDLINE, and clinical registries for studies related to DL for IK published between 1974 and July 16, 2024. We performed meta-analyses using bivariate models to estimate summary sensitivities and specificities. This systematic review was registered with PROSPERO (CRD42022348596).
Findings: Of 963 studies identified, 35 studies (136,401 corneal images from >56,011 patients) were included. Most studies had low risk of bias (68.6%) and low applicability concern (91.4%) in all domains of QUADAS-2, except the index test domain. Against the reference standard of expert consensus and/or microbiological results (seven external validation studies; 10,675 images), the summary estimates (95% CI) for sensitivity and specificity of DL for IK were 86.2% (71.6-93.9) and 96.3% (91.5-98.5). From 28 internal validation studies (16,059 images), summary estimates for sensitivity and specificity were 91.6% (86.8-94.8) and 90.7% (84.8-94.5). Based on seven studies (4007 images), DL and ophthalmologists had comparable summary sensitivity [89.2% (82.2-93.6) versus 82.2% (71.5-89.5); P = 0.20] and specificity [(93.2% (85.5-97.0) versus 89.6% (78.8-95.2); P = 0.45].
Interpretation: DL models may have good diagnostic accuracy for IK and comparable performance to ophthalmologists. These findings should be interpreted with caution due to the image-based analysis that did not account for potential correlation within individuals, relatively homogeneous population studies, lack of pre-specification of DL thresholds, and limited external validation. Future studies should improve their reporting, data diversity, external validation, transparency, and explainability to increase the reliability and generalisability of DL models for clinical deployment.
Citation
Ong ZZ, Sadek Y, Qureshi R, Liu SH, Li T, Liu X, Takwoingi Y, Sounderajah V, Ashrafian H, Ting DSW, Mehta JS, Rauz S, Said DG, Dua HS, Burton MJ, Ting DSJ. Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis. EClinicalMedicine. 2024 Oct 18;77:102887. doi: 10.1016/j.eclinm.2024.102887.
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Article