Diagnostic performance of artificial intelligence in strabismus screening: a meta-analysis of 19 872 participants_Chinese Journal of Evidence-Based Medicine

Authors：

QIAN Huiming , YANG Chenyao , LONG Guangfeng ,  SONG Desheng

Department of Ophthalmology, Children’s Hospital of Nanjing Medical University, Nanjing 210008, P. R. China;

Corresponding?author：

SONG Desheng, Email: songdesheng123456@163.com

Keywords：

Strabismus; Artificial intelligence; Diagnosis; Meta-analysis

DOI：

10.7507/1672-2531.202507136

Video：

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Objective To evaluate the diagnostic performance of artificial intelligence (AI) in strabismus detection through a systematic review and meta-analysis. Methods The PubMed, Embase, Cochrane Library, Web of Science, CBM, WanFang Data, and CNKI databases were systematically searched to identify clinical studies on AI-assisted strabismus diagnosis. Study quality was assessed using the QUADAS-AI tool. Diagnostic performance metrics were pooled using a random-effects model. Meta-regression and subgroup analyses were performed to explore sources of heterogeneity. Results Nine studies involving 19872 participants were included. Meta-analysis revealed a pooled sensitivity of 0.94 (95%CI 0.88 to 0.97) and a pooled specificity of 0.95 (95%CI 0.90 to 0.98) in internal validation test sets. For independent external validation cohorts, the pooled sensitivity reached 0.97 (95%CI 0.93 to 1.00) and specificity was 0.99 (95%CI 0.98 to 1.00). Four studies (44%) had a high risk of bias. Meta-regression and subgroup analyses indicated that the performance of AI in diagnosing strabismus was associated with sample size, validation method, use of transfer learning, and feature extraction type. Conclusion AI demonstrates excellent sensitivity and specificity for strabismus screening and early diagnosis, showing potential as a clinical decision support tool. Future studies require standardized methodologies and rigorous external validation to ensure clinical applicability.

Citation： QIAN Huiming, YANG Chenyao, LONG Guangfeng, SONG Desheng. Diagnostic performance of artificial intelligence in strabismus screening: a meta-analysis of 19 872 participants. Chinese Journal of Evidence-Based Medicine, 2026, 26(8): 926-933. doi: 10.7507/1672-2531.202507136 Copy

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2.	Chen DN, Li R, Li XX, et al. Prevalence, incidence and risk factors of strabismus in a Chinese population-based cohort of preschool children: the Nanjing eye study[J]. Br J Ophthalmol, 2021, 105(9): 1203-1210.
3.	Fu J, Li SM, Liu LR, et al. Prevalence of amblyopia and strabismus in a population of 7th-grade junior high school students in central China: the Anyang childhood eye study (ACES)[J]. Ophthalmic Epidemiol, 2014, 21(3): 197-203.
4.	Mojon-azzi SM, Mojon DS. Strabismus and employment: the opinion of headhunters[J]. Acta Ophthalmol, 2009, 87(7): 784-788.
5.	Jablan B, Vucinic V, Eskirovic B, et al. Psychosocial aspects of strabismus[J]. Srp Ark Celok Lek, 2014, 142(7-8): 492-497.
6.	Cotter SA, Tarczy-hornoch K, Song E, et al. Fixation preference and visual acuity testing in a population-based cohort of preschool children with amblyopia risk factors[J]. Ophthalmology, 2009, 116(1): 145-153.
7.	Sprunger DT, Lambert SR, Hercinovic A, et al. American Academy of Ophthalmology Preferred Practice Pattern Pediatric Ophthalmology/Strabismus Panel. Esotropia and exotropia preferred practice pattern?[J]. Ophthalmology. 2023, 130(3): 179-221.
8.	Bruce A, Santorelli G. Prevalence and risk factors of strabismus in a UK multi-ethnic Birth cohort[J]. Strabismus, 2016, 24(4): 153-160.
9.	Buck D, Powell C, Cumberland P, et al. Presenting features and early management of childhood intermittent exotropia in the UK: inception cohort study[J]. Br J Ophthalmol, 2009, 93(12): 1620-1624.
10.	Chougule P, Kekunnaya R. Surgical management of intermittent exotropia: do we have an answer for all[J]. BMJ Open Ophthalmol, 2019, 4(1): e000243.
11.	Heidari Z, Hashemi H, Sotude D, et al. Applications of artificial intelligence in diagnosis of dry eye disease: a systematic review and meta-analysis[J]. Cornea, 2024, 43(10): 1310-1318.
12.	Ambrósio R Jr, Salomao MQ, Barros L, et al. Multimodal diagnostics for keratoconus and ectatic corneal diseases: a paradigm shift[J]. Eye Vis, 2023, 10(1): 45.
13.	Zhou Y, Li G, Li H. Automatic cataract classification using deep neural network with discrete state transition[J]. IEEE Trans Med Imaging, 2020, 39(2): 436-46.
14.	Chu YH, Hu SP, Li ZL, et al. Image analysis-based machine learning for the diagnosis of retinopathy of prematurity a meta-analysis and systematic review[J]. Ophthalmol Retina, 2024, 8(7): 678-687.
15.	Dong L, He W, Zhang R, et al. Artificial intelligence for screening of multiple retinal and optic nerve diseases[J]. JAMA Netw Open, 2022, 5(5): e229960.
16.	Wu DW, Li YF, Zhang HX, et al. An artificial intelligence platform for the screening and managing of strabismus[J]. Eye, 2024, 38(16): 3101-3107.
17.	Mao KL, Yang YH, Guo C, et al. An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos[J]. Ann Transl Med, 2021, 9(5): 3374.
18.	Huang XL, Lee SJ, Kim CZ, et al. An improved strabismus screening method with combination of meta-learning and image processing under data scarcity[J]. Plos One, 2022, 17(8): e0269365.
19.	Shu Q, Pang JL, Liu ZJ, et al. Artificial intelligence for early detection of pediatric eye diseases using mobile photos[J]. JAMA Netw Open, 2024, 7(8): e2425124.
20.	Yarkheir M, Sadeghi M, Azarnoush H, et al. Automated strabismus detection and classification using deep learning analysis of facial images[J]. Sci Rep, 2025, 15(1): 3910.
21.	Zheng C, Yao Q, Lu JW, et al. Detection of referable horizontal strabismus in children’s primary gaze photographs using deep learning[J]. Transl Vis Sci Technol, 2021, 10(1): 33.
22.	Hamid H, Alkindy B, Abbas A, et al. An intelligent strabismus detection method based on convolution neural network[J]. TELKOMNIKA, 2022, 20(6): 1288-1296.
23.	Mengash HA, Hosny M, Al-Qerem A, et al. Methodology for detecting strabismus through video analysis and intelligent mining techniques[J]. Comput Mater Contin, 2021, 67(1): 35648.
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29.	Jabbar A, Jabbar MK, Mahmood T, et al. A retinal detachment based strabismus detection through FEDCNN[J]. Sci Rep, 2024, 14(1): 23255.
30.	Chen ZH, Fu H, Lo WL, et al. Eye-tracking-aided digital system for strabismus diagnosis[J]. Healthc Technol Lett, 2018, 5(1): 1-6.
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34.	Miao Y, Jeon JY, Park G, et al. Virtual reality-based measurement of ocular deviation in strabismus[J]. Comput Meth Programs Biomed, 2020, 185(3): 105132.
35.	Valente TLA, De almeida JDS, Silva AC, et al. Automatic diagnosis of strabismus in digital videos through cover test[J]. Comput Meth Programs Biomed, 2017, 140: 295-305.
36.	Kang YC, Yang HK, Kim YJ, et al. Automated mathematical algorithm for quantitative measurement of strabismus based on photographs of nine cardinal gaze positions[J]. Biomed Res Int, 2022, 2022: 9840494.
37.	Zhao ZH, Meng HB, Li SR, et al. High-accuracy intermittent strabismus screening via wearable eye-tracking and AI-enhanced ocular feature analysis[J]. Biosensors (Basel), 2025, 15(2): 110.
38.	Balki I, Amirabadi A, Levman J, et al. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review[J]. Can Assoc Radiol J, 2019, 70(4): 344-353.
39.	Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[J]. BMJ, 2020, 370: m3164.
40.	Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension[J]. BMJ, 2020, 370: m3210.

1. Repka MX, Lum F, Burugapalli B. Strabismus, strabismus surgery, and reoperation rate in the United States analysis from the IRIS registry[J]. Ophthalmology, 2018, 125(10): 1646-1653.
2. Chen DN, Li R, Li XX, et al. Prevalence, incidence and risk factors of strabismus in a Chinese population-based cohort of preschool children: the Nanjing eye study[J]. Br J Ophthalmol, 2021, 105(9): 1203-1210.
3. Fu J, Li SM, Liu LR, et al. Prevalence of amblyopia and strabismus in a population of 7th-grade junior high school students in central China: the Anyang childhood eye study (ACES)[J]. Ophthalmic Epidemiol, 2014, 21(3): 197-203.
4. Mojon-azzi SM, Mojon DS. Strabismus and employment: the opinion of headhunters[J]. Acta Ophthalmol, 2009, 87(7): 784-788.
5. Jablan B, Vucinic V, Eskirovic B, et al. Psychosocial aspects of strabismus[J]. Srp Ark Celok Lek, 2014, 142(7-8): 492-497.
6. Cotter SA, Tarczy-hornoch K, Song E, et al. Fixation preference and visual acuity testing in a population-based cohort of preschool children with amblyopia risk factors[J]. Ophthalmology, 2009, 116(1): 145-153.
7. Sprunger DT, Lambert SR, Hercinovic A, et al. American Academy of Ophthalmology Preferred Practice Pattern Pediatric Ophthalmology/Strabismus Panel. Esotropia and exotropia preferred practice pattern?[J]. Ophthalmology. 2023, 130(3): 179-221.
8. Bruce A, Santorelli G. Prevalence and risk factors of strabismus in a UK multi-ethnic Birth cohort[J]. Strabismus, 2016, 24(4): 153-160.
9. Buck D, Powell C, Cumberland P, et al. Presenting features and early management of childhood intermittent exotropia in the UK: inception cohort study[J]. Br J Ophthalmol, 2009, 93(12): 1620-1624.
10. Chougule P, Kekunnaya R. Surgical management of intermittent exotropia: do we have an answer for all[J]. BMJ Open Ophthalmol, 2019, 4(1): e000243.
11. Heidari Z, Hashemi H, Sotude D, et al. Applications of artificial intelligence in diagnosis of dry eye disease: a systematic review and meta-analysis[J]. Cornea, 2024, 43(10): 1310-1318.
12. Ambrósio R Jr, Salomao MQ, Barros L, et al. Multimodal diagnostics for keratoconus and ectatic corneal diseases: a paradigm shift[J]. Eye Vis, 2023, 10(1): 45.
13. Zhou Y, Li G, Li H. Automatic cataract classification using deep neural network with discrete state transition[J]. IEEE Trans Med Imaging, 2020, 39(2): 436-46.
14. Chu YH, Hu SP, Li ZL, et al. Image analysis-based machine learning for the diagnosis of retinopathy of prematurity a meta-analysis and systematic review[J]. Ophthalmol Retina, 2024, 8(7): 678-687.
15. Dong L, He W, Zhang R, et al. Artificial intelligence for screening of multiple retinal and optic nerve diseases[J]. JAMA Netw Open, 2022, 5(5): e229960.
16. Wu DW, Li YF, Zhang HX, et al. An artificial intelligence platform for the screening and managing of strabismus[J]. Eye, 2024, 38(16): 3101-3107.
17. Mao KL, Yang YH, Guo C, et al. An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos[J]. Ann Transl Med, 2021, 9(5): 3374.
18. Huang XL, Lee SJ, Kim CZ, et al. An improved strabismus screening method with combination of meta-learning and image processing under data scarcity[J]. Plos One, 2022, 17(8): e0269365.
19. Shu Q, Pang JL, Liu ZJ, et al. Artificial intelligence for early detection of pediatric eye diseases using mobile photos[J]. JAMA Netw Open, 2024, 7(8): e2425124.
20. Yarkheir M, Sadeghi M, Azarnoush H, et al. Automated strabismus detection and classification using deep learning analysis of facial images[J]. Sci Rep, 2025, 15(1): 3910.
21. Zheng C, Yao Q, Lu JW, et al. Detection of referable horizontal strabismus in children’s primary gaze photographs using deep learning[J]. Transl Vis Sci Technol, 2021, 10(1): 33.
22. Hamid H, Alkindy B, Abbas A, et al. An intelligent strabismus detection method based on convolution neural network[J]. TELKOMNIKA, 2022, 20(6): 1288-1296.
23. Mengash HA, Hosny M, Al-Qerem A, et al. Methodology for detecting strabismus through video analysis and intelligent mining techniques[J]. Comput Mater Contin, 2021, 67(1): 35648.
24. Chen ZH, Fu H, Lo WL, et al. Strabismus recognition using eye-tracking data and convolutional neural networks[J]. J Healthc Eng, 2018, 2018: 7692198.
25. Sounderajah V, Ashrafian H, Rose S, et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI[J]. Nat Med, 2021, 27(10): 1663-1665.
26. Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies[J]. Ann Intern Med, 2011, 155(8): 529-536.
27. Yang B, Mallett S, Takwoingi Y, et al. QUADAS-C: a tool for assessing risk of bias in comparative diagnostic accuracy studies[J]. Ann Intern Med, 2021, 174(11): 1592-1599.
28. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement[J]. BMJ, 2015, 350: g7594.
29. Jabbar A, Jabbar MK, Mahmood T, et al. A retinal detachment based strabismus detection through FEDCNN[J]. Sci Rep, 2024, 14(1): 23255.
30. Chen ZH, Fu H, Lo WL, et al. Eye-tracking-aided digital system for strabismus diagnosis[J]. Healthc Technol Lett, 2018, 5(1): 1-6.
31. De almeida JDS, Silva AC, Teixeira JAM, et al. Computer-aided methodology for syndromic strabismus diagnosis[J]. J Digit Imaging, 2015, 28(4): 462-473.
32. De figueiredo LA, Dias JVP, Polati M, et al. Strabismus and artificial intelligence app: optimizing diagnostic and accuracy[J]. Transl Vis Sci Technol, 2021, 10(7): 22.
33. Mesquita MJTAM, Valente TLA, De almeida JDS, et al. A mhealth application for automated detection and diagnosis of strabismus[J]. Int J Med Inform, 2021, 153(2): 104527.
34. Miao Y, Jeon JY, Park G, et al. Virtual reality-based measurement of ocular deviation in strabismus[J]. Comput Meth Programs Biomed, 2020, 185(3): 105132.
35. Valente TLA, De almeida JDS, Silva AC, et al. Automatic diagnosis of strabismus in digital videos through cover test[J]. Comput Meth Programs Biomed, 2017, 140: 295-305.
36. Kang YC, Yang HK, Kim YJ, et al. Automated mathematical algorithm for quantitative measurement of strabismus based on photographs of nine cardinal gaze positions[J]. Biomed Res Int, 2022, 2022: 9840494.
37. Zhao ZH, Meng HB, Li SR, et al. High-accuracy intermittent strabismus screening via wearable eye-tracking and AI-enhanced ocular feature analysis[J]. Biosensors (Basel), 2025, 15(2): 110.
38. Balki I, Amirabadi A, Levman J, et al. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review[J]. Can Assoc Radiol J, 2019, 70(4): 344-353.
39. Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[J]. BMJ, 2020, 370: m3164.
40. Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension[J]. BMJ, 2020, 370: m3210.

Chinese Journal of Evidence-Based Medicine

Diagnostic performance of artificial intelligence in strabismus screening: a meta-analysis of 19 872 participants

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