Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis
<b>Purpose:</b> To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. <b>Materials and Methods:</b> Relevant studies published up to 30 October 20...
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MDPI AG
2025-02-01
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| author | Xiao Chun Ling Henry Shen-Lih Chen Po-Han Yeh Yu-Chun Cheng Chu-Yen Huang Su-Chin Shen Yung-Sung Lee |
| author_facet | Xiao Chun Ling Henry Shen-Lih Chen Po-Han Yeh Yu-Chun Cheng Chu-Yen Huang Su-Chin Shen Yung-Sung Lee |
| author_sort | Xiao Chun Ling |
| collection | DOAJ |
| description | <b>Purpose:</b> To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. <b>Materials and Methods:</b> Relevant studies published up to 30 October 2024 were retrieved from PubMed, Medline, EMBASE, Cochrane Library, Web of Science, and ClinicalKey. A bivariate random-effects model was employed to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, and area under the receiver operating characteristic curve (AUROC). <b>Results:</b> A total of 48 studies were included in the meta-analysis. DL algorithms demonstrated high diagnostic performance in glaucoma detection using fundus photography and OCT images. For fundus photography, the pooled sensitivity and specificity were 0.92 (95% CI: 0.89–0.94) and 0.93 (95% CI: 0.90–0.95), respectively, with an AUROC of 0.90 (95% CI: 0.88–0.92). For the OCT imaging, the pooled sensitivity and specificity were 0.90 (95% CI: 0.84–0.94) and 0.87 (95% CI: 0.81–0.91), respectively, with an AUROC of 0.86 (95% CI: 0.83–0.90). In predicting glaucoma progression, DL models generally showed less robust performance, with pooled sensitivities and specificities ranging lower than in diagnostic tasks. Internal validation datasets showed higher accuracy than external validation datasets. <b>Conclusions:</b> DL algorithms achieve excellent performance in diagnosing glaucoma using fundus photography and OCT imaging. To enhance the prediction of glaucoma progression, future DL models should integrate multimodal data, including functional assessments, such as visual field measurements, and undergo extensive validation in real-world clinical settings. |
| format | Article |
| id | doaj-art-eb77eac9738b4d21891b086725daed2a |
| institution | DOAJ |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj-art-eb77eac9738b4d21891b086725daed2a2025-08-20T03:12:16ZengMDPI AGBiomedicines2227-90592025-02-0113242010.3390/biomedicines13020420Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-AnalysisXiao Chun Ling0Henry Shen-Lih Chen1Po-Han Yeh2Yu-Chun Cheng3Chu-Yen Huang4Su-Chin Shen5Yung-Sung Lee6Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, TaiwanDepartment of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, TaiwanDepartment of Ophthalmology, New Taipei Municipal Tucheng Hospital, New Taipei 236, TaiwanDepartment of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, TaiwanDepartment of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, TaiwanDepartment of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, TaiwanDepartment of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan 333, Taiwan<b>Purpose:</b> To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. <b>Materials and Methods:</b> Relevant studies published up to 30 October 2024 were retrieved from PubMed, Medline, EMBASE, Cochrane Library, Web of Science, and ClinicalKey. A bivariate random-effects model was employed to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, and area under the receiver operating characteristic curve (AUROC). <b>Results:</b> A total of 48 studies were included in the meta-analysis. DL algorithms demonstrated high diagnostic performance in glaucoma detection using fundus photography and OCT images. For fundus photography, the pooled sensitivity and specificity were 0.92 (95% CI: 0.89–0.94) and 0.93 (95% CI: 0.90–0.95), respectively, with an AUROC of 0.90 (95% CI: 0.88–0.92). For the OCT imaging, the pooled sensitivity and specificity were 0.90 (95% CI: 0.84–0.94) and 0.87 (95% CI: 0.81–0.91), respectively, with an AUROC of 0.86 (95% CI: 0.83–0.90). In predicting glaucoma progression, DL models generally showed less robust performance, with pooled sensitivities and specificities ranging lower than in diagnostic tasks. Internal validation datasets showed higher accuracy than external validation datasets. <b>Conclusions:</b> DL algorithms achieve excellent performance in diagnosing glaucoma using fundus photography and OCT imaging. To enhance the prediction of glaucoma progression, future DL models should integrate multimodal data, including functional assessments, such as visual field measurements, and undergo extensive validation in real-world clinical settings.https://www.mdpi.com/2227-9059/13/2/420glaucomadeep learningartificial intelligencefundus photographyoptical coherence tomographydiagnosis |
| spellingShingle | Xiao Chun Ling Henry Shen-Lih Chen Po-Han Yeh Yu-Chun Cheng Chu-Yen Huang Su-Chin Shen Yung-Sung Lee Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis Biomedicines glaucoma deep learning artificial intelligence fundus photography optical coherence tomography diagnosis |
| title | Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis |
| title_full | Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis |
| title_fullStr | Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis |
| title_short | Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis |
| title_sort | deep learning in glaucoma detection and progression prediction a systematic review and meta analysis |
| topic | glaucoma deep learning artificial intelligence fundus photography optical coherence tomography diagnosis |
| url | https://www.mdpi.com/2227-9059/13/2/420 |
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