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Background <p>Retinal detachment (RD) requires prompt detection to prevent vision loss. Ultra-widefield (UWF) imaging captures the peripheral retina, and deep learning (DL) may enable automated RD detection. We aimed to systematically review and meta-analyze the diagnostic accuracy of DL applied to UWF images for detecting RD.</p> Methods <p>We systematically searched PubMed, Web of Science, and reference lists (last search 22 May 2025) for diagnostic-accuracy studies evaluating DL models for retinal detachment on UWF images with extractable 2 × 2 data. Two reviewers independently selected studies, extracted data, and assessed risk of bias and concerns regarding applicability using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Sensitivity, specificity, and area under the curve (AUC) were pooled using random-effects models, with subgroup analyses by dataset origin (internal vs. external) and retinal detachment spectrum.</p> Results <p>We included 11 studies (2017–2024) using UWF imaging and DL, with test set sizes ranging from 89 to 6,222 images. The pooled sensitivity and specificity were 0.95 (95% CI, 0.94–0.96) and 0.99 (95% CI, 0.99–0.99); the AUC of the summary receiver operating characteristic (SROC) = 0.9962. Heterogeneity was high (I² = 92% for sensitivity; 90% for specificity). In subgroup analyses, external evaluations showed higher sensitivity than internal ones (0.97 vs. 0.92), with similarly high specificity (both ≈ 0.99). Heterogeneity remained substantial within subgroups. QUADAS-2 indicated a low risk of bias in most domains, with unclear index test risk common due to non-prespecified thresholds.</p> Conclusions <p>DL applied to UWF imaging shows high diagnostic accuracy for RD, with pooled sensitivity and specificity of 0.95 and 0.99, respectively, and an AUC of 0.9962. However, the evidence is limited by substantial heterogeneity, inconsistent index-test reporting, and variation in case spectrum and sample size, which may constrain generalizability. Overall, these findings suggest that DL combined with UWF imaging is likely to serve as a valuable adjunctive tool for RD detection and triage, particularly in settings where rapid, wide-field assessment is needed.</p> Registration <p>UMIN-CTR UMIN000057903; PROSPERO CRD420251058209.</p>

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Diagnostic accuracy of deep learning using ultra-widefield fundus imaging for retinal detachment: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月3日

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  • Chiaki Kawamoto,
  • Yuki Mizuki,
  • Nobuyuki Horita,
  • Yuichi Kuramochi,
  • Shun Kanasashi,
  • Tatsukata Kawagoe,
  • Nobuhisa Mizuki

摘要

Background

Retinal detachment (RD) requires prompt detection to prevent vision loss. Ultra-widefield (UWF) imaging captures the peripheral retina, and deep learning (DL) may enable automated RD detection. We aimed to systematically review and meta-analyze the diagnostic accuracy of DL applied to UWF images for detecting RD.

Methods

We systematically searched PubMed, Web of Science, and reference lists (last search 22 May 2025) for diagnostic-accuracy studies evaluating DL models for retinal detachment on UWF images with extractable 2 × 2 data. Two reviewers independently selected studies, extracted data, and assessed risk of bias and concerns regarding applicability using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Sensitivity, specificity, and area under the curve (AUC) were pooled using random-effects models, with subgroup analyses by dataset origin (internal vs. external) and retinal detachment spectrum.

Results

We included 11 studies (2017–2024) using UWF imaging and DL, with test set sizes ranging from 89 to 6,222 images. The pooled sensitivity and specificity were 0.95 (95% CI, 0.94–0.96) and 0.99 (95% CI, 0.99–0.99); the AUC of the summary receiver operating characteristic (SROC) = 0.9962. Heterogeneity was high (I² = 92% for sensitivity; 90% for specificity). In subgroup analyses, external evaluations showed higher sensitivity than internal ones (0.97 vs. 0.92), with similarly high specificity (both ≈ 0.99). Heterogeneity remained substantial within subgroups. QUADAS-2 indicated a low risk of bias in most domains, with unclear index test risk common due to non-prespecified thresholds.

Conclusions

DL applied to UWF imaging shows high diagnostic accuracy for RD, with pooled sensitivity and specificity of 0.95 and 0.99, respectively, and an AUC of 0.9962. However, the evidence is limited by substantial heterogeneity, inconsistent index-test reporting, and variation in case spectrum and sample size, which may constrain generalizability. Overall, these findings suggest that DL combined with UWF imaging is likely to serve as a valuable adjunctive tool for RD detection and triage, particularly in settings where rapid, wide-field assessment is needed.

Registration

UMIN-CTR UMIN000057903; PROSPERO CRD420251058209.