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Purpose <p>The global impact of myopia extends far beyond individual ocular health, posing significant challenges to healthcare systems worldwide. Artificial intelligence (AI), particularly deep learning (DL) applied to ophthalmic imaging, offers a promising strategy to ease constraints posed by the myopia epidemic by detecting subtle structural changes early. Here we describe the current literature on AI for detecting retinal sequelae of myopia, including retinal detachments (RD), myopic macular degeneration (MMD), and myopic traction maculopathy (MTM), with attention to imaging modality and model task (classification vs. segmentation).</p> Methods <p>A literature search was conducted to identify studies using DL to detect RD, MMD, and MTM across ophthalmic imaging modalities (including OCT and fundus photography, and where available fluorescein angiography and ultrasonography).</p> Results/findings <p>We reviewed 28 studies that piloted DL models usingclassification and/or segmentation approaches for RD (10 studies), MMD (12 studies), and MTM (6 studies). Reported performance for RD ranged from area under the curve (AUC) 86-100%, accuracy 79.3-98.9%, sensitivity 77.1-97.6%, and specificity 79.7-100%. For MMD, performance ranged from AUC 86-100%, accuracy 85.3-99.8%, sensitivity 37.1-97.8%, and specificity 91.5-99.9%. For MTM, performance ranged from AUC 93.8-99.7%, accuracy 94.3-99.3%, sensitivity 74.5-98.4%, and specificity 84.8-99.7%. Across studies, there was substantial heterogeneity in case definitions, datasets, and evaluation methods, and external validation was inconsistently reported. Many earlier studies used CNN-based architectures, while more recent work increasingly incorporates transformer-based backbones and pretrained or foundation models.</p> Conclusion <p>Researchers have demonstrated excellent results for developing DL models that accurately classify and segment retinal pathologies associated with myopia. However, despite strong performance, additional work is needed to translate these models into clinical use, including robust external validation, calibration for clinical decision-making, and prospective evaluation, particularly for longitudinal prognostication of incident complications in pathologic myopia. </p>

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Shaping the future of myopia: artificial intelligence for vitreoretinal complications of high and pathologic myopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Yeabsira Mesfin,
  • Anish Salvi,
  • Leo Arnal,
  • Curtis Langlotz,
  • Vinit Mahajan,
  • Chase A. Ludwig

摘要

Purpose

The global impact of myopia extends far beyond individual ocular health, posing significant challenges to healthcare systems worldwide. Artificial intelligence (AI), particularly deep learning (DL) applied to ophthalmic imaging, offers a promising strategy to ease constraints posed by the myopia epidemic by detecting subtle structural changes early. Here we describe the current literature on AI for detecting retinal sequelae of myopia, including retinal detachments (RD), myopic macular degeneration (MMD), and myopic traction maculopathy (MTM), with attention to imaging modality and model task (classification vs. segmentation).

Methods

A literature search was conducted to identify studies using DL to detect RD, MMD, and MTM across ophthalmic imaging modalities (including OCT and fundus photography, and where available fluorescein angiography and ultrasonography).

Results/findings

We reviewed 28 studies that piloted DL models usingclassification and/or segmentation approaches for RD (10 studies), MMD (12 studies), and MTM (6 studies). Reported performance for RD ranged from area under the curve (AUC) 86-100%, accuracy 79.3-98.9%, sensitivity 77.1-97.6%, and specificity 79.7-100%. For MMD, performance ranged from AUC 86-100%, accuracy 85.3-99.8%, sensitivity 37.1-97.8%, and specificity 91.5-99.9%. For MTM, performance ranged from AUC 93.8-99.7%, accuracy 94.3-99.3%, sensitivity 74.5-98.4%, and specificity 84.8-99.7%. Across studies, there was substantial heterogeneity in case definitions, datasets, and evaluation methods, and external validation was inconsistently reported. Many earlier studies used CNN-based architectures, while more recent work increasingly incorporates transformer-based backbones and pretrained or foundation models.

Conclusion

Researchers have demonstrated excellent results for developing DL models that accurately classify and segment retinal pathologies associated with myopia. However, despite strong performance, additional work is needed to translate these models into clinical use, including robust external validation, calibration for clinical decision-making, and prospective evaluation, particularly for longitudinal prognostication of incident complications in pathologic myopia.