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<p>Glaucoma is the leading cause of irreversible blindness worldwide with heterogeneous progression rates. Artificial Intelligence (AI) may enable accurate progression predictions in clinical practice. We conducted a systematic review to survey quantitative AI performance and examine strengths and shortfalls in current AI approaches with future clinical implementation in mind. Two reviewers independently screened studies in English from MEDLINE, Embase, Web of Science, Cochrane CENTRAL and arXiv since 2014 and performed risk of bias assessment on eligible studies using QUADAS-2. 46 reports of 43 unique studies demonstrated moderate to good performance in predicting glaucoma conversion, biological deterioration and progression to surgery. Several challenges for clinical translation remain, including inconsistent reporting, limitations and heterogeneity in study design and poor AI generalisability and transparency. We encourage future studies to adopt robust study design and transparent reporting and propose the first glaucoma-specific list of recommended practices and reporting items for future clinical implementation.</p>

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A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Yichuan G. Liang,
  • Leo Fan,
  • Armando Teixeira-Pinto,
  • Gerald Liew,
  • Andrew J. R. White

摘要

Glaucoma is the leading cause of irreversible blindness worldwide with heterogeneous progression rates. Artificial Intelligence (AI) may enable accurate progression predictions in clinical practice. We conducted a systematic review to survey quantitative AI performance and examine strengths and shortfalls in current AI approaches with future clinical implementation in mind. Two reviewers independently screened studies in English from MEDLINE, Embase, Web of Science, Cochrane CENTRAL and arXiv since 2014 and performed risk of bias assessment on eligible studies using QUADAS-2. 46 reports of 43 unique studies demonstrated moderate to good performance in predicting glaucoma conversion, biological deterioration and progression to surgery. Several challenges for clinical translation remain, including inconsistent reporting, limitations and heterogeneity in study design and poor AI generalisability and transparency. We encourage future studies to adopt robust study design and transparent reporting and propose the first glaucoma-specific list of recommended practices and reporting items for future clinical implementation.