Diagnostic Performance of Deep Learning in Infectious Keratitis: A Systematic Review and Meta-Analysis Protocol
Diagnostic Performance of Deep Learning in Infectious Keratitis: A Systematic Review and Meta-Analysis Protocol
复制标题
深度学习在传染性角膜炎中的诊断性能：系统回顾和荟萃分析方案
DOI：
10.1101/2022.10.11.22280968
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ong Z
中科院分区：
文献类型：
--
作者：
Ong Z
感染性角膜炎是全球第五大致盲眼病。诊断的延误往往是IK发展为不可逆转的视力损害和/或失明的主要因素。微生物培养产量低、周转时间长、分化差的临床特征和多菌感染进一步加剧了诊断挑战。近年来，深度学习作为人工智能的一个子领域，在辅助自动化医疗诊断、临床分诊和决策以及提高医疗服务工作流程效率方面迅速崛起。最近的研究已经证明了使用DL辅助诊断IK的潜力，尽管其准确性仍有待阐明。这篇系统的综述和荟萃分析旨在与临床专家和/或微生物学结果(当前的“黄金标准”)一起，批判性地检查和比较各种DL模型在诊断IK方面的性能，目的是向实践提供关于DL辅助诊断模型的临床适用性和部署的信息。方法和分析本综述将考虑将任何DL模型应用于诊断疑似IK患者的研究，包括细菌、真菌、原生动物和/或病毒来源。我们将搜索各种电子数据库，包括EMBASE和MEDLINE，以及试验登记。语言和出版日期将不受限制。两名独立评审员将对标题、摘要和全文进行评估。提取的数据将包括每项主要研究的细节，包括标题、发表年份、作者、使用的DL模型类型、总体、样本量、决策阈值和诊断性能。当结果报告有足够的相似性时，我们将对纳入的主要研究进行荟萃分析。伦理和传播这项系统性审查不需要伦理批准。我们计划通过在同行评议的杂志上发表演讲/发表来传播我们的发现。PROSPERO注册号为CRD42022348596。
IntroductionInfectious keratitis (IK) represents the fifth-leading cause of blindness worldwide. A delay in diagnosis is often a major factor in progression to irreversible visual impairment and/or blindness from IK. The diagnostic challenge is further compounded by low microbiological culture yield, long turnaround time, poorly differentiated clinical features and polymicrobial infections. In recent years, deep learning (DL), a subfield of artificial intelligence, has rapidly emerged as a promising tool in assisting automated medical diagnosis, clinical triage and decision-making, and improving workflow efficiency in healthcare services. Recent studies have demonstrated the potential of using DL in assisting the diagnosis of IK, though the accuracy remains to be elucidated. This systematic review and meta-analysis aims to critically examine and compare the performance of various DL models with clinical experts and/or microbiological results (the current ‘gold standard’) in diagnosing IK, with an aim to inform practice on the clinical applicability and deployment of DL-assisted diagnostic models.Methods and analysisThis review will consider studies that included application of any DL models to diagnose patients with suspected IK, encompassing bacterial, fungal, protozoal and/or viral origins. We will search various electronic databases, including EMBASE and MEDLINE, and trial registries. There will be no restriction to the language and publication date. Two independent reviewers will assess the titles, abstracts and full-text articles. Extracted data will include details of each primary studies, including title, year of publication, authors, types of DL models used, populations, sample size, decision threshold and diagnostic performance. We will perform meta-analyses for the included primary studies when there are sufficient similarities in outcome reporting.Ethics and disseminationNo ethical approval is required for this systematic review. We plan to disseminate our findings via presentation/publication in a peer-reviewed journal.PROSPERO registration numberCRD42022348596.