Diagnostic Performance of Deep Learning in Infectious Keratitis: A Systematic Review and Meta-Analysis Protocol

Author:

Ong Zun Zheng,Sadek Youssef,Liu Xiaoxuan,Qureshi Riaz,Liu Su-Hsun,Li Tianjing,Sounderajah Viknesh,Ashrafian Hutan,Ting Daniel S. W.,Said Dalia G.,Mehta Jodhbir S.,Burton Matthew J.,Dua Harminder S.,Ting Darren S. J.

Abstract

ABSTRACTIntroductionInfectious keratitis (IK) represents the 5thleading 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. 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.Protocol registrationThis systematic review protocol will be registered with the PROSPERO after peer review.STRENGTH AND LIMITATIONS OF THIS STUDY- This study will serve as the most up-to-date systematic review and meta-analysis specifically evaluating the diagnostic performance of deep learning in infectious keratitis.- The quality of the study will depend on the quality of the available published literature related to this topic.- This study will help identify the gaps in the current clinical evidence, which may be related to study design, quality of the research methodologies, setting of reference standard, risk of bias, and outcome reporting.

Publisher

Cold Spring Harbor Laboratory

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