Diagnostic performance of deep learning in infectious keratitis: a systematic review and meta-analysis protocol

Introduction
Infectious 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 analysis
This 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 in...

Source: BMJ Open - May 10, 2023 Category: General Medicine Authors: Ong, Z. Z., Sadek, Y., Liu, X., Qureshi, R., Liu, S.-H., Li, T., Sounderajah, V., Ashrafian, H., Ting, D. S. W., Said, D. G., Mehta, J. S., Burton, M. J., Dua, H. S., Ting, D. S. J. Tags: Open access, Ophthalmology Source Type: research