Summary:
«Infectious keratitis (IK) represents the 5th 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.»
Article written by Zun Zheng Ong, Youssef Sadek, Xiaoxuan Liu, Riaz Qureshi, Su-Hsun Liu, Tianjing Li, Viknesh Sounderajah, Hutan Ashrafian, Daniel S. W. Ting, Dalia G. Said, Jodhbir S. Mehta, Matthew J. Burton, Harminder S. Dua, Darren S. J. Ting
10|10|2022
Source:
MedRXIV
https://www.medrxiv.org/content/10.1101/2022.10.11.22280968v1?rss=1