TY - JOUR
T1 - Diagnostic performance of deep learning in infectious keratitis
T2 - A systematic review and meta-analysis protocol
AU - Ong, Zun Zheng
AU - Sadek, Youssef
AU - Liu, Xiaoxuan
AU - Qureshi, Riaz
AU - Liu, Su Hsun
AU - Li, Tianjing
AU - Sounderajah, Viknesh
AU - Ashrafian, Hutan
AU - Ting, Daniel Shu Wei
AU - Said, Dalia G.
AU - Mehta, Jodhbir S.
AU - Burton, Matthew J.
AU - Dua, Harminder Singh
AU - Ting, Darren Shu Jeng
N1 - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
PY - 2023/5/10
Y1 - 2023/5/10
N2 - 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 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 dissemination No ethical approval is required for this systematic review. We plan to disseminate our findings via presentation/publication in a peer-reviewed journal. PROSPERO registration number CRD42022348596.
AB - 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 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 dissemination No ethical approval is required for this systematic review. We plan to disseminate our findings via presentation/publication in a peer-reviewed journal. PROSPERO registration number CRD42022348596.
KW - Artificial intelligence
KW - Corneal infection
KW - Diagnosis
KW - Keratitis
KW - Ophthalmology
KW - Meta-Analysis as Topic
KW - Humans
KW - Artificial Intelligence
KW - Deep Learning
KW - Keratitis/diagnosis
KW - Sample Size
KW - Systematic Reviews as Topic
KW - Research Design
UR - https://www.scopus.com/pages/publications/85158866429
U2 - 10.1136/bmjopen-2022-065537
DO - 10.1136/bmjopen-2022-065537
M3 - 文章
C2 - 37164459
AN - SCOPUS:85158866429
SN - 2044-6055
VL - 13
SP - e065537
JO - BMJ Open
JF - BMJ Open
IS - 5
M1 - e065537
ER -