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Diagnostic performance of deep learning in infectious keratitis: a systematic review and meta-analysis protocol
Zun Zheng Ong1, Youssef Sadek1, Xiaoxuan Liu2
1Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK.
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.
Insights
Deep learning (DL) shows promise for diagnosing infectious keratitis (IK), a leading cause of blindness. This review critically examines DL model accuracy against current standards to guide clinical use.
Area of Science:
- Ophthalmology
- Medical Artificial Intelligence
- Infectious Diseases
Background:
- Infectious keratitis (IK) is a major cause of global blindness, often due to diagnostic delays.
- Challenges in IK diagnosis include low culture yields, long turnaround times, and overlapping clinical features.
- Deep learning (DL) offers potential for automated medical diagnosis and improved healthcare efficiency.
Purpose of the Study:
- To systematically review and meta-analyze the diagnostic performance of DL models for infectious keratitis.
- To compare DL model accuracy against clinical experts and microbiological results (gold standard).
- To inform the clinical applicability and deployment of DL-assisted diagnostic tools for IK.
Main Methods:
- Systematic review and meta-analysis of studies applying DL models to diagnose suspected IK.
- Inclusion of studies on bacterial, fungal, protozoal, and viral keratitis.
- Comprehensive search of EMBASE, MEDLINE, and trial registries without language or date restrictions.
Main Results:
- Performance metrics of various DL models in diagnosing IK will be critically examined.
- Meta-analyses will be conducted on studies with sufficient outcome reporting similarities.
- Comparison of DL diagnostic accuracy with established clinical and microbiological benchmarks.
Conclusions:
- DL demonstrates potential as an assistive tool in diagnosing infectious keratitis.
- Further evaluation is needed to elucidate the accuracy and clinical utility of DL models.
- Findings will guide the integration of DL into clinical practice for improved IK management.
