Using artificial intelligence for bladder cancer detection during cystoscopy and its impact on clinical outcomes: a protocol for a systematic review and meta-analysis

IntroductionCystoscopy has revolutionised the process of diagnosing bladder cancer leading to better categorisation of risk levels and more precise treatment plans. Nonetheless, concerns arise about the lack of uniformity among observers in predicting tumour stage and grade. To address these concern...

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Bibliographic Details
Published in:BMJ open 2024-10, Vol.14 (10), p.e089125
Main Authors: Baana, Mohamed, Arkwazi, Murtada, Zhao, Yi, Ofagbor, Ojone, Bhardwaj, Gaurika, Lami, Mariam, Bolton, Eva, Heer, Rakesh
Format: Article
Language:English
Subjects:
Artificial Intelligence
Automation
Bladder cancer
Clinical outcomes
Cystoscopy - methods
Deep learning
Demographics
Diagnostic Imaging
Humans
Medical diagnosis
Medical imaging
Medical research
Meta-analysis
Meta-Analysis as Topic
Research Design
Systematic review
Systematic Reviews as Topic
Urinary Bladder Neoplasms - diagnosis
Urology
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Description
Summary:IntroductionCystoscopy has revolutionised the process of diagnosing bladder cancer leading to better categorisation of risk levels and more precise treatment plans. Nonetheless, concerns arise about the lack of uniformity among observers in predicting tumour stage and grade. To address these concerns, artificial intelligence (AI) is being incorporated into clinical settings to aid in the analysis of diagnostic and therapeutic images. The subsequent report outlines a systematic review and meta-analysis protocol aimed at evaluating the effectiveness of AI in predicting bladder cancer based on cystoscopic images.Methods and analysisOur systematic search will use databases including PubMed, MEDLINE, Embase and Cochrane. The articles published between May 2015 and April 2024 will be eligible for inclusion. For articles to be considered, they must employ AI for analysis of cystoscopic images to identify bladder cancer, present original data and be written in English. The protocol adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol 2015 checklist. Quality and bias risk across chosen studies will be evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 score.Ethics and disseminationEthical clearance will not be necessary for conducting this systematic review since results will be disseminated through peer-reviewed publications and presentations at both national and international conferences.PROSPERO registration numberCRD42024528345.
ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2024-089125