157 Diagnostic Test Accuracy of Artificial Intelligence Models Used in Cross-Sectional Radiological Imaging of Surgical Pathology in the Abdominopelvic Cavity: A Systematic Review
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British Journal of Surgery
© 2022, Oxford University Press
Medical imaging is important for diagnostic, prognostic, and management decisions. It is reliant on an increasingly limited number of interpreters. A developing interest has explored how artificial intelligence (AI) research in medical imaging can support clinicians and provide greater efficiency in clinical care. Reviews exist for thoracic and endoscopic imaging, but one is lacking for the abdominopelvic cavity. This could benefit several specialities which use this modality of imaging to guide their clinical decision making. This systematic review examines and critically appraises the application of AI models to identify surgical pathology from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) to identify relevant studies were performed, adhering to the PRISMA-DTA guidelines. Study characteristics and outcomes assessing diagnostic performance were extracted. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines.10 retrospective studies were included, comprising 3,096 and 1,432 patients for AI training and test sets, respectively. There was diversity in the speciality, intention of the AI applications and the reporting, which was unstandardised. Diagnostic performance of models varied (range: 70–95% sensitivity, 73.7%-98% specificity). Only one study used a comparator, in which AI (AUC=0.920) outperformed both senior and junior radiologists (AUC=0.791 and 0.780, respectively).AI application in this field is diverse and adherence to new and developing reporting guidelines is warranted. With finite healthcare resources and funding, future endeavours may benefit from prioritising clinical need, rather than scientific inquiry.
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