Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of surgical pathology in the abdominopelvic cavity: a systematic review. Issue 3 (6th March 2023)

• Record Type: Journal Article
• Authors: Fowler, George E; Blencowe, Natalie S; Hardacre, Conor; Callaway, Mark P; Smart, Neil J; Macefield, Rhiannon
• Is Part Of: BMJ open. Volume 13:Issue 3(2023)
• Publication Date: 2023-03-06
• Subjects: SURGERY -- Diagnostic radiology -- Adult surgery; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2022-064739
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: There is emerging use of artificial intelligence (AI) models to aid diagnostic imaging. This review examined and critically appraised the application of AI models to identify surgical pathology from radiological images of the abdominopelvic cavity, to identify current limitations and inform future research. Design: Systematic review. Data sources: Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) were performed. Date limitations (January 2012 to July 2021) were applied. Eligibility criteria: Primary research studies were considered for eligibility using the PIRT (participants, index test(s), reference standard and target condition) framework. Only publications in the English language were eligible for inclusion in the review. Data extraction and synthesis: Study characteristics, descriptions of AI models and outcomes assessing diagnostic performance were extracted by independent reviewers. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines. Risk of bias was assessed (Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)). Results: Fifteen retrospective studies were included. Studies were diverse in surgical specialty, the intention of the AI applications and the models used. AI training and test sets comprised a median of 130 (range: 5–2440) and 37 (range: 10–1045) patients, respectively. Diagnostic performance of models varied (range: 70%–95% sensitivity, 53%–98% specificity). Only four studies compared the AI model with human performance. Reporting of studies was unstandardised and often lacking in detail. Most studies (n=14) were judged as having overall high risk of bias with concerns regarding applicability. Conclusions: AI application in this field is diverse. Adherence to reporting guidelines is warranted. With finite healthcare resources, future endeavours may benefit from targeting areas where radiological expertise is in high demand to provide greater efficiency in clinical care. Translation to clinical practice and adoption of a multidisciplinary approach should be of high priority. PROSPERO registration number: CRD42021237249.