Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis

Article Article scientifique Méta-analyse Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis eng 2024-01-13 2024-02-28 author Lopez Melia Manel 0009-0004-5195-2543 University Centre of Legal Medicine Lausanne-Geneva, Geneva 1206, Switzerland.,University Hospital and University of Geneva, Geneva 1205, Switzerland. 402306 Institut de médecine légale (COLEG) author Magnin Virginie University Centre of Legal Medicine Lausanne-Geneva, Geneva 1206, Switzerland.,University Hospital and University of Geneva, Geneva 1205, Switzerland.,University Hospital and University of Lausanne, Lausanne 1005, Switzerland. author Marchand-Maillet Stéphane 0000-0002-4875-6101 Department of Computer Science, Viper Group, University of Geneva, Carouge 1227, Switzerland. 518563 Département d'informatique (DINFO) author Grabherr Silke University Centre of Legal Medicine Lausanne-Geneva, Geneva 1206, Switzerland.,University Hospital and University of Geneva, Geneva 1205, Switzerland.,University Hospital and University of Lausanne, Lausanne 1005, Switzerland. 746290 Institut de médecine légale (COLEG) 535-543 10.1093/bjr/tqae014 38323515 0007-1285 PMC11027249 https://academic.oup.com/bjr/article/97/1155/535/7529222 Objectives: To review studies on deep learning (DL) models for classification, detection, and segmentation of rib fractures in CT data, to determine their risk of bias (ROB), and to analyse the performance of acute rib fracture detection models. Methods: Research articles written in English were retrieved from PubMed, Embase, and Web of Science in April 2023. A study was only included if a DL model was used to classify, detect, or segment rib fractures, and only if the model was trained with CT data from humans. For the ROB assessment, the Quality Assessment of Diagnostic Accuracy Studies tool was used. The performance of acute rib fracture detection models was meta-analysed with forest plots. Results: A total of 27 studies were selected. About 75% of the studies have ROB by not reporting the patient selection criteria, including control patients or using 5-mm slice thickness CT scans. The sensitivity, precision, and F1-score of the subgroup of low ROB studies were 89.60% (95%CI, 86.31%-92.90%), 84.89% (95%CI, 81.59%-88.18%), and 86.66% (95%CI, 84.62%-88.71%), respectively. The ROB subgroup differences test for the F1-score led to a p-value below 0.1. Conclusion: ROB in studies mostly stems from an inappropriate patient and data selection. The studies with low ROB have better F1-score in acute rib fracture detection using DL models. Advances in knowledge: This systematic review will be a reference to the taxonomy of the current status of rib fracture detection with DL models, and upcoming studies will benefit from our data extraction, our ROB assessment, and our meta-analysis. CT Computed tomography Deep learning Rib fracture Deep Learning Humans Rib Fractures / diagnostic imaging Sensitivity and Specificity Tomography, X-Ray Computed / methods Dewey 614.1 Dewey 025.063 13 Centre universitaire d'informatique 0D3 67 Institut de médecine légale 22A2 false British journal of radiology 97 1155 4e3a37a4-3d2d-4d3e-93ab-8dd5f954a884 Article (Published version) Public tqae014.pdf 824862 application/pdf CC BY-4.0 9d871953862ef3994bb51009932f56665e4580aa9b18297e38089f93f7e649b1 c4b8f8a4-d777-4b7b-a3f5-666098522ebb Appendix Public tqae014_supplementary_data.docx 483655 application/vnd.openxmlformats-officedocument.wordprocessingml.document c81422c278ce95fcf178a61257494541acc35b80de7967014035b258b8de7ac8 openAccess Centres et instituts / Centre universitaire d'informatique Faculté de médecine / Section de médecine clinique / Département de santé et médecine communautaires / Institut de médecine légale

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Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis	British journal of radiology			2024	34	443

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