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Congress: ECR24
Poster Number: C-19105
Type: EPOS Radiologist (scientific)
Authorblock: G. Herpe1, V. Rabeau2, P-A. Lentz2, S. Luzi2, A. Parpaleix3, M. Lederlin2; 1Poitiers/FR, 2Rennes/FR, 3paris/FR
Disclosures:
Guillaume Herpe: Advisory Board: INCEPTO-MEDICAL
Valentin Rabeau: Nothing to disclose
Pierre-Axel Lentz: Nothing to disclose
Stephanie Luzi: Nothing to disclose
Alexandre Parpaleix: CEO: MILVUE
Mathieu Lederlin: Nothing to disclose
Keywords: Emergency, Conventional radiography, Decision analysis, Economics
Purpose Chest radiography plays an essential role in the diagnostic framework of emergency departments (EDs), providing critical insights into a wide range of medical conditions. However, the task of interpreting these images poses significant challenges for radiology departments, primarily due to the increasing volume of radiographs and the consequent workload. Additionally, the necessity for specialized expertise and stringent quality control measures to minimize diagnostic errors becomes particularly acute during off-hours, when access to qualified radiologists may be limited. This shortfall in...
Read more Methods and materials Study Design and Data CollectionThis study was conducted retrospectively, focusing on the evaluation of Milvue Suite v2.1 (Milvue, Paris, France), a CE-marked artificial intelligence (AI) algorithm. The algorithm is intended to detect pathologies on frontal and lateral chest radiographs, irrespective of the patient's positioning, among which the 5 following common findings evaluated in this study: pneumothorax, fracture, pleural effusion, non-nodular pulmonary opacity, and pulmonary nodule.Ground Truth EstablishmentA total of 202 consecutive radiograph sets from outpatients who underwent chest radiography at...
Read more Results Patient Demographics and Radiograph CharacteristicsIn this study, radiograph sets from 119 patients were analyzed, with a median age of 38 years (interquartile range [Q1-Q3]: 26-56.5 years; range: 16-89 years). Of these patients, 52 (43.7%) were female. The radiographs were evaluated for the presence of abnormalities, with 29 (24.4%) of the sets identified as abnormal. Among these abnormal sets, 10 (37.9%) were classified as critical in nature, and 17 (58.6%) contained a non-nodular pulmonary opacity. [fig 1] Overall Performance of the AI AlgorithmThe AI...
Read more Conclusion A deep learning AI-based application, Milvue Suite, can identify multiple relevant and critical findings on emergency chest radiographs, with a high level of accuracy compared to expert thoracic radiologists, mostly when identifying critical conditions such as pneumothorax and fractures. Overall, the high negative predictive value underscores the algorithm's potential as a reliable tool for the triage of chest radiographs in emergency department settings.However, it is important to acknowledge the limitations of this study, including the relatively small dataset size, which...
Read more References Ahluwalia M, Abdalla M, Sanayei J, et al. The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups. Radiology: Artificial Intelligence. 2023;5(5):e220270. doi:10.1148/ryai.220270Ahn JS, Ebrahimian S, McDermott S, et al. Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency. JAMA Netw Open. 2022;5(8):e2229289. doi:10.1001/jamanetworkopen.2022.29289Al aseri Z. Accuracy of chest radiograph interpretation by emergency physicians. Emerg Radiol. 2009;16(2):111-114. doi:10.1007/s10140-008-0763-9Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G. Automated Triaging of Adult...
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