Article ; Online: Added value of an artificial intelligence solution for fracture detection in the radiologist's daily trauma emergencies workflow.
Diagnostic and interventional imaging
2022 Volume 103, Issue 12, Page(s) 594–600
Abstract: Purpose: The main objective of this study was to compare radiologists' performance without and with artificial intelligence (AI) assistance for the detection of bone fractures from trauma emergencies.: Materials and methods: Five hundred consecutive ... ...
| Abstract | Purpose: The main objective of this study was to compare radiologists' performance without and with artificial intelligence (AI) assistance for the detection of bone fractures from trauma emergencies. Materials and methods: Five hundred consecutive patients (232 women, 268 men) with a mean age of 37 ± 28 (SD) years (age range: 0.25-99 years) were retrospectively included. Three radiologists independently interpreted radiographs without then with AI assistance after a 1-month minimum washout period. The ground truth was determined by consensus reading between musculoskeletal radiologists and AI results. Patient-wise sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for fracture detection and reading time were compared between unassisted and AI-assisted readings of radiologists. Their performances were also assessed by receiver operating characteristic (ROC) curves. Results: AI improved the patient-wise sensitivity of radiologists for fracture detection by 20% (95% confidence interval [CI]: 14-26), P< 0.001) and their specificity by 0.6% (95% CI: -0.9-1.5; P = 0.47). It increased the PPV by 2.9% (95% CI: 0.4-5.4; P = 0.08) and the NPV by 10% (95% CI: 6.8-13.3; P < 0.001). Thanks to AI, the area under the ROC curve for fracture detection of readers increased respectively by 10.6%, 10.2% and 9.9%. Their mean reading time per patient decreased by respectively 10, 16 and 12 s (P < 0.001). Conclusions: AI-assisted radiologists work better and faster compared to unassisted radiologists. AI is of great aid to radiologists in daily trauma emergencies, and could reduce the cost of missed fractures. |
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| MeSH term(s) | Male ; Humans ; Female ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Aged ; Infant ; Child, Preschool ; Aged, 80 and over ; Artificial Intelligence ; Workflow ; Retrospective Studies ; Emergencies ; Radiologists ; Fractures, Bone |
| Language | English |
| Publishing date | 2022-06-29 |
| Publishing country | France |
| Document type | Journal Article |
| ZDB-ID | 2648283-6 |
| ISSN | 2211-5684 ; 2211-5684 |
| ISSN (online) | 2211-5684 |
| ISSN | 2211-5684 |
| DOI | 10.1016/j.diii.2022.06.004 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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