Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist
| Autor: | Nils Hendrix, Ward Hendrix, Kees van Dijke, Bas Maresch, Mario Maas, Stijn Bollen, Alexander Scholtens, Milko de Jonge, Lee-Ling Sharon Ong, Bram van Ginneken, Matthieu Rutten |
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| Přispěvatelé: | Radiology and nuclear medicine, Radiology and Nuclear Medicine, AMS - Rehabilitation & Development, AMS - Sports, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Fractures
bone Artificial intelligence All institutes and research themes of the Radboud University Medical Center Scaphoid bone Radiology Nuclear Medicine and imaging General Medicine Clinical decision support system Fractures bone Multicenter study Women's cancers Radboud Institute for Health Sciences [Radboudumc 17] Rare cancers Radboud Institute for Health Sciences [Radboudumc 9] |
| Zdroj: | Hendrix, N, Hendrix, W, van Dijke, K, Maresch, B, Maas, M, Bollen, S, Scholtens, A, de Jonge, M, Ong, L-L S, van Ginneken, B & Rutten, M 2022, ' Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist ', European Radiology . https://doi.org/10.1007/s00330-022-09205-4 European Radiology. Springer Verlag European Radiology, 33, 1575-1588 European Radiology, 33, 3, pp. 1575-1588 European radiology. Springer Verlag |
| ISSN: | 0938-7994 |
| DOI: | 10.1007/s00330-022-09205-4 |
| Popis: | Objectives To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs. Methods Four datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen’s kappa coefficient (κ), fracture localization precision, and reading time. Results The algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists’ mean], p ≥ .05). AI assistance improved five out of ten pairs of inter-observer Cohen’s κ agreements (p < .05) and reduced reading time in four radiologists (p < .001), but did not improve other metrics in the majority of radiologists (p ≥ .05). Conclusions The AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time. Key Points • An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists. |
| Databáze: | OpenAIRE |
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