Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs

Objective To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs. Methods This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a de...

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Veröffentlicht in:European radiology 2023-06, Vol.33 (6), p.3974-3983
Hauptverfasser: Cohen, Mathieu, Puntonet, Julien, Sanchez, Julien, Kierszbaum, Elliott, Crema, Michel, Soyer, Philippe, Dion, Elisabeth
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container_end_page 3983
container_issue 6
container_start_page 3974
container_title European radiology
container_volume 33
creator Cohen, Mathieu
Puntonet, Julien
Sanchez, Julien
Kierszbaum, Elliott
Crema, Michel
Soyer, Philippe
Dion, Elisabeth
description Objective To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs. Methods This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) Results A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) ( p < 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) ( p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR ( p < 0.001) and a lower specificity (92%; 95% CI: 89–95%) ( p < 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR). Conclusions Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances. Key Points • Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice. • Performance of artificial intelligence greatly differs depending on the anatomical area. • Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.
doi_str_mv 10.1007/s00330-022-09349-3
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Methods This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) Results A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) ( p &lt; 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) ( p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR ( p &lt; 0.001) and a lower specificity (92%; 95% CI: 89–95%) ( p &lt; 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR). Conclusions Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances. Key Points • Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice. • Performance of artificial intelligence greatly differs depending on the anatomical area. • Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-022-09349-3</identifier><identifier>PMID: 36515712</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial Intelligence ; Bones ; Diagnostic Radiology ; Fractures ; Fractures, Bone - diagnostic imaging ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Medicine ; Medicine &amp; Public Health ; Musculoskeletal ; Neural networks ; Neuroradiology ; Radiographs ; Radiography ; Radiologists ; Radiology ; Retrospective Studies ; Scaphoid Bone ; Sensitivity ; Ultrasound ; Wrist ; Wrist Fractures ; Wrist Injuries - diagnostic imaging</subject><ispartof>European radiology, 2023-06, Vol.33 (6), p.3974-3983</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2022. 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Methods This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) Results A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) ( p &lt; 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) ( p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR ( p &lt; 0.001) and a lower specificity (92%; 95% CI: 89–95%) ( p &lt; 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR). Conclusions Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances. 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Methods This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) Results A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) ( p &lt; 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) ( p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR ( p &lt; 0.001) and a lower specificity (92%; 95% CI: 89–95%) ( p &lt; 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR). Conclusions Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances. Key Points • Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice. • Performance of artificial intelligence greatly differs depending on the anatomical area. • Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>36515712</pmid><doi>10.1007/s00330-022-09349-3</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6566-7397</orcidid></addata></record>
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subjects Algorithms
Artificial Intelligence
Bones
Diagnostic Radiology
Fractures
Fractures, Bone - diagnostic imaging
Humans
Imaging
Internal Medicine
Interventional Radiology
Medicine
Medicine & Public Health
Musculoskeletal
Neural networks
Neuroradiology
Radiographs
Radiography
Radiologists
Radiology
Retrospective Studies
Scaphoid Bone
Sensitivity
Ultrasound
Wrist
Wrist Fractures
Wrist Injuries - diagnostic imaging
title Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs
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