Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.
Background and purpose — We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods — 1,891 images (1 image per person) of normal shoulders (n = 515)...
| Publicado en: | Acta Orthopaedica Vol. 89; no. 4; pp. 468 - 474 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Medical Journals Sweden AB
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130896229&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130896229 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17453674 1BVL jtl: Acta Orthopaedica issn: 17453674 maglogo: N pubinfo: dt: Aug2018 vid: 89 iid: 4 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 130896229 130896229 130896229 10.1080/17453674.2018.1453714 130896229 ppf: 468 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. aug: au: Chung, Seok Won Han, Seung Seog Lee, Ji Whan Oh, Kyung-Soo Kim, Na Ra Yoon, Jong Pil Kim, Joon Yub Moon, Sung Hoon Kwon, Jieun Lee, Hyo-Jin Noh, Young-Min Kim, Youngjun affil: Department of Orthopaedic Surgery sug: subj: Humeral Fractures Radiography Humeral Fractures Classification Artificial Intelligence Algorithms Automation Neural Networks (Computer) Sensitivity and Specificity Human Surgeons Shoulder Radiography ROC Curve Validity Youden's J Statistic Physicians, Family Orthopedic Surgery ab: Background and purpose — We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods — 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated. Results — The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65-86% top-1 accuracy, 0.90-0.98 AUC, 0.88/0.83-0.97/0.94 sensitivity/specificity, and 0.71-0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures. Interpretation — The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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