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)...

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Published in:	Acta orthopaedica Vol. 89; no. 4; pp. 468 - 473
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	England Taylor & Francis 04.07.2018 Medical Journals Sweden
Subjects:	Adult Aged Aged, 80 and over Algorithms Area Under Curve Arthrography Deep Learning Female Humans Male Middle Aged Shoulder Fractures - classification Shoulder Fractures - diagnostic imaging Young Adult
ISSN:	1745-3674, 1745-3682, 1745-3682
Online Access:	Get full text
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Abstract	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.
AbstractList	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. 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. 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.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.
Author	Lee, Ji Whan Kim, Youngjun Chung, Seok Won Kim, Joon Yub Kwon, Jieun Kim, Na Ra Yoon, Jong Pil Noh, Young-Min Lee, Hyo-Jin Oh, Kyung-Soo Han, Seung Seog Moon, Sung Hoon
Author_xml	– sequence: 1 givenname: Seok Won surname: Chung fullname: Chung, Seok Won organization: Department of Orthopaedic Surgery – sequence: 2 givenname: Seung Seog surname: Han fullname: Han, Seung Seog organization: Department of Dermatology – sequence: 3 givenname: Ji Whan surname: Lee fullname: Lee, Ji Whan organization: Department of Orthopaedic Surgery – sequence: 4 givenname: Kyung-Soo surname: Oh fullname: Oh, Kyung-Soo organization: Department of Orthopaedic Surgery – sequence: 5 givenname: Na Ra surname: Kim fullname: Kim, Na Ra organization: Department of Radiology, Konkuk University School of Medicine – sequence: 6 givenname: Jong Pil surname: Yoon fullname: Yoon, Jong Pil organization: Department of Orthopaedic Surgery, Kyungpook National University College of Medicine – sequence: 7 givenname: Joon Yub surname: Kim fullname: Kim, Joon Yub organization: Department of Orthopaedic Surgery, Myungji Hospital – sequence: 8 givenname: Sung Hoon surname: Moon fullname: Moon, Sung Hoon organization: Department of Orthopaedic Surgery, Kangwon National University College of Medicine – sequence: 9 givenname: Jieun surname: Kwon fullname: Kwon, Jieun organization: Department of Othopaedic Surgery, National Police Hospital – sequence: 10 givenname: Hyo-Jin surname: Lee fullname: Lee, Hyo-Jin organization: Department of Orthopaedic Surgery, Catholic University College of Medicine, Seoul, St Mary's Hospital – sequence: 11 givenname: Young-Min surname: Noh fullname: Noh, Young-Min organization: Department of Orthopaedic Surgery, Dong-A University College of Medicine – sequence: 12 givenname: Youngjun surname: Kim fullname: Kim, Youngjun email: junekim@kist.re.kr organization: Center for Bionics, Korea Institute of Science and Technology
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29577791$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1186/1749-799X-6-38 10.1109/TPAMI.2012.277 10.1001/jama.2016.17216 10.1148/radiol.2017162326 10.1038/nature21056 10.2106/00004623-197052060-00001 10.1080/17453674.2017.1344459 10.1016/j.media.2016.07.007 10.1038/nature14539 10.1109/TPAMI.2013.50 10.3121/cmr.2009.779 10.1007/s11263-015-0816-y 10.1016/j.media.2012.02.005
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SubjectTerms	Adult Aged Aged, 80 and over Algorithms Area Under Curve Arthrography Deep Learning Female Humans Male Middle Aged Shoulder Fractures - classification Shoulder Fractures - diagnostic imaging Young Adult
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Title	Automated detection and classification of the proximal humerus fracture by using deep learning algorithm
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