Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

Acta Orthop. 2018 Aug;89(4):468-473. doi: 10.1080/17453674.2018.1453714. Epub 2018 Mar 26.


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.

Publication types

  • Evaluation Study
  • Multicenter Study
  • Video-Audio Media

MeSH terms

  • 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

Grants and funding

This work was supported by Konkuk University in 2017 and the KIST Institutional program (2E27990)