In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 2 ( 2022-2-24), p. e0264140-
Abstract:
Early detection and classification of bone tumors in the proximal femur is crucial for their successful treatment. This study aimed to develop an artificial intelligence (AI) model to classify bone tumors in the proximal femur on plain radiographs. Methods Standard anteroposterior hip radiographs were obtained from a single tertiary referral center. A total of 538 femoral images were set for the AI model training, including 94 with malignant, 120 with benign, and 324 without tumors. The image data were pre-processed to be optimized for training of the deep learning model. The state-of-the-art convolutional neural network (CNN) algorithms were applied to pre-processed images to perform three-label classification (benign, malignant, or no tumor) on each femur. The performance of the CNN model was verified using fivefold cross-validation and was compared against that of four human doctors. Results The area under the receiver operating characteristic (AUROC) of the best performing CNN model for the three-label classification was 0.953 (95% confidence interval, 0.926–0.980). The diagnostic accuracy of the model (0.853) was significantly higher than that of the four doctors (0.794) (P = 0.001) and also that of each doctor individually (0.811, 0.796, 0.757, and 0.814, respectively) (P 〈 0.05). The mean sensitivity, specificity, precision, and F1 score of the CNN models were 0.822, 0.912, 0.829, and 0.822, respectively, whereas the mean values of four doctors were 0.751, 0.889, 0.762, and 0.797, respectively. Conclusions The AI-based model demonstrated high performance in classifying the presence of bone tumors in the proximal femur on plain radiographs. Our findings suggest that AI-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0264140
DOI:
10.1371/journal.pone.0264140.g001
DOI:
10.1371/journal.pone.0264140.g002
DOI:
10.1371/journal.pone.0264140.g003
DOI:
10.1371/journal.pone.0264140.g004
DOI:
10.1371/journal.pone.0264140.g005
DOI:
10.1371/journal.pone.0264140.g006
DOI:
10.1371/journal.pone.0264140.g007
DOI:
10.1371/journal.pone.0264140.t001
DOI:
10.1371/journal.pone.0264140.t002
DOI:
10.1371/journal.pone.0264140.t003
DOI:
10.1371/journal.pone.0264140.t004
DOI:
10.1371/journal.pone.0264140.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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