Dataset Information

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation.

ABSTRACT:

Purpose

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.

SUBMITTER: Park CW

PROVIDER: S-EPMC8870496 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation.

Park Chan-Woo CW Oh Seong-Je SJ Kim Kyung-Su KS Jang Min-Chang MC Kim Il Su IS Lee Young-Keun YK Chung Myung Jin MJ Cho Baek Hwan BH Seo Sung-Wook SW

PloS one 20220224 2

<h4>Purpose</h4>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.<h4>Methods</h4>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 witho ...[more]

PMID: 35202410

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Dataset Information

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation.

Purpose

Methods

Results

Conclusions

Publications

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation.

Similar Datasets

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