YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons

Abstract

Purpose: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification.

Methods: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1.

Results: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS.

Conclusion: In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.

Keywords: Artificial intelligence; Assist diagnosis; Fracture classification; Intertrochanteric fracture; Swin transformer; YOLOX.

Fig. 1

Schematic diagram of YOLOX-SwinT network structure. YOLOX: You Only Look Once X; SwinT: Swin Transformer; Conv2D: Convolution2D; CSPlayer: cross stage partial layer; ASFF: adaptively spatial feature fusion.

Fig. 2

The detection results of YOLOX-SwinT network subgroups. (A) The result of A1.2 subgroup; (B) The result of A1.3 subgroup; (C) The result of A2.2 subgroup; (D) The result of A2.3 subgroup; (E) The result of A3 subgroup. YOLOX-SwinT: You Only Look Once X-Swin Transformer

Fig. 3

Heatmaps of YOLOX and YOLOX-SwinT network. (A–E) The original image of A1.2-A3 subgroup, respectively; (A1-E1) Heatmaps of the YOLOX subgroup model; (A2-E2) Heatmaps of the YOLOX-SwinT subgroup model. YOLOX: You Only Look Once X; YOLOX-SwinT: You Only Look Once X-Swin Transformer.