Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.

BACKGROUND

Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size.

METHODS

We enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer.

RESULTS

The SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia.

CONCLUSION

We developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.