Automated measurement of hip–knee–ankle angle on the unilateral lower limb X-rays using deep learning

Significant inherent extra-articular varus angulation is associated with abnormal postoperative hip–knee–ankle (HKA) angle. At present, HKA is manually measured by orthopedic surgeons and it increases the doctors’ workload. To automatically determine HKA, a deep learning-based automated method for measuring HKA on the unilateral lower limb X-rays was developed and validated. This study retrospectively selected 398 double lower limbs X-rays during 2018 and 2020 from Jilin University Second Hospital. The images (n = 398) were cropped into unilateral lower limb images (n = 796). The deep neural network was used to segment the head of hip, the knee, and the ankle in the same image, respectively. Then, the mean square error of distance between each internal point of each organ and the organ’s boundary was calculated. The point with the minimum mean square error was set as the central point of the organ. HKA was determined using the coordinates of three organs’ central points according to the law of cosines. In a quantitative analysis, HKA was measured manually by three orthopedic surgeons with a high consistency (176.90 ° ± 12.18°, 176.95 ° ± 12.23°, 176.87 ° ± 12.25°) as evidenced by the Kandall’s W of 0.999 (p < 0.001). Of note, the average measured HKA by them (176.90 ° ± 12.22°) served as the ground truth. The automatically measured HKA by the proposed method (176.41 ° ± 12.08°) was close to the ground truth, showing no significant difference. In addition, intraclass correlation coefficient (ICC) between them is 0.999 (p < 0.001). The average of difference between prediction and ground truth is 0.49°. The proposed method indicates a high feasibility and reliability in clinical practice.