Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.

This study evaluated the correction rates of idiopathic genu valgum or varum after percutaneous epiphysiodesis using transphyseal screws (PETS) and analyzed the affecting factors. A total of 35 children without underlying diseases were enrolled containing 64 physes (44 distal femoral (DT), 20 proximal tibial (PT)). Anatomic tibiofemoral angle (aTFA) and the mechanical axis deviation (MAD) were taken from teleroentgenograms before PETS surgery and screw removal. The correction rates of the valgus and varus deformities for patients treated with PETS were 1.146°/month and 0.639°/month using aTFA while using MAD showed rates of 4.884%/month and 3.094%/month. After aTFA (p < 0.001) and MAD (p < 0.001) analyses, the correction rate of DF was significantly faster than that of PT. Under multivariable analysis, the aTFA correction rate was significantly faster in younger patients (p < 0.001), in males (p < 0.001), in patients with lower weights (p < 0.001), and in the group that was screwed at DF (p < 0.001). Meanwhile, the MAD correction rate was significantly faster in patients with lower heights (p = 0.003). PETS is an effective treatment method for valgus and varus deformities in growing children and clinical characters should be considered to estimate the correction rate.

We thank Dr. Sadettin Ciftci for his comment on the key point issues in measuring the alpha and beta angle with Graf method. We appreciated his feedback