Separate regulatory variants at GDF5-UQCC1 underlie common knee osteoarthritis risk and developmental dysplasia of the hip

Objective Closed reduction (CR) is a noninvasive treatment for developmental dysplasia of the hip (DDH), and this treatment is confirmed intraoperatively. This study aimed to develop a preoperative estimation model of the probability of requiring open reduction (OR) for DDH. Methods The study design was cross-sectional by screening all patients younger than 2 years who had attempted CR between October 2012 and July 2016 by a single surgeon. Potential diagnostic determinants were sex, age, side, bilaterality, International Hip Dysplasia Institute (IHDI) grade, and acetabular index (AI). An intraoperative arthrogram was the reference standard. A logistic regression equation was built from a reduced model. Bootstrapping was performed for internal validity. Results A total of 164 hips in 104 patients who met the inclusion criteria were analysed. The prevalence of CR was 72.2%. Independent factors for OR were older age, higher IHDI grade, and lower AI. The probability of OR = 1/[1 + exp − (−2.753 + 0.112 × age (months) + 1.965 × IHDI grade III (0 or 1) + 3.515 × IHDI grade IV (0 or 1) − 0.058 × AI (degrees)]. The area under the curve was 0.79. Conclusion This equation is an objective tool that can be used to estimate the requirement for OR.

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Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening

Diagnostics ◽

10.3390/diagnostics11071174 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1174

Author(s):

Si-Wook Lee ◽

Hee-Uk Ye ◽

Kyung-Jae Lee ◽

Woo-Young Jang ◽

Jong-Ha Lee ◽

...

Keyword(s):

Deep Learning ◽

Intraclass Correlation ◽

Alpha Angle ◽

Learning Model ◽

Developmental Dysplasia ◽

Human Observer ◽

Intraobserver Variability ◽

Cad System ◽

Deep Learning Model ◽

Dysplasia Of The Hip

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.

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