Purpose This study aimed to investigate the detection rate of developmental dysplasia of the hip (DDH) by ultrasound. To obtain the distribution characteristics of the different types (I–IV) and the role of ultrasound in the evaluation of infants with DDH (type IIc and above) receiving conservative treatment. Methods A retrospective analysis was performed. The chi‐square test was used for comparisons between count‐data groups. Two‐sided tests were used for all analyses. The results of ultrasound follow‐up after conservative treatment are described. Results Among the 48 666 infants examined, the detection rates were as follows: type I, 95.42%; IIa, 3.18%; IIb, 0.91%; IIc, 0.22%; D, 0.01%; III, 0.14%; and IV, 0.12%. There were 4456 hips with IIa and above, more left (54.65%) than right (45.35%) hips, and more females (82.60%) than males (17.40%). The detection rate of type IIa and above was 4.58%, and that of type IIb and above was 1.40%. After the treatment, the α value increased, and β value decreased. The cure rate of the less‐than‐42‐days group was higher than that of the other groups. Conclusion The ultrasound detection rate of DDH is high. DDH was more likely to occur on the left side and in females. It is recommended that the infants should be treated within 42 days.

Purpose An automatic evaluation technology based on artificial intelligence and three‐dimensional ultrasonography (3D US) is proposed for hip US inspection plane selection. This study aimed to evaluate the consistency of the α angle as measured using 3D US to select the section plane and two‐dimensional ultrasonography (2D US) to manually select the Graf image, as well as to explore the feasibility of diagnosing developmental dysplasia of the hip (DDH) using 3D US and reconstruction technology. Methods A total of 216 infant hips were included and assessed by doctors using 3D US layer‐by‐layer. The researchers used a computer to identify the coronal images that met the Graf standard and then compared the αX values obtained with the αG values measured artificially by 2D US. Results Compared with 2D US, 3D US more clearly showed the relative positions of the ilium, ischia, and pubis. The measured α value of the optimal section obtained by 3D US showed good agreement with the measured α value of the standard Graf section. Conclusion The artificial intelligence and 3D US‐based automatic evaluation technology for section selection and inspection for DDH showed good agreement with the Graf method based on standard sections.

Background: Graf’s method is currently the most commonly used ultrasound-based technique for the diagnosis of developmental dysplasia of the hip (DDH). However, the efficiency and accuracy of diagnosis are highly affected by the sonographers’ qualification and the time and effort expended, which has a significant intra- and inter-observer variability. Methods: Aiming to minimize the manual intervention in the diagnosis process, we developed a deep learning-based computer-aided framework for the DDH diagnosis, which can perform fully automated standard plane detection and angle measurement for Graf type I and type II hips. The proposed framework is composed of three modules: an anatomical structure detection module, a standard plane scoring module, and an angle measurement module. This framework can be applied to two common clinical scenarios. The first is the static mode, measurement and classification are performed directly based on the given standard plane. The second is the dynamic mode, where a standard plane from ultrasound video is first determined, and measurement and classification are then completed. To the best of our knowledge, our proposed framework is the first CAD method that can automatically perform the entire measurement process of Graf’s method. Results: In our experiments, 1051 US images and 289 US videos of Graf type I and type II hips were used to evaluate the performance of the proposed framework. In static mode, the mean absolute error of α, β angles are 1.71° and 2.40°, and the classification accuracy is 94.71%. In dynamic mode, the mean absolute error of α, β angles are 1.97° and 2.53°, the classification accuracy is 89.51%, and the running speed is 31 fps. Conclusions: Experimental results demonstrate that our fully automated framework can accurately perform standard plane detection and angle measurement of an infant’s hip at a fast speed, showing great potential for clinical application.