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.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. We therefore present a cost-efficient solution by designing a deep neural network to synthesize augmented reality EUS (AR-EUS) from conventional B-mode images. By using 4580 cases from 15 medical centers, we evaluate the performance of AR-EUS on breast cancer diagnosis. The quantitative metric and blind evaluation results show no significant difference between AR-EUS and real EUS in image authenticity and in clinical diagnosis. The performance of pocket-sized ultrasound in breast tumor diagnosis is also significantly improved after AR-EUS is equipped. These results highlight the potential of AR-EUS in clinical application.

ObjectiveTo evaluate the feasibility of axillary nerve (AN) visualization in healthy volunteers and the diagnostic value of AN injury via high‐resolution ultrasonography (HRUS).MethodsAN was examined by HRUS on both sides of 48 healthy volunteers and oriented the transducer according to three anatomical landmarks: quadrilateral space, anterior to subscapular muscle, and posterior to axillary artery. The maximum short‐axis diameter (SD) and cross‐sectional area (CSA) of AN were measured at different levels, and AN visibility was graded by using a five‐point scale. The patients suspected of having AN injury were assessed by HRUS, and the HRUS features of AN injury were observed.ResultsAN can be visualized on both sides in all volunteers. There was no significant difference in SD and CSA of AN at the three levels between the left and right sides or in SD between males and females. However, the CSA of males at different levels was slightly larger than those of females (P < .05). In most volunteers, AN visibility at different levels was excellent or good, and AN was best displayed anterior to subscapular muscle. Rank correlation analysis revealed that the degree of AN visibility had correlation with height, weight, and BMI. A total of 15 patients diagnosed with AN injury, 12 patients showed diffuse swelling or focal thickening in AN, and 3 patients showed AN discontinuity.ConclusionHRUS is able to reliably visualize AN, and it could be considered as the first choice for diagnosing AN injury.