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Objectives: Accurate detection of carotid plaque using ultrasound (US) is essential for preventing stroke. However, the diagnostic performance of junior radiologists (with approximately 1 year of experience in carotid US evaluation) is relatively poor. We thus aim to develop a deep learning (DL) model based on US videos to improve junior radiologists' performance in plaque detection., Methods: This multicenter prospective study was conducted at five hospitals. CaroNet-Dynamic automatically detected carotid plaque from carotid transverse US videos allowing clinical detection. Model performance was evaluated using expert annotations (with more than 10 years of experience in carotid US evaluation) as the ground truth. Model robustness was investigated on different plaque characteristics and US scanning systems. Furthermore, its clinical applicability was evaluated by comparing the junior radiologists' diagnoses with and without DL-model assistance., Results: A total of 1647 videos from 825 patients were evaluated. The DL model yielded high performance with sensitivities of 87.03% and 94.17%, specificities of 82.07% and 74.04%, and areas under the receiver operating characteristic curve of 0.845 and 0.841 on the internal and multicenter external test sets, respectively. Moreover, no significant difference in performance was noted among different plaque characteristics and scanning systems. Using the DL model, the performance of the junior radiologists improved significantly, especially in terms of sensitivity (largest increase from 46.3 to 94.44%)., Conclusions: The DL model based on US videos corresponding to real examinations showed robust performance for plaque detection and significantly improved the diagnostic performance of junior radiologists., Key Points: • The deep learning model based on US videos conforming to real examinations showed robust performance for plaque detection. • Computer-aided diagnosis can significantly improve the diagnostic performance of junior radiologists in clinical practice., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

This study was aimed at developing a first-trimester standard plane detection (FTSPD) system that can automatically locate nine standard planes in ultrasound videos and investigating its utility in clinical practice. The FTSPD system, based on the YOLOv3 network, was developed to detect structures and evaluate the quality of plane images by using a pre-defined scoring system. A total of 220 videos from two different ultrasound scanners were collected to compare detection performance between our FTSPD system and sonographers with different levels of experience. The quality of the detected standard planes was quantitatively rated by an expert according to a scoring protocol. Kolmogorov–Smirnov analysis was used to compare the distributions of scores across all nine standard planes. The expert-rated scores indicated that the quality of the standard planes detected by the FTSPD system was on par with that of the planes detected by senior sonographers. There were no significant differences in the distributions of the scores across all nine standard planes. The FTSPD system performed significantly better than junior sonographers in five standard plane types. The results of this study suggest that our FTSPD system has significant potential for detecting standard planes in first-trimester ultrasound screening, which may help to improve the accuracy of fetal ultrasound screening and facilitate early diagnosis of abnormalities. The quality of the standard planes selected by junior sonographers can be significantly improved with the assistance of our FTSPD system. [ABSTRACT FROM AUTHOR]

• An effective test-time bi-directional adaptation strategy is proposed to seek robust segmentation. • A window-based order statistics alignment module is presented to adapt appearance-agnostic test images to existing learned models. • An augmented self-supervised learning is developed to adapt the segmentation model to images with unknown appearance shifts. • The method generalizes well across multi-vendor/center datasets. Deep learning models often suffer from performance degradations when deployed in real clinical environments due to appearance shifts between training and testing images. Most extant methods use training-time adaptation , which almost require target domain samples in the training phase. However, these solutions are limited by the training process and cannot guarantee the accurate prediction of test samples with unforeseen appearance shifts. Further, it is impractical to collect target samples in advance. In this paper, we provide a general method of making existing segmentation models robust to samples with unknown appearance shifts when deployed in daily clinical practice. Our proposed test-time bi-directional adaptation framework combines two complementary strategies. First, our image-to-model (I2M) adaptation strategy adapts appearance-agnostic test images to the learned segmentation model using a novel plug-and-play statistical alignment style transfer module during testing. Second, our model-to-image (M2I) adaptation strategy adapts the learned segmentation model to test images with unknown appearance shifts. This strategy applies an augmented self-supervised learning module to fine-tune the learned model with proxy labels that it generates. This innovative procedure can be adaptively constrained using our novel proxy consistency criterion. This complementary I2M and M2I framework demonstrably achieves robust segmentation against unknown appearance shifts using existing deep-learning models. Extensive experiments on 10 datasets containing fetal ultrasound, chest X-ray, and retinal fundus images demonstrate that our proposed method achieves promising robustness and efficiency in segmenting images with unknown appearance shifts. To address the appearance shift problem in clinically acquired medical images, we provide robust segmentation by using two complementary strategies. Our solution is general and amenable for deployment in clinical settings. [ABSTRACT FROM AUTHOR]