Carpal Tunnel Syndrome (CTS), the predominant type of peripheral entrapment neuropathy, necessitates timely and precise diagnosis for optimal treatment. While ultrasound (US) has emerged as a non-invasive diagnostic modality, existing computer-aided methods largely rely on static frames or unimodal features, failing to capture the temporal dynamics and multidimensional cues integral to clinical assessment. To address these, we present a panoramic diagnostic system for CTS that harnesses the full potential of US videos. It seamlessly consolidates median nerve segmentation, multi-dimensional biometric measurement, and multimodal CTS classification within a unified pipeline, transforming conventional diagnosis into a comprehensive digital solution. Specifically, we develop a Mamba-based video segmentation model with temporal compression and spectral gated enhancement to enable efficient, high-fidelity delineation and measurement. Building upon this, we propose a Spatiotemporal and Multimodal Processing (STAMP) framework that synergistically integrates video dynamics, anatomical measurements, and clinical covariates through bidirectional metadata-visual interactions and temporal contextualization. This approach mirrors the clinical reasoning process and provides clinically interpretable diagnostic results. Experimental results demonstrate that the proposed system outperforms existing automatic segmentation methods across multiple metrics (Dice=87.18%) and achieves performance comparable to manually initialized ones. Moreover, our system not only surpasses both frame-based and video-based approaches (F1-score=97.44%), but also exceeds that of junior radiologists and rivals senior experts.

Ultrasound (US) imaging plays a crucial role in diagnosing heart and pelvic diseases, where sonographers tend to evaluate dynamic motion and structure. However, the scarcity of US videos for rare cases limitstraining opportunities for novice sonographers and deep learning models, hindering detection rates and clinical diagnostic applications. US video synthesis is a promising solution to this issue. Nevertheless, accurately imitating the intricate motion of the anatomy while preserving image fidelity presents asignificant challenge. In this work, we propose OnUVS, a novel online feature-decoupling framework for high-fidelity US video synthesis. First, to simulate realistic motion, we incorporate keypoints into anatomical learning through a weakly supervised training approach, which enhances motion representation and minimizes the need for fully annotated data. Second, we implement a dual-decoder generator that effectively balances content and textural features of generated frames, significantly enhancing the image fidelity of US videos. Third, a multi-scale discriminator further refines the sharpness and fine details, ensuring high-fidelity video synthesis. Fourth, an online learning strategy is designed to smooth coherence between frames by constraining the keypoint trajectories during inference. Validation on echocardiographic and pelvic floor US datasets demonstrates that OnUVS outperforms existing methods, achieving a 22.08% improvement in motion consistency (FVD) and 25.04% in image fidelity (FID).

Cognitive support in aviation operations can help reduce cognitive workload and improve flight safety. Existing data-driven models, typically trained for specific aircraft types, often lack generalization capabilities and cannot be directly applied to other aircrafts. The multimodal large language models (MLLMs) have shown significant value in various applications without additional training. Motivated by the success of MLLMs, we explore and develop a proactive aviation cognitive support framework (PACS) based on MLLMs to proactively assist pilots in anomaly perception and decision-making during aviation emergencies. We propose a localization-augmented anomaly perception method to address the limitations of MLLMs in recognizing small-sized warning message text. To improve the accuracy and efficiency in decision-making, we design a hierarchically structured aviation knowledge base and a "Retrieval-Selection Generation" strategy to generate accurate operational instructions. The experimental results demonstrate high accuracy in anomaly perception (87.51%) and decision-making (93.04%), while reducing token count by up to 56.77%. Our PACS is expected to adapt to various scenarios without additional fine-tuning, offering a new paradigm for future human-AI collaboration in aviation operations.

Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagnosis. 2D US has to perform scanning for each SP, which is time-consuming and operator-dependent. While 3D US containing multiple SPs in one shot has the inherent advantages of less user-dependency and more efficiency. Automatically locating SP in 3D US is very challenging due to the huge search space and large fetal posture variations. Our previous study proposed a deep reinforcement learning (RL) framework with an alignment module and active termination to localize SPs in 3D US automatically. However, termination of agent search in RL is important and affects the practical deployment. In this study, we enhance our previous RL framework with a newly designed adaptive dynamic termination to enable an early stop for the agent searching, saving at most 67% inference time, thus boosting the accuracy and efficiency of the RL framework at the same time. Besides, we validate the effectiveness and generalizability of our algorithm extensively on our in-house multi-organ datasets containing 433 fetal brain volumes, 519 fetal abdomen volumes, and 683 uterus volumes. Our approach achieves localization error of 2.52mm/10.26°, 2.48mm/10.39°, 2.02mm/10.48°, 2.00mm/14.57°, 2.61mm/9.71°, 3.09mm/9.58°, 1.49mm/7.54°for the transcerebellar, transventricular, transthalamic planes in fetal brain, abdominal plane in fetal abdomen, and mid-sagittal, transverse and coronal planes in uterus, respectively. Experimental results show that our method is general and has the potential to improve the efficiency and standardization of US scanning.