The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages I–II and III–IV of LSCC. Data from 401 histologically confirmed LSCC patients were collected from two centers (training set: 213; internal test set: 91; external test set: 97). Radiomics features were extracted from the MRI images, and seven radiomics models based on single and combined sequences were developed via random forest (RF). A DL model was constructed via ResNet 18, where DL features were extracted from its final fully connected layer. These features were fused with crucial radiomics features to create a combined model. The performance of the models was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with the radiologist performances. The predictive capability of the combined model for Progression-Free Survival (PFS) was evaluated via Kaplan–Meier survival analysis and the Harrell’s Concordance Index (C-index). In the external test set, the combined model had an AUC of 0.877 (95% CI 0.807–0.946), outperforming the DL model (AUC: 0.811) and the optimal radiomics model (AUC: 0.835). The combined model significantly outperformed both the DL (p = 0.017) and the optimal radiomics models (p = 0.039), and the radiologists (both p < 0.050). Moreover, the combined model demonstrated great prognostic predictive value in patients with LSCC, achieving a C-index of 0.624 for PFS. This combined model enhances preoperative LSCC staging, aiding in making more informed clinical decisions.

In this study, we propose a rehabilitation training system that incorporates active and passive rehabilitation modes to enhance the convenience, efficacy, and safety of rehabilitation training for patients with upper limb hemiplegia. This system facilitates elbow flexion and extension as well as wrist and palm flexion and extension. The experimental results demonstrate that the exoskeleton robot on the affected limb exhibits a rapid response and maintains a highly synchronized movement with the unaffected upper limb equipped with an angle sensor, preserving stability and coordination throughout the movement process without significant delay affecting the overall motion. When the movement of the unaffected upper limb exceeds the predetermined angle threshold, the affected limb promptly initiates a protective mechanism to maintain its current posture. Upon equalization of the angles between the two limbs, the affected limb resumes synchronized movement, thereby ensuring the safety of the rehabilitation training. This research provides some insights into the functional improvements of safe and reliable upper limb exoskeleton rehabilitation training systems.

Alzheimer disease (AD) is the most commonly occurring neurodegenerative disease. However, current diagnostics for AD primarily rely on invasive tests, which limit the application of diagnostic procedures in early screening. Speech, as a noninvasive biomarker, is closely associated with AD but has not been fully leveraged as a diagnostic tool. This study develops a novel early AD diagnosis method that uses primitive speech and explores its potential application in community screening. Moreover, the study proposes an innovative multimodal method for speech feature fusion that combines acoustic and semantic information to differentiate patients with AD from normal controls. This method uses the ImageBind audio encoder to extract acoustic features and the Embeddings from Language Model to extract semantic features, thereby effectively integrating the features by mid-level fusion. The training set comprises 166 speech recordings, which comprise 87 samples from individuals with AD and 79 samples from healthy control subjects. The ratio of training set to test set is 7:3. Evaluation of the Alzheimer dementia recognition through spontaneous speech only dataset showed that the proposed model achieved a classification accuracy of 0.903 and a recall rate of 1, and it considerably outperformed existing baseline models, thereby confirming the effectiveness of the proposed approach to AD diagnosis. This study applies the multimodal fusion of speech features to an early AD diagnostic procedure and achieves excellent performance. The findings of this study not only provide a new approach to noninvasive AD screening but also open new pathways to the early diagnosis of other neurodegenerative diseases.

Background. The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. To address the issue of the current classical deterministic method tending to deviate in the tracking direction in the region of crossing fiber region, in this paper, we propose an adaptive correction-based deterministic white matter fiber tracking method, named FTACTD. Methods. The proposed FTACTD method can accurately track white matter fibers by adaptively adjusting the deflection direction strategy based on the tensor matrix and the input fiber direction of adjacent voxels. The degree of correction direction changes adaptively according to the shape of the diffusion tensor, mimicking the actual tracking deflection angle and direction. Furthermore, both forward and reverse tracking techniques are employed to track the entire fiber. The effectiveness of the proposed method is validated and quantified using both simulated and real brain datasets. Various indicators such as invalid bundles (IB), valid bundles (VB), invalid connections (IC), no connections (NC), and valid connections (VC) are utilized to assess the performance of the proposed method on simulated data and real diffusion-weighted imaging (DWI) data. Results. The experimental results of the simulated data show that the FTACTD method tracks outperform existing methods, achieving the highest number of VB with a total of 13 bundles. Additionally, it identifies the least number of incorrect fiber bundles, with only 32 bundles identified as wrong. Compared to the FACT method, the FTACTD method reduces the number of NC by 36.38. In terms of VC, the FTACTD method surpasses even the best performing SD_Stream method among deterministic methods by 1.64. Extensive in vivo experiments demonstrate the superiority of the proposed method in terms of tracking more accurate and complete fiber paths, resulting in improved continuity. Conclusion. The FTACTD method proposed in this study indicates superior tracking results and provides a methodological basis for the investigating, diagnosis, and treatment of brain disorders associated with white matter fiber deficits and abnormalities.

The aim of this study was to explore the relationships between teaching competence, career development, industrial orientation, and specialty construction in the case of intelligent medicine specialties in universities of traditional Chinese medicine, as well as the influencing factors of specialty construction. Two-stage sampling with unequal probability was conducted to distribute self-administered questionnaires in the fieldwork. A structural equation model was built to investigate the influencing factors of specialty construction. Forty-two teachers were recruited and completed the questionnaire. The study found that teaching competence, career development, and industrial orientation positively affect specialty construction at a significant level. Career development and industrial orientation play chain mediating roles in the effect of teaching competence on specialty construction. In the construction of intelligent medicine specialties, a professional curriculum system should be built that caters to industrial demand and is combined with industrial development, the traditional mode of teaching should be transformed, and the transformation of theoretical knowledge into practical ability should be promoted. Innovations in teaching modes should be achieved by introducing information technology, and the teacher evaluation system should be optimized.

Inadequate force feedback and collision warnings in teleoperated surgical instruments elevate risks during intravascular cannulation. This study introduces an innovative master haptic interface that utilizes magnetorheological (MR) fluid to enhance surgeons’ operational perception during robot-assisted intervention surgery. The system delivers real-time haptic feedback to enhance surgical operational safety and automatically amplifies the feedback force when the contact force on the slave side surpasses the predefined threshold, enabling timely collision alerts. A series of preliminary experiments has been carried out to validate the efficacy of this particular type of haptic interface. The experimental results clearly indicate that the master haptic interface based on MR fluid and carefully designed can effectively enhance the operator’s haptic perception and provide collision alarms in a timely manner with haptic clues, improving the safety and operability of robot intravascular intervention. This research provides some insights into the functional improvements of safe and reliable robot-assisted catheter systems.