Purpose: Traditional craniofacial phenotyping of obstructive sleep apnea (OSA) relies on predefined anatomical hypotheses, often yielding incomplete assessments. This study aimed to apply multiple explainable deep learning (DL) approaches to explore novel craniofacial phenotypes. Patients and Methods: A multimodal DL model was trained using frontal and lateral facial images, lateral cephalograms, and demographic data from 130 participants (65 OSA patients diagnosed by overnight polysomnography, and 65 age-and sex-matched controls). Explainability analysis of the model's prediction was conducted in a subset of 56 participants per group using average face analysis, feature importance analysis, and gradient-weighted class activation mapping (Grad-CAM). Targeted measurements were then performed on the identified non-traditional regions to validate morphological differences. Results: The model achieved an area under the curve of 0.87. The explainability analysis identified not only anatomical structures that have been confirmed to predict OSA (such as the mandible, chin, and hyoid regions), but also the middle and upper facial third (e.g. the forehead, eyebrows, and upper eyelids), which has not been fully emphasized in previous studies. Targeted measurements confirmed a significantly smaller inter-eyebrow distance, increased frontal protrusion (p < 0.001) and frontal sinus area (p < 0.001) in OSA patients. Conclusion: Explainable DL expanded OSA-related craniofacial phenotypes. Future studies are needed to validate these upper-face traits in larger cohorts and clarify their relevance to OSA subtyping and mechanisms.

Inspired by the concept of symmetry in functional representation, complex nonlinear relationships can be decomposed into combinations of lower-dimensional functions, providing an interpretable framework for modeling high-dimensional systems. With the continuous growth of road traffic volume in China and the rapid acceleration of urbanization, traffic safety issues have become increasingly prominent. To address the limitations of traditional traffic accident prediction models-including insufficient spatial information representation, weak nonlinear fitting capability, and poor interpretability-this study proposes an improved Kolmogorov-Arnold Networks (KANs) model. Specifically, a spatial embedding module, a multi-scale spline mechanism, and a residual connection structure are incorporated into the original KAN framework to enhance its ability to capture spatial heterogeneity and complex nonlinear relationships in traffic accident data. Experimental results demonstrate that the improved KAN model achieves a 2.38% increase in the coefficient of determination, while reducing the mean absolute deviation and mean squared prediction error by 24.89% and 34.69%, respectively, indicating a significant improvement in both prediction accuracy and model stability. Furthermore, the proposed model enhances interpretability by visualizing variable relationships through spline functions, enabling intuitive analysis of nonlinear effects. Overall, the improved KAN model exhibits strong capability in modeling spatially non-stationary and nonlinear structures, making it a promising tool for macroscopic traffic safety modeling with substantial application potential and practical value.

Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and assessing its severity. However, current manual measurement techniques require considerable human effort and resources from radiologists, and there is a lack of standardized methods for classifying the severity of NAFLD in existing research. To address these challenges, we propose a novel method for NAFLD segmentation and automated severity scoring. The method consists of three key modules: (1) The Semi-automatization nnU-Net Module (SNM) constructs a high-quality dataset by combining manual annotations with semi-automated refinement; (2) The Focal Feature Fusion Swin-Unet Module (FSM) enhances liver and spleen segmentation through multi-scale feature fusion and Swin Transformer-based architectures; (3) The Automated Severity Scoring Module (ASSM) integrates segmentation results with radiological features to classify NAFLD severity. These modules are embedded in a Flask-RESTful API-based system, enabling users to upload abdominal CT data for automated preprocessing, segmentation, and scoring. The Focal Feature Fusion Swin-Unet (FF Swin-Unet) method significantly improves segmentation accuracy, achieving a Dice similarity coefficient (DSC) of 95.64

Motor coordination is crucial for preschoolers' development and is a key factor in assessing childhood development. Current diagnostic methods often rely on subjective manual assessments. This paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of assessments. The method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network, thereby transforming video assessments into evaluations of keypoint sequences. The study uses different methods to handle static and dynamic actions, including regularization and Dynamic Time Warping (DTW) for spatial alignment and temporal discrepancies. A penalty-adjusted single-frame pose similarity method is used to evaluate actions. The lightweight pose estimation model reduces parameters by 85%, uses only 6.6% of the original computational load, and has an average detection missing rate of less than 1%. The average error for static actions is 0.071 with a correlation coefficient of 0.766, and for dynamic actions it is 0.145 with a correlation coefficient of 0.653. These results confirm the proposed method's effectiveness, which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.

Nocturnal groaning syndrome is a common sleep disorder characterized by irregular groaning or vocalizations during nighttime sleep, representing a significant area of research in sleep disorders. Nocturnal groaning syndrome is a common sleep disorder characterized by irregular groaning or vocalizations during nighttime sleep, representing a significant area of research in sleep disorders. proposes a multimodal recognition approach based on speech, image, and text modalities. The study analyzes audio features using Mel Frequency Cepstral Coefficients (MFCC), which is the most common method for identifying nocturnal groaning syndrome. Coefficients (MFCC), extracts image features with pretrained MobileNetV2, and identifies key physiological signals from text using TF-IDF algorithm. Subsequently, Multimodal Compact Bilinear Pooling (MCB) is employed to fuse audio and image features, and a Text-Image CNN is used to combine image and text features. Support Vector Machine (SVM) is then used to classify the fused multimodal features, and decision-level fusion is performed using weighting criteria. Experimental results demonstrate an identification accuracy of 89.5% on the test set, significantly enhancing the auxiliary diagnostic effectiveness of nocturnurnal diagnosis. Experimental results demonstrate an identification accuracy of 89.5% on the test set, significantly enhancing the auxiliary diagnostic effectiveness of nocturnal groaning syndrome.

ObjectiveThe objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.Patients and methodsThis study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.ResultsFeature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.ConclusionsThis study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

The early detection accuracy of early gastric cancer (EGC) determines the choice of the optimal treatment strategy and the related medical expenses. We aimed to develop a simple, affordable, and time-saving diagnostic model using six machine learning (ML) algorithms for the diagnosis of EGC. It is based on the endoscopy-based Kyoto classification score obtained after the completion of endoscopy and other clinical features obtained after medical consultation. We retrospectively evaluated 1999 patients who underwent gastrointestinal endoscopy at the China Beijing Hospital. Of these, 203 subjects were diagnosed with EGC. The data were randomly divided into training and test sets (ratio 4:1). We constructed six ML models, and the developed models were evaluated on the testing set. This procedure was repeated five times. The Kolmogorov–Arnold Networks (KANs) model achieved the best performance (mean AUC value: 0.76; mean balanced accuracy: 70.96%; mean precision: 58.91%; mean recall: 70.96%; mean false positive rate: 26.11%; mean false negative rate: 31.96%; and mean F1 score value: 58.46). The endoscopy-based Kyoto classification score was the most important feature with the highest feature importance score. The results suggest that the KAN model, the optimal ML model in this study, has the potential to identify EGC patients, which may result in a reduction in both the time cost and medical expenses in clinical practice.

The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful tool to screen for OSA. We recruited consecutive subjects suspected of OSA who had received sleep examination and photographing. Sixty-eight points from 2-dimensional facial photos were labelled by automated identification. An optimized model with facial features and basic clinical information was established and tenfold cross-validation was performed. Area under the receiver operating characteristic curve (AUC) indicated the model’s performance using sleep monitoring as the reference standard. A total of 653 subjects (77.2

With the rapid development of artificial intelligence, especially deep learning technology, various new technologies and applications based on face images have emerged. Obstructive sleep apnea (OSA) is the disease with the highest morbidity and the most serious long-term harm among childhood sleep breathing disorders, and it is increasingly receiving common attention from families and society. Children with the disease have a special facial appearance and require early identification and treatment to prevent it. However, the current diagnostic methods have problems such as being invasive, time-consuming, and expensive. The purpose of this article is to use graph neural network technology based on face images to establish an OSA auxiliary diagnosis strategy for children to achieve OSA screening and analysis. Therefore, this article first takes the facial landmarks as the analysis object, divides the face into six key areas, and selects important landmarks in these regions. On this basis, to better consider the relationship between important landmarks, a global collaborative recognition strategy is proposed. By extracting the implicit relationship between landmarks, face graph structure data is established. Finally, the OSA-GNN model is established to achieve OSA screening and auxiliary analysis in children. Compared with other related studies, this strategy not only has a stronger representation and generalisation ability but can also carry out clinical applications better, providing doctors with diagnostic suggestions.

目的:提出一套可客观量化评估儿童运动发展的辅助诊断方法,对儿童运动发展状况进行定量评估,辅助诊断儿童发育性协调障碍.方法:以规定动作类别的儿童动作视频数据作为输入,使用关键点检测算法得到人体关键点数据,在进行动作规整后使用基于相似度计算方法得到对应动作的质量评估结果,通过动作完成质量反映发育性协调障碍诊断结果.结果:结合关键点波形分析以及动作质量评估,得出的结果与观察得到的结果表现出明显的一致性.结论:本研究以动作视频为基础结合计算机视觉方法将儿童发育性协调障碍的诊断方法进行标准化,为该疾病的诊断以及后续干预效果评估提供了一种标准量度.

Poor fine motor performance is an important feature of children's developmental coordination disorder. To improve the diagnosis efficiency of developmental coordination disorder, computer vision-based evaluation methods have become a research hot topic. Most of the current methods are based on the evaluation of artificial features, while this paper proposes an automatic evaluation method of children's fine movements based on deep learning. By extracting human key points from videos, the method extracts the time series features of key points and uses deep learning neural networks to classify and predict children's fine movements. The experimental results show that the highest accuracy of this method is 81%, which provides an effective tool for the auxiliary diagnosis of children's developmental coordination disorder.

目的 提出一种基于计算机视觉的方法来评估儿童精细动作,以促进对儿童发育性协调障碍的准确诊断和进一步研究.方法 利用计算机视觉技术从视频中提取人体关键点,并对其进行时间序列特征提取,然后使用深度学习神经网络对儿童精细动作进行分类和预测.结果 实验结果显示,本研究方法的准确率达到81%,相较于传统的基于手工特征的评价方法,在准确率上有所提高.结论 基于计算机视觉的儿童精细动作自动评估方法,为儿童发育性协调障碍疾病的辅助诊断提供了一个有效的判别工具,为促进幼儿早期综合发展提供了有力的技术支持.

Chronic obstructive pulmonary disease (COPD) is a serious chronic respiratory disease. Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease. With the continuous development of medical digitization, the application of big data informatization in the medical and health fields has become possible. Recently, applying innovative technologies such as big data analysis, machine learning, and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot. Based on the identification and diagnosis of COPD in the high-risk population, this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions. The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.

Children’s motor coordination is an important component of physical fitness test for young children. The development of children’s motor coordination occurs throughout children’s motor development, and it is not only limited by the maturity of children’s neurodevelopment, but also plays an important role in promoting children’s neurodevelopment. This study proposes an automatic assessment method based on deep learning to improve assessment efficiency and reduce costs. The method combines human posture estimation, similarity calculation and time series feature extraction for the assessment of children’s movements. The results showed that the accuracy rate and redundancy rate of the fine action coin toss keyframes finding algorithm are 89.8% and 7.5%; the accuracy rate of the dynamic action standing long jump keyframes finding algorithm is 74.3%; the accuracy rate, precision rate and recall rate of the fine action coin toss assessment algorithm are 74.4%, 72.5% and 90.0%; the accuracy rate, precision rate and recall rate of the static action single-leg balance assessment algorithm are 87.1%, 73.5% and 87.1%; the accuracy rate, precision rate and recall rate of the dynamic action standing long jump assessment algorithm are 71.6%, 73.5% and 71.4%; the results of the three actions generally matched the expert assessment results. The method provides a good auxiliary tool for determining the motor development level of young children, and provides a good technical support for achieving the goal of “promoting the early comprehensive development of young children through actions”.

目的 探索真实世界大样本儿童病例注射用尖吻蝮蛇不良反应主动监测方法,以期为儿童病例注射用尖吻蝮蛇血凝酶不良反应的揭示和挖掘提供支持.方法 利用实体识别技术对 2014 年2月25日至 2021年1月8日首都儿科研究所住院并使用注射用尖吻蝮蛇血凝酶的35 720儿童病例进行回顾性不良反应主动监测,采用查准率(precision,P)、查全率(recall,R)和F1值 3个指标来评估实验结果.结果 经过 3轮分析,查准率、查全率和F1值 3个指标均达到了100%.结论 应用自然语言处理技术可以用于真实世界大样本儿童病例注射用尖吻蝮蛇血凝酶不良反应监测.

Gastric cancer is one of the most common malignant tumors of the digestive system. Although gastroscopy can effectively detect early cancers, manual identification and diagnosis is time-consuming and laborious, and the false-negative rate is as high as 4.6-25.8%. Early gastric cancer lesion areas can be detected and segmented using deep learning techniques. We collected and organized 12665 eligible gastroscopy images, and cropped and expanded the original dataset after labeling by professional doctors, and finally obtained 19673 images for model training. We propose ResMSE Net and DWFF Net and apply these two networks to Mask R-CNN to obtain a new model: Mask R-CNN With R-D Net. On the same standard dataset, the mAP@[IoU=0.5] and ACC values of the Mask R-CNN With R-D Net model achieve respectively 45.37% and 67.74%, which are improved by 5.83% and 9.08%, respectively, compared with the original model. In our experiments, we found that Mask R-CNN With R-D Net is difficult to detect targets that are extremely similar to the background, which is the direction of our future work. Secondly, we hope to minimize the number of model parameters and make the model more lightweight under the premise of ensuring accuracy, so that the model can be embedded into gastroscopy detection equipment for real-time detection.

基于大数据开展儿童健康管理,从多维度指标综合评估儿童的生长发育、营养和心理行为等,可以提供更加精准、个性化的儿童健康管理和评估服务.利用互联网、云计算和人工智能等技术,可实现对大量儿童健康数据的收集和分析,预测儿童健康风险,提供个性化、精准的健康管理方案.本文旨在探讨健康医疗大数据在儿童健康管理方面的应用和前景,为促进儿童健康发展、提高儿童健康水平等提供借鉴和启示.

随着数字时代的来临,医疗卫生信息化也逐步从诊疗、健康服务拓展到治疗服务.数字疗法在这一背景下应运而生.虽然数字疗法的发展前景非常广阔,但同时也面临着诸多问题与挑战.本文以文献综述的方法,介绍了传统疾病治疗的基本要素及局限性,数字疗法的概念、起源、产品分类、适用原则和范围,以及目前典型的应用情况.同时,总结分析了我国数字疗法的应用前景和发展趋势,指出数字疗法面临的难题和挑战,为医疗卫生界和学术界利用数字化智能技术与新型传感系统、AI算法技术,以及为数字化干预生命健康及体征、改善疾病防治等领域开辟新的思路提供参考.

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.