Postoperative respiratory complications are a major source of morbidity and mortality following cardiac surgery. Early and accurate prediction is crucial for targeted interventions, but existing models often face challenges in performance or interpretability. To address this, we developed a deep learning model, the Postoperative Hypoxemic Respiratory Failure Network(PHRF-Net), for predicting postoperative hypoxemic respiratory failure using data from 7,875 cardiac surgery patients. The PHRF-Net architecture leverages a self-attention mechanism to provide not only accurate predictions but also inherent interpretability, offering transparent insights into the key drivers of its decisions. The proposed PHRF-Net model demonstrated robust performance, achieving an Area Under the ROC Curve (AUC) of 0.816 and an F1-score of 0.900. Analysis of the model’s attention weights revealed several influential predictors. By integrating a clinically-informed feature structure with a self-attention mechanism, this work provides a framework for building AI tools that are not only accurate but also offer clinically relevant insights into the decision-making process.

This Special Issue of Bioengineering is dedicated to the profound impact of big data and artificial intelligence (AI) in the fields of biomedical research and healthcare. In an age defined by the rapid evolution of technology, this Issue explores the dynamic intersection of AI and data science with medicine. A total of 14 papers were accepted after a thorough review process, with their topics including disease diagnosis, medical data analysis, image processing, personalized medicine, pathological image segmentation, survival prediction, cognitive load assessment, and medical knowledge extraction. These studies aim to enhance medical image analysis, signal processing, data prediction, and interpretability to improve diagnostic accuracy, medical efficiency, and personalized treatment plans for patients. We hope the publication of this Special Issue can offer a comprehensive view of the transformative power of these innovative approaches and enrich research and investigations into the applications of big data and AI in biomedical research and healthcare.

Neurological disorders are the leading cause of global disease burden. Early diagnosis and treatment are crucial for enhancing patients’ quality of life and improving their prognosis. With the rapid development of artificial intelligence, especially in speech processing technology, the diagnosis of neurological diseases has made revolutionary breakthroughs. Speech processing technology can detect subtle pronunciation variations in patients’ pronunciation, enabling doctors to identify neurological disorders more objectively and accurately. In this paper, we focus on two specific disorders (Parkinson’s disease, Alzheimer’s disease), conducting an in-depth analysis and comprehensive summary of the existing research on speech-based diagnosis in neurological diseases.This article starts by introducing commonly used public speech datasets and AI processing frameworks. Then, from the perspective of model construction, this article elucidates advanced endeavors in the diagnosis of Parkinson’s disease and Alzheimer’s disease. The related works are divided into three categories: machine learning, deep learning, and large models. Furthermore, this article elaborates on the existing problems in current research and the future development trends, aiming to offer novel insights into the in-depth application of speech processing technology in neurological diseases.

Myasthenia Gravis (MG) is an autoimmune neuromuscular disease. Given that extraocular muscle manifestations are the initial and primary symptoms in most patients, ocular muscle assessment is regarded necessary early screening tool. To overcome the limitations of the manual clinical method, an intuitive idea is to collect data via imaging devices, followed by analysis or processing using Deep Learning (DL) techniques (particularly image segmentation approaches) to enable automatic MG evaluation. Unfortunately, their clinical applications in this field have not been thoroughly explored. To bridge this gap, our study prospectively establishes a new DL-based system to promote the diagnosis of MG disease, with a complete workflow including facial data acquisition, eye region localization, and ocular structure segmentation. Experimental results demonstrate that the proposed system achieves superior segmentation performance of ocular structure. Moreover, it markedly improves the diagnostic accuracy of doctors. In the future, this endeavor can offer highly promising MG monitoring tools for healthcare professionals, patients, and regions with limited medical resources.

Intraoperative Hypotension (IOH) real-time monitoring is crucial for maintaining patient stability and preventing postoperative complications. Existing IOH prediction methods primarily rely on preoperative and intraoperative data modeling and have achieved certain breakthroughs, but they generally overlook the correlations among preoperative features. The modeling mehods For other disease prediction tasks, have proposed effective solutions to this issue, particularly the "graph-structured representation & domain knowledge" fusion strategy, which has demonstrated highly competitive performance. This provides important references for constructing more accurate IOH prediction models. However, clinical realities reveal some limitations of these methods: 1) These methods construct graph-structured representations at the conceptual level, yet clinical practice shows that some medical concepts inherently lack temporal or causal relations. This leads to distorted representations of patients' physiological states and their dynamic trends, thus introducing noise into the model's predictions. 2) The domain knowledge introduced by these methods typically originates from static knowledge bases, making it difficult to dynamically expand. This results in insufficient generalization ability when the model encounters new data or rare cases. To address these issues, we propose a Large Language Model-guided Event Graph Network (LEGN): First, we design an event graph representation method that accurately characterizes the patient's physiological state and its evolution trends through higher-order event granularity abstraction. Second, we realize the dynamic provision of domain knowledge based on a Large Language Model (LLM), leveraging its powerful knowledge reasoning capabilities to expand the boundaries of knowledge and enhance the model's adaptability. Extensive experimental validation demonstrates that our proposed method achieves F1 value improvements of 1.89% and 2.63% over the best baseline on real-world and public datasets, respectively.

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks.

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

Multi-class segmentation of eye images plays a pivotal role in assessing patients with myasthenia gravis, and the measurement results rely heavily on the segmentation accuracy. However, there is still a problem with inaccurate boundary segmentation. Compared to heuristic-based network structure optimization, exploring effective loss function is an intuitive, simple, and interpretable way to address this issue. In this paper, we experimentally verify the effectiveness of boundary loss for multi-class segmentation of eye images and investigate its hybrid law with other segmentation losses. The application of the study significantly enhances the accuracy of myasthenia gravis scoring and holds promise for assisting in the evaluation of various other eye diseases.

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of “network structure” and “loss function”, and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of “nnUNet” network structure and “Cross-Entropy + Iou + Boundary” hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services.

Myasthenia Gravis (MG) is an autoimmune neuromuscular disorder that heavily affects various daily actions of the suffers. The manifestations of MG disease are fluctuating muscle weakness and fatigue across different muscle units of human body, while ocular muscles involvement is regarded as primary symptom in a majority of the patients. Currently, the conventional clinical approach for MG assessment involves identifying and measuring key ocular structures manually, which is resource-intensive and prone to subjective errors. In our paper, a novel application of computer vision technique is introduced to assist in the quantitative evaluation of extraocular muscles in MG patients. By employing advanced segmentation algorithms, both prior feature-driven and deep feature-driven methods, we aims to automatically determine the ocular structures such as the iris, sclera, and pupil, etc. This pilot study serves as a proof of concept for using classical segmentation models to facilitate automatic and non-invasive MG assessments, enhancing clinical management and patient autonomy in monitoring their condition. Our approach not only aids physicians in obtaining more accurate measurements for better disease management but also reduces the necessity for frequent clinical visits, potentially improving adherence to treatment and overall quality of life for MG patients.

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.

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.

随着数字医疗的迅猛发展,临床数字化成为关注的核心.不断涌现的新技术让数据获取变得极为简便,使得物理世界能够比较精准和全面地投射在数字世界中,从而让人们能够在数字世界重构临床活动和思维模型,也使得基于大数据的智能化应用更加深化.然而在临床数字化转型过程中也伴随着各种问题.围绕临床关键活动,提出了临床数字化过程中几个关键问题,包括流程重塑问题、精准决策问题、快速决策问题、医患共决策问题等,并归纳了人工智能技术在临床数字化环境中的几类应用.

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient’s lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient’s bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease.