Explore the vast range of titles available.

We summarized the unique challenges that China faced in digital health due to its large population, regional disparities, and uneven distribution of medical resources. Under the guidance of the Global Initiative on Digital Health (GIDH) released by WHO, we proposed corresponding solutions that address infrastructure, data, terminology, technology and security.

In this trial involving 543 patients with stroke due to occlusion of medium or distal vessels, endovascular treatment within 24 hours after the onset of symptoms was not effective in improving functional outcome at 90 days.

Whether a conservative strategy of medical therapy alone or a strategy of medical therapy plus invasive treatment is more beneficial in older adults with non-ST-segment elevation myocardial infarction (NSTEMI) remains unclear. We conducted a prospective, multicenter, randomized trial involving patients 75 years of age or older with NSTEMI at 48 sites in the United Kingdom. The patients were assigned in a 1:1 ratio to a conservative strategy of the best available medical therapy or an invasive strategy of coronary angiography and revascularization plus the best available medical therapy. Patients who were frail or had a high burden of coexisting conditions were eligible. The primary outcome was a composite of death from cardiovascular causes (cardiovascular death) or nonfatal myocardial infarction assessed in a time-to-event analysis. A total of 1518 patients underwent randomization; 753 patients were assigned to the invasive-strategy group and 765 to the conservative-strategy group. The mean age of the patients was 82 years, 45% were women, and 32% were frail. A primary-outcome event occurred in 193 patients (25.6%) in the invasive-strategy group and 201 patients (26.3%) in the conservative-strategy group (hazard ratio, 0.94; 95% confidence interval [CI], 0.77 to 1.14; P = 0.53) over a median follow-up of 4.1 years. Cardiovascular death occurred in 15.8% of the patients in the invasive-strategy group and 14.2% of the patients in the conservative-strategy group (hazard ratio, 1.11; 95% CI, 0.86 to 1.44). Nonfatal myocardial infarction occurred in 11.7% in the invasive-strategy group and 15.0% in the conservative-strategy group (hazard ratio, 0.75; 95% CI, 0.57 to 0.99). Procedural complications occurred in less than 1% of the patients. In older adults with NSTEMI, an invasive strategy did not result in a significantly lower risk of cardiovascular death or nonfatal myocardial infarction (the composite primary outcome) than a conservative strategy over a median follow-up of 4.1 years. (Funded by the British Heart Foundation; BHF SENIOR-RITA ISRCTN Registry number, ISRCTN11343602.).

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. •We study the segmentation of isointense infant brain images.•We integrate multi-modality images.•We employ deep convolutional neural networks.•Integration of multi-modality images improves performance.•Deep convolutional neural networks outperform other methods.

Cancer is the second leading cause of mortality worldwide, largely due to low survival rates resulting from diagnosis at advanced stages. This paper focuses on how machine learning (ML) and deep learning (DL) algorithms have evolved over the past decade to improve cancer detection and classification, emphasizing the importance of early diagnosis. Convolutional Neural Networks (CNNs) have demonstrated an accuracy of 89.5% in medical image recognition, highlighting their effectiveness in imaging-based diagnosis. Recent advancements such as YOLOv7 further outperform traditional diagnostic methods by providing more accurate tumor detection. Prognostic analysis using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks has achieved accuracies of 82.3% and 84.7%, respectively. Ensemble methods exhibit superior performance with an impressive accuracy of 91.2%, outperforming individual models. Additionally, data augmentation using Generative Adversarial Networks (GANs) improves precision to 76.8%, underscoring the importance of synthetic data generation in addressing data scarcity. These findings collectively demonstrate the transformative impact of artificial intelligence in oncology and emphasize the significance of integrated, collaborative approaches for achieving improved cancer diagnosis and treatment outcomes.

Background Colorectal cancer (CRC) is the second most prevalent type of cancer in the world. Surgery is the only curative option. However, postoperative complications occur in up to 50% of patients and are associated with higher morbidity and mortality rates, lower health related quality of life (HRQoL) and increased expenditure in health care. The number and severity of complications are closely related to preoperative functional capacity, nutritional state, psychological state, and smoking behavior. Traditional approaches have targeted the postoperative period for rehabilitation and lifestyle changes. However, recent evidence shows that the preoperative period might be the optimal moment for intervention. This study will determine the impact of multimodal prehabilitation on patients’ functional capacity and postoperative complications. Methods/design This international multicenter, prospective, randomized controlled trial will include 714 patients undergoing colorectal surgery for cancer. Patients will be allocated to the intervention group, which will receive 4 weeks of prehabilitation (group 1, prehab), or the control group, which will receive no prehabilitation (group 2, no prehab). Both groups will receive perioperative care in accordance with the enhanced recovery after surgery (ERAS) guidelines. The primary outcomes for measurement will be functional capacity (as assessed using the six-minute walk test (6MWT)) and postoperative status determined with the Comprehensive Complication Index (CCI). Secondary outcomes will include HRQoL, length of hospital stay (LOS) and a cost-effectiveness analysis. Discussion Multimodal prehabilitation is expected to enhance patients’ functional capacity and to reduce postoperative complications. It may therefore result in increased survival and improved HRQoL. This is the first international multicenter study investigating multimodal prehabilitation for patients undergoing colorectal surgery for cancer. Trial registration Trial Registry: NTR5947 – date of registration: 1 August 2016.

The increasing digitization of healthcare data systems presents substantial opportunities for enhancing patient care and operational efficiency, while simultaneously introducing critical vulnerabilities such as unauthorized access, inconsistent data formats, and privacy breaches. To systematically address these risks, this study employs Failure Modes and Effects Analysis (FMEA) to identify, evaluate, and prioritize potential hazards within digital healthcare systems. It is among the first to apply the FMEA approach in a comprehensive manner to assess risks across diverse healthcare data categories and modalities, offering a novel perspective on the vulnerabilities inherent in digital health systems. Through a structured methodology, this research investigates risks across three key healthcare data categories, such as clinical, operational, and patient-reported, as well as across five major data modalities including text, image, tabular, audio, and video. Each identified failure mode was assessed through expert consultation and comprehensive literature review, considering its severity, occurrence, and detectability, and subsequently assigned a Risk Priority Number for quantitative prioritization. Key findings highlighted significant risks, including unauthorized access, data corruption, transmission errors, and privacy breaches, that threaten patient safety and system reliability. This study provides actionable recommendations to strengthen data integrity, security, and interoperability, supporting the safe adoption of AI, blockchain, and other emerging technologies in developing secure and resilient digital healthcare systems.

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model’s capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Diabetes mellitus is a long-term metabolic condition marked by high blood sugar levels due to issues with insulin production, insulin effectiveness, or a combination of both. It stands as one of the fastest-growing diseases worldwide, projected to afflict 693 million adults by 2045. The escalating prevalence of diabetes and associated health complications (kidney disease, retinopathy, and neuropathy) underscore the imperative to devise predictive models for early diagnosis and intervention. These complications contribute to increased mortality rates, blindness, kidney failure, and an overall diminished quality of life in individuals living with diabetes. While clinical risk factors and glycemic control provide valuable insights, they alone cannot reliably predict the onset of vascular complications. Genetic biomarkers and machine learning techniques have emerged as promising tools for predicting diabetes development risk and associated complications. Despite the emergence of numerous smart AI models for diabetes prediction, there is still a need for a thorough review outlining their progress and challenges. To address this gap, this paper offers a systematic review of the literature on AI-based models for diabetes identification, following the PRISMA extension for scoping reviews guidelines. Our review revealed that multimodal diabetes prediction models outperformed unimodal models. Most studies focused on classical machine learning models, with SNPs being the most used data type, followed by gene expression profiles, while lipidomic and metabolomic data were the least utilized. Moreover, some studies focused on identifying genetic determinants of diabetes complications relied on familial linkage analysis, tailored for robust effect loci. However, these approaches had limitations, including susceptibility to false positives in candidate gene studies and underpowered AI models capabilities due to sample size constraints. The landscape shifted dramatically with the proliferation of genomic datasets, fueled by the emergence of biobanks and the amalgamation of global cohorts. This surge has led to a more than twofold increase in genetic discoveries related to both diabetes and its complications using AI. Our focus here is on these genetic breakthroughs, particularly those empowered by AI models. However, we also highlight the existing gaps in research and underscore the need for further advancements to propel genomic discovery to the next level.

Background Timely initiation of physical, occupational, and speech therapy in critically ill patients is crucial to reduce morbidity and improve outcomes. Over a 5-year time interval, we sought to determine the utilization of these rehabilitation therapies in the USA. Methods We performed a retrospective cohort study utilizing a large, national administrative database including ICU patients from 591 hospitals. Patients over 18 years of age with acute respiratory failure requiring invasive mechanical ventilation within the first 2 days of hospitalization and for a duration of at least 48 h were included. Results A total of 264,137 patients received invasive mechanical ventilation for a median of 4.0 [2.0–8.0] days. Overall, patients spent a median of 5.0 [3.0–10.0] days in the ICU and 10.0 [7.0–16.0] days in the hospital. During their hospitalization, 66.5%, 41.0%, and 33.2% (95% CI = 66.3–66.7%, 40.8–41.2%, 33.0–33.4%, respectively) received physical, occupational, and speech therapy. While on mechanical ventilation, 36.2%, 29.7%, and 29.9% (95% CI = 36.0–36.4%, 29.5–29.9%, 29.7–30.1%) received physical, occupational, and speech therapy. In patients receiving therapy, their first physical therapy session occurred on hospital day 5 [3.0–8.0] and hospital day 6 [4.0–10.0] for occupational and speech therapy. Of all patients, 28.6% (95% CI = 28.4–28.8%) did not receive physical, occupational, or speech therapy during their hospitalization. In a multivariate analysis, patients cared for in the Midwest and at teaching hospitals were more likely to receive physical, occupational, and speech therapy (all P  < 0.05). Of patients with identical covariates receiving therapy, there was a median of 61%, 187%, and 70% greater odds of receiving physical, occupational, and speech therapy, respectively, at one randomly selected hospital compared with another (median odds ratio 1.61, 2.87, 1.70, respectively). Conclusions Physical, occupational, and speech therapy are not routinely delivered to critically ill patients, particularly while on mechanical ventilation in the USA. The utilization of these therapies varies according to insurance coverage, geography, and hospital teaching status, and at a hospital level.