Technology and Health Care最新文献

ObjectiveThis analysis aims to examine studies on artificial intelligence (AI) applications in breast cancer diagnosis through bibliometric methods, focusing on temporal and geographical trends. It contributes to shaping the field's roadmap and helping researchers adapt to technological innovations.MethodA comprehensive search was conducted in the Web of Science (WOS) database. Bibliometric analyses of data from 2013-2024 were performed using VOSviewer and Bibliometrix R programs.ResultsThe analysis included 1537 articles. A significant rise in research activity was observed in 2019. The thematic analysis highlighted topics like histopathology, feature selection, deep learning, and machine learning. India was the most productive country with 405 studies. Keyword analysis showed increased usage of terms like transfer learning, CNN, and radiomics. U.S. was the most cited country with 7511 citations. Concept co-occurrence analysis revealed strong associations between terms such as feature selection, datasets, algorithm performance, and classification methods. Bejnordi's 2017 study was identified as the most influential, with 1909 citations.Discussion and ConclusionThis study identifies key authors, influential works, and trending topics, offering a broad understanding of the field's structure and evolution. It helps outline the advancements and emerging directions in AI applications for breast cancer diagnosis.

IntroductionThe purpose of this study was to examine the content quality and potential shortcomings of arthroplasty training videos on Instagram.Materials and MethodsA search on Instagram was performed from November 1, 2023, to April 30, 2024. The hashtags Replacement, Total knee replacement and Knee arthroplasty were translated into 6 different languages and searched on Instagram by 6 observers who are native speakers of those languages. The videos were scored using the DISCERN score and Global Quality Score (GQS). The extent to which the videos addressed the processes about which patients need to be informed was also examined.ResultA total of 126 videos were analyzed in this study. The median DISCERN and GQS scores were 3.0 [1.0-5.0] and 3.0 [2.0-5.0], respectively. The most frequently mentioned subheading was arthroplasty procedure and prosthesis technology (74%), followed by treatment options (66%). The least mentioned subheading was complications (19%), followed by return to social life (44%).ConclusionsThe main finding of this study was that knee arthroplasty videos posted on Instagram were lacking in data. Video content largely describes surgical techniques but is insufficient to inform patients about postoperative processes. The video content quality was found to be moderately good according to both video quality scores, and these quality scores were moderately correlated with the mention of subheadings.

BackgroundHypertension is one of the most important health-related problems worldwide, and its monitoring is necessary constantly.ObjectiveThe regular methods of blood pressure monitoring have disadvantages; hence, the interest in finding better solutions is stirred.MethodsIn this study, PPG signals from 218 subjects in Guilin People's Hospital were analyzed, where 657 PPG recordings were employed together with demographic and clinical data. CNN-Attention, CNN-GRU, and LSTM, have been conducted with z-score normalization and augmentation in an 80:20 train-test split.ResultsThe highest performance of the CNN-GRU model achieved 75% accuracy, an AUC-ROC of 0.658, and perfect recall for hypertensive cases at 1.00. While the CNN-Attention model reached an accuracy of 61%, the overall poorest performance was given by LSTM.ConclusionThese results prove that accessible cardiovascular monitoring is feasible and valuable in a resource-limited settings.

BackgroundDiabetic Retinopathy (DR) remains a leading cause of blindness among diabetic patients worldwide, necessitating early and accurate diagnostic interventions. While traditional screening methods rely heavily on manual ophthalmologic evaluations, recent advancements in machine learning (ML) and deep learning (DL) have opened new avenues for automated, scalable, and interpretable diagnostic tools. However, challenges persist in developing models that are not only high-performing but also transparent enough to gain clinical trust.ObjectiveThis study introduces a novel, standardized, and interpretable ML framework designed specifically to enhance diagnostic efficiency and accuracy for DR risk prediction. By prioritizing model interpretability alongside predictive performance, our approach aims to bridge the gap between cutting-edge AI technology and clinical applicability.MethodsWe evaluated eleven ML algorithms, optimizing hyperparameters via grid search and five-fold cross-validation to identify top-performing models. A key innovation lies in our dynamic weighted voting ensemble (Voting_soft), which integrates multiple classifiers based on model confidence, thereby leveraging the strengths of diverse algorithms. Model performance was rigorously assessed using accuracy, sensitivity, and area under the curve (AUC) metrics, with ROC and PR curves comparing performance across varying training dataset proportions. Crucially, we employed SHAP (SHapley Additive exPlanations) for interpretability analysis, providing clinicians with actionable insights into feature contributions.ResultsThrough LightGBM-based correlation analysis and AUC curve determination, fourteen clinical features were identified as optimal predictors. Notably, the CatBoost model achieved superior performance on a 20% test set, while the Extreme Random Tree model demonstrated robustness on a 30% test set. Our dynamic weighted voting ensemble (Voting_soft) outperformed individual models in terms of AUC across both datasets. SHAP analysis revealed that age, triglycerides, sex, and HDL-C were key predictors of DR prevalence, offering clinically meaningful explanations for model decisions.ConclusionsThis study presents a groundbreaking ML-based DR risk prediction system that excels in both accuracy and interpretability. The integration of SHAP analysis not only enhances model transparency but also empowers clinicians with a deeper understanding of diagnostic decision-making, ultimately improving the precision and efficiency of DR screening. Our dynamic voting ensemble approach sets a new benchmark for interpretable, multi-model integration in medical diagnostics.

BackgroundOsteoarthritis (OA), a prevalent degenerative joint disease causing pain and disability, burdens global health. Acupotomy offers a minimally invasive alternative to surgery but faces limitations like variable efficacy. Combining acupotomy with oral pharmacotherapy (conventional or herbal medicine) may optimize outcomes through synergistic effects.ObjectiveTo systematically evaluate the efficacy and safety of acupotomy combined with oral medication for the treatment of osteoarthritis through a Bayesian network meta-analysis (NMA).MethodsThis study followed PRISMA-P guidelines. Randomised controlled trials (RCTs)were selected through 6 databases. Primary outcomes included overall effective rate, WOMAC score, VAS pain score, and adverse events.Results31 RCTs (3323 patients and 8 interventions) included. NMA revealed that Combinations outperformed other interventions in most comparisons. SUCRA represents the probability that an intervention ranks among the best. Notably, "acupotomy + herbal medicine" consistently ranked among the best across all three outcomes.ConclusionAcupotomy combined with oral medications demonstrated superior clinical efficacy and significant application potential. In clinical, acupotomy combined with conventional medications (e.g., NSAIDs) may be prioritised to alleviate acute symptoms, whereas acupotomy combined with herbal medicine shows more promising potential in long-term functional recovery. Treatment protocols should be tailored to individual patient conditions to maximise therapeutic outcomes.

The effect of continuous medical service intervention on health management for people who have suffered from Acute Ischemic Stroke (AIS) is an important issue in health care tracking. To pick out core aspects related to health, a relapse prediction model, evaluate the efficiency of continuous care and boost post-discharge results, a structured study is designed. After investigation and scientific verification, important signs and symptoms were chosen to set up a Significant Factors Neural Network Relapse Prediction Model (SFNNR) which aims to predict possible relapses based on previous patterns in medical data. The continuous care group was compared with the control group, and it turned out that participants in continuous care had significantly better results with fewer chances of having relapses and controlling chronic risks while displaying less psychological stress compared to the control group; furthermore, the continuous medical service showed great value on long-term management of AIS patients. The study points out that the integrated care approach should be taken more seriously as it can help healthcare staff predict the risk of relapse accurately so as to come up with personalized plans to control the relapse probability of the patients.