Technology and Health Care最新文献

{"title":"Multi-modality NDE fusion using encoder-decoder networks for identify multiple neurological disorders from EEG signals.","authors":"Shraddha Jain, Rajeev Srivastava","doi":"10.1177/09287329241291334","DOIUrl":"10.1177/09287329241291334","url":null,"abstract":"<p><strong>Background: </strong>The complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.</p><p><strong>Objectives: </strong>A novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets.</p><p><strong>Methods: </strong>We determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data.</p><p><strong>Results: </strong>Our method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders.</p><p><strong>Conclusions: </strong>This innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2431-2451"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of retinal nerve fiber layer in patients with high myopia complicated with glaucoma by optical coherence tomography.","authors":"Xin Wang, Yinglang Zhang, Hongbo Hu, Ning Wei","doi":"10.1177/09287329241296770","DOIUrl":"10.1177/09287329241296770","url":null,"abstract":"<p><strong>Objective: </strong>To detect the changes in the thickness of the Retinal Nerve Fiber Layer (RNFL) in patients with High Myopia (HM) complicated with glaucoma through Optical Coherence Tomography (OCT).</p><p><strong>Methods: </strong>80 patients (160 eyes) with HM complicated with glaucoma treated from March 2018 to March 2020 were enrolled as the experimental group, and 60 healthy volunteers (120 eyes) undergoing physical examination in the same period were selected as the control group. OCT measured their RNFL thicknesses.</p><p><strong>Results: </strong>Compared with that in the control group, the nasal, supratemporal, subnasal, supranasal, and infratemporal RNFL thickness and overall mean RNFL thickness in the experimental group was significantly decreased, while the temporal RNFL thickness was significantly increased in the experimental group (<i>P </i>< 0.05). According to the diopter, patients in the experimental group were assigned into group A (<i>n </i>= 25, 50 eyes, diopter range: ≥ -6.00 D and ≤ -8.00 D), group B (<i>n</i> = 30, 60 eyes, diopter range: > -8.00 D and ≤ -10.00 D) and group C (<i>n</i> = 25, 50 eyes, diopter range: > -10.00 D). The nasal, supratemporal, subnasal, supranasal, and infratemporal RNFL thickness and overall mean RNFL thickness in group A were significantly greater than those in groups B and C (<i>P</i> < 0.05). Spearman correlation analysis revealed that the absolute value of diopter was negatively correlated with the nasal, supratemporal, subnasal, supranasal, and infratemporal RNFL thickness and overall mean RNFL thickness (<i>P</i> < 0.05), and positively correlated with the thickness of temporal RNFL (<i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>In patients with HM complicated with glaucoma, RNFL is thinner in all quadrants except for temporal RNFL.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2425-2430"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FCM-NPOA: A hybrid Fuzzy C-means clustering with nomadic people optimizer for ovarian cancer detection.","authors":"S M Vijayarajan, V Purna Chandra Reddy, D Marlene Grace Verghese, Dattatray G Takale","doi":"10.1177/09287329241302736","DOIUrl":"10.1177/09287329241302736","url":null,"abstract":"<p><p>Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2452-2467"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on sustainable transportation mode of medical waste in big city hospitals based on the multi-agent modeling method.","authors":"Hao Liu, Sebastiaan Meijer, Zhong Yao","doi":"10.1177/09287329251333878","DOIUrl":"10.1177/09287329251333878","url":null,"abstract":"<p><p>BackgroundMedical waste should be collected, classified, and transported to the treatment plant within 48 h. If it is not disposed of in time, it will cause cross-infection, increasing the risk of disease transmission and environmental pollution. How to reasonably plan transportation routes to ensure that the medical waste can be transported to the treatment plant in time is very important.ObjectiveThere are usually two modes of transportation, the fastest speed and shortest path, how to reasonably plan the transportation scheme so that medical waste can be transported to the treatment plant for disposal in the specified time is the main purpose of this article.MethodsThe multi-agent modeling method is adopted. AnyLogic simulation software is used to model the transportation routes of 118 Grade III hospitals and 2 treatment plants in Beijing under the two transportation modes of fastest speed and shortest path.ResultsBased on the traffic index in Beijing, the speed range of 20 km/h-32 km/h is set up and divided into 4 parts and 24 levels with 0.5 km/h as the unit, and the 24 levels of medical waste transportation data set is formed. The key speed nodes of 21 km/h, 24 km/h and 29.5 km/h are identified.ConclusionsThe medical waste transportation model and transport data set formed in this paper have enriched the theory and data basis of medical waste transportation management. The key speed nodes of transportation model selection have important practical significance for the transportation management decision of medical waste in big cities.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2244-2257"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing a generalizable model for migraine prediction: Analysis of filtering techniques on physiological signals.","authors":"Viroslava Kapustynska, Vytautas Abromavičius, Artūras Serackis, Saulius Andruškevičius, Kristina Ryliškienė, Šarūnas Paulikas","doi":"10.1177/09287329251332415","DOIUrl":"10.1177/09287329251332415","url":null,"abstract":"<p><strong>Background: </strong>Despite wearable sensors' ability to provide continuous physiologic monitoring, migraine remains challenging to predict due to unpredictability of onset and a variety of triggers. Developing an accurate prediction model requires reducing signal variability by using effective filtering techniques.</p><p><strong>Objective: </strong>The main objective of this study is to evaluate machine learning models for predicting migraines and analyze the effect of different filtering techniques and classifiers on prediction performance.</p><p><strong>Methods: </strong>A feature set based on ANOVA analysis of four key physiological signals was used. After the pre-processing, filtering methods, including median, Butterworth, and Savitzky-Golay filter, were applied. Five classification models, Extreme Gradient Boosting, Histogram-Based Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors, were evaluated.</p><p><strong>Results: </strong>The highest predictive performance was achieved using the Savitzky-Golay filter. The Random Forest model demonstrated the best accuracy (0.858) and precision (0.815), and an F1-score of 0.677, indicating the potential of investigated signals for migraine prediction. Furthermore, the Histogram-Based Gradient Boosting model achieved the highest recall using the Savitzky-Golay filter (0.719), demonstrating its effectiveness in identifying true positive cases of migraines.</p><p><strong>Conclusion: </strong>The results indicate significant potential for healthcare applications for early migraine prediction and treatment using wearable technology. The study highlights the importance of selecting appropriate features and filtering methods to improve the accuracy and reliability of the predictions.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2184-2193"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oral health-related quality of life of orthodontic clear aligner versus conventional fixed appliance during treatment: A prospective cohort study.","authors":"Nancy M Ajwa","doi":"10.1177/09287329251326022","DOIUrl":"10.1177/09287329251326022","url":null,"abstract":"<p><p>BackgroundOrthodontic clear aligners are a technologically advanced treatment modality that improves aesthetics and comfort while impacting patients' oral health-related quality of life (OHRQoL).ObjectiveThis study assessed and compared the OHRQoL of adult orthodontic patients receiving clear aligners and fixed appliances (metal and ceramic brackets) during orthodontic treatments.MethodologyOne hundred and five orthodontic patients were recruited and classified according to the treatment received. Group 1 clear aligners, group 2- fixed appliances/metal brackets, and Group 3-fixed appliances/ceramic brackets. The patients were surveyed using an Arabic version of the Oral Health Impact Profile 14 (OHIP- 14) questionnaire before (T0), 1-week (T1), and 3-months (T2) after the start of orthodontic treatment. Data was analysed using SPSS software at a significance level set at ≤0.05.ResultsThe mean OHIP scores showed no significant difference between the 3 groups at T0 (p = 0.09) and T2 (p = 0.41) time intervals. On the contrary, the mean OHIP scores at T1 significantly differed between 3 groups (p = 0.03). The mean OHIP scores within the groups at different time intervals was significantly different. Multiple comparison within the groups showed significant reduction in the mean OHIP scores from T0 to T1 and T2 period and further from T1 to T2 period, and the mean differences were statistically significant (p < 0.001).ConclusionAdult patients treated with clear aligners had significantly higher OHRQoL than those who underwent conventional fixed bracket-based treatment after 7 days of treatment but OHRQoL was similar after three months of treatment.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2114-2124"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian sequential decision-making for rare disease clinical trials.","authors":"Yuan Gao, Jianling Bai, Feng Chen","doi":"10.1177/09287329251344056","DOIUrl":"10.1177/09287329251344056","url":null,"abstract":"<p><p>BackgroundRare disease clinical trials face challenges due to limited sample sizes and ethical imperatives to minimize futile treatments. Bayesian sequential design dynamically optimizes decisions under uncertainty, offering efficiency gains over traditional fixed-sample approaches.MethodsPropose a framework integrating sequential Bayes factor and adaptive stopping rules for trials with binary endpoint. Bayesian posterior probabilities define early termination thresholds (superiority/futility), while Bayes Factor Design Analysis validates trial feasibility. Sequential Bayes factor updates iteratively guide interim decisions based on evidence strength.ResultsThe approach enables earlier trial termination (for superiority or futility), reducing sample size, time, and costs. Patients avoid unnecessary exposure to futility treatments, while results remain interpretable even if thresholds are unmet.ConclusionThe primary goal is to confirm treatment efficacy earlier, enabling trials to be stopped promptly for either superiority or futility treatments. This strategy reduces sample size, time, and financial costs, and prevents patient exposure to futile treatments. Moreover, the study aims to promote the adoption of Bayesian sequential decision-making, thereby accelerating rare disease clinical trial approvals and drug marketing.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2350-2370"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bibliometric analysis of research on artificial İntelligence applications in breast cancer diagnosis.","authors":"Bengünur Ekinci, Hakan Tekedere","doi":"10.1177/09287329251362602","DOIUrl":"10.1177/09287329251362602","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251362602"},"PeriodicalIF":1.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of S-ketamine on emergence agitation after sevoflurane anesthesia for children: A randomized clinical trial.","authors":"Xiaole Wu, Li Li, Bei Peng, Bing Du, Jingjing Liu, Junli Yao, Ruiyu Wang","doi":"10.1177/09287329251365430","DOIUrl":"https://doi.org/10.1177/09287329251365430","url":null,"abstract":"<p><p>BackgroundWith the use of sevoflurane, the incidence of emergence agitation (EA) has also increased.ObjectiveWe aimed to investigate whether S-ketamine can prevent EA after sevoflurane anesthesia in children.MethodsChildren undergoing otolaryngology surgery were assigned to one of four groups randomly. Drugs were given five minutes before the operation was accomplished. The incidence of EA was measured by the Pediatric Anesthesia Emergence Delirium Scale (PAED) scores. Face, Legs, Activity, Cry, and Consolability scale (FLACC) scores and the rate of adverse events were evaluated.ResultsThe incidence of EA was significantly lower in children given 2 mg/kg propofol, 0.25 mg/kg S-ketamine and 0.5 mg/kg S-ketamine compared with that in children given normal saline. At 3 h and 6 h after operation, the FLACC scores in children given 0.25 mg/kg S-ketamine and 0.5 mg/kg S-ketamine were significantly lower than those in children given 2 mg/kg propofol and saline (<i>p</i> < 0.001). No statistical differences were found in adverse reactions among children in the four groups.ConclusionIntravenous injection of propofol 2 mg/kg, S-ketamine 0.25 mg/kg and S-ketamine 0.5 mg/kg before end of the operation can all reduce the incidence of occurrence of emergence agitation in children undergoing tonsillectomy with or without adenoidectomy after sevoflurane anesthesia. Compared with children given propofol 2 mg/kg and S-ketamine 0.5 mg/kg, children given S-ketamine 0.25 mg/kg has the advantage of not prolonging the awakening time.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251365430"},"PeriodicalIF":1.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of cerebral infarction using bilateral photoplethysmography.","authors":"Sang Yeon Kim, Hyun Goo Kang, YoungSuk Shin","doi":"10.1177/09287329251363294","DOIUrl":"https://doi.org/10.1177/09287329251363294","url":null,"abstract":"<p><p>BackgroundCerebral infarction is often associated with underlying cerebral vascular stenosis, such as carotid artery stenosis or cerebral artery stenosis due to arteriosclerosis. Existing imaging techniques, including carotid ultrasound, computed tomography angiography (CTA), and magnetic resonance angiography (MRA), are useful for diagnosis, but have limitations such as radiation exposure, contrast medium use side effects, and high cost. Therefore, the need for a simple, noninvasive, and cost-effective screening tool is emerging.ObjectiveIn this study, we propose a novel cerebral infarction screening technique using PPG signals measured from both index fingers for 120 s.MethodsPPG is a noninvasive optical technology that measures pulse waves that appear according to changes in blood volume. The collected waveforms were divided into windows and then normalized. Maximum Positive Amplitude (MPA) and Maximum Negative Amplitude (MNA) were extracted from each section, and the normal group and cerebral infarction patients were classified through linear discriminant analysis.ResultsAs a result of analyzing a total of 100 subjects (50 patients with cerebral infarction and 50 normal controls), the recognition rate based on MNA was 84%, MPA was 81%, and when the two indices were combined, it was 80%. Sensitivity was 80% for MNA and 72% for MPA, and specificity was 88% and 90%, respectively, suggesting that amplitude-based PPG indices can effectively reflect the presence or absence of cerebrovascular lesions.ConclusionThis study suggests the possibility of simply identifying patients with cerebral infarction by analyzing PPG signals of both fingers. The proposed technique can be used as a screening tool to complement existing imaging techniques, and is expected to contribute to reducing the burden of stroke through early diagnosis and preventive intervention in the future.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251363294"},"PeriodicalIF":1.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144823024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}