Technology and Health Care最新文献

{"title":"Advancement of post-market surveillance of medical devices leveraging artificial intelligence: ECG devices case study.","authors":"Madžida Hundur, Lemana Spahić, Faruk Bećirović, Lejla Gurbeta Pokvić, Almir Badnjević","doi":"10.1177/09287329241303727","DOIUrl":"10.1177/09287329241303727","url":null,"abstract":"<p><p>BackgroundAfter 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment.ObjectiveThis study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices. Specifically, the research focuses on classifying the success or failure of ECG device operations based on performance and safety parameters. The ultimate goal is to improve the management strategies of ECG devices in healthcare institutions, ensuring optimal functionality and increasing the reliability of diagnostic procedures.MethodDuring the inspection process of ECG devices conducted by an accredited laboratory in accordance with ISO 17020 standard in numerous healthcare institutions in Bosnia and Herzegovina, a total of 5577 samples were collected. Various machine learning algorithms, including Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM), were employed for result comparison and selection of the most accurate algorithm.ResultsAll algorithms demonstrated good performance, but the Random Forest (RF) algorithm stood out, achieving 100% accuracy in predicting the success/unsuccess status of the device. While the results of this research are specific to the collected data from EKG devices, the developed algorithms can be applied to other similar datasets, offering opportunities for broader use in the medical environment.ConclusionImplementing machine learning algorithms for automated systems in healthcare institutions can significantly enhance the quality of patient diagnosis and treatment. Additionally, these systems can optimize costs associated with managing medical devices. Improved post-market surveillance using ML can address challenges related to ensuring device reliability and safety.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1818-1826"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460413","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":"MANSHIP: Mobile-based assistive notification service for hearing-impaired people using a hybrid deep learning model.","authors":"Soualihou Ngnamsie Njimbouom, Kwonwoo Lee, Jeong-Dong Kim","doi":"10.1177/09287329241309702","DOIUrl":"10.1177/09287329241309702","url":null,"abstract":"<p><p>Hearing impairment, often caused by noise-induced trauma, significantly affects sound perception, communication, and cognitive abilities while increasing the risk of secondary accidents-individuals with hearing impairment are twice as likely to experience accidents as those with normal hearing. According to a 2023 WHO report, approximately 432 million adults and 34 million children globally are affected by hearing loss. While Assistive Listening Devices are commonly recommended, they are inadequate for individuals with total hearing loss. Therefore, alternatives are necessary to enhance safety and reduce accident risks. The present study introduces a hybrid deep learning model combining Very Deep Convolutional Networks (VGG16) and Residual Networks (ResNet-50) for efficient sound wave analysis and classification. Trained and validated on a comprehensive urban sound dataset, the model achieved a remarkable accuracy of 97.14%, surpassing existing state-of-the-art solutions. Furthermore, a mobile-based assistive notification system, MANSHIP, was developed to detect environmental sounds and alert individuals with profound or total hearing loss to potential hazards. MANSHIP addresses critical safety challenges and demonstrates the potential to improve the quality of life for those with severe hearing impairments by fostering safer environments and reducing caregiver dependency.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1787-1799"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460061","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":"Treatment of recurrent shoulder dislocation by arthroscopic subscapularis augmentation using the long head of the biceps tendon.","authors":"Jun Wang, Danlei Huang, Zhiyang Ye, Weizong Weng, Guangze Wang, Haoyuan Liu, Jianming Huang","doi":"10.1177/09287329241302741","DOIUrl":"10.1177/09287329241302741","url":null,"abstract":"<p><p>BackgroundRecurrent anterior shoulder dislocation is a common shoulder problem, usually caused by a force from the front when the shoulder joint is abducted and externally rotated. In the present study, we investigated the effect of arthroscopic subscapularis augmentation using the long head of the biceps tendon on shoulder motion after restoring anterior stability of the joint in patients with 13.5-20% of scapular glenoid defects.MethodsFifty patients admitted to our department with recurrent anterior shoulder dislocation between April 2017 and July 2021 were retrospectively analyzed. The patients were divided into two groups (groups A and B, n = 25 each) with comparable age, sex, hand dominance, and articular glenoid bone loss. Patients in group A were treated with arthroscopic Bankart repair and subscapularis augmentation, whereas those in group B underwent arthroscopic long head of the biceps transposition and subscapularis augmentation. All patients in both groups were followed up for more than 1 year, with a mean follow-up period of 20.1 ± 0.7 months (range, 13-28 months). The primary outcomes were changes in the visual analog scale score, Rowe classification, and Constant-Murley shoulder outcome score.ResultsNone of the patients in either group had experienced recurrent dislocation at 1-year follow-up. The visual analog scale scores decreased, and the Rowe and Constant-Murley scores improved significantly compared to the preoperative scores. Significant differences were observed in the forward flexion, abduction, and internal rotation angles of the shoulder joint in both groups at 1-year follow-up compared to baseline. The postoperative forward flexion (<i>P </i>= 0.143), abduction (<i>P </i>= 0.778), and internal rotation angles (<i>P </i>= 0.609) did not differ significantly between the two groups. At 1-year follow-up, the loss of angles of external rotation at the side and external rotation at 90° abduction in group B exhibited significantly less angular loss than group A.ConclusionArthroscopic subscapularis augmentation using the long head of the biceps transposition technique was effective at restoring anterior stability in patients with 13.5-20% scapular glenoid defects. It was more effective at restoring the external rotational function of the shoulder joint than arthroscopic Bankart repair and subscapularis augmentation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1763-1772"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460189","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":"Uncovering potential molecular markers and pathological mechanisms of Parkinson's disease and myocardial infarction based on bioinformatics analysis.","authors":"Jian Liu, Lu Xing, Tianye Lan, Qiang Wang, Yitong Wang, Xuenan Chen, Weimin Zhao, Liwei Sun","doi":"10.1177/09287329241307805","DOIUrl":"10.1177/09287329241307805","url":null,"abstract":"<p><p>BackgroundThe direct association between Parkinson's disease (PD) and Myocardial infarction (MI) has been the subject of relatively limited research.ObjectiveThe purpose of this study was to identify the genes most associated with PD and MI to explore their common pathogenesis.MethodsThe gene expression profiles of PD and MI were downloaded from GEO database. Differential expression analysis was performed to identify the common differential expression genes (DEGs) of PD and MI, followed by functional annotation. Subsequently, protein-protein interaction network were constructed, and hub DEGs were identified based on CytoHubba plugin and LASSO regression analysis. To explore the potential molecular mechanism of hub DEGs, GSEA analysis, immune correlation analysis, drug prediction and molecular docking were performed, and transcription factors (TF) and lncRNA-miRNA-mRNA (ceRNA) regulatory networks were constructed.ResultsA total of 48 DEGs with the same expression trend were identified in the MI vs. normal control (NC) and PD vs. NC groups. Functional annotation results showed that the common DEGs were significantly enriched in immune and inflammation-related pathways. RPS4Y1 and UTY were the most relevant hub DEGs for PD and MI, and may be involved in the HALLMARK_MYC_TARGETS_V1 and HALLMARK_PROTEIN_SECRETION pathways. TP63 was a common TF of RPS4Y1 and UTY. The PVT1/KCNQ1OT1-hsa-miR-31-5p-RPS4Y1 and KCNQ1OT1-hsa-let-7a-5p/hsa-miR-19b-3p-UTY axes may play an important role in regulating PD and MI. CYCLOHEXIMIDE and ATALAREN may be potential drugs for the treatment of PD and MI comorbidity. In addition, PD and MI exhibit different patterns of immune cell infiltration and immune function status, which may be related to the specific pathological processes of the disease.ConclusionsThis study revealed for the first time that RPS4Y1 and UTY may be common biomarkers of PD and MI and may be potential therapeutic targets. This study provides new perspective on the common molecular mechanisms between PD and MI.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1878-1894"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460191","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":"Relationship of influenza virus to inflammatory factors and immune function in elderly patients with COPD: A retrospective analysis.","authors":"Yanrong Chen, Yun Shi, Liyang Dou, Yan Liu, Jing Zhang","doi":"10.1177/09287329251317307","DOIUrl":"10.1177/09287329251317307","url":null,"abstract":"<p><p>ObjectiveThe aim of this retrospective study was to investigate the relevance of influenza A virus (IAV) in acutely exacerbating airway inflammatory response and disrupting immune function in elderly COPD patients.MethodsThe group conducted a pre-test: using multiplex combined real-time PCR detection kits Multiple real⁃time PCR was used to detect twenty-four pathogens, 385 patients clinically diagnosed with COPD were tested for viral nucleic acid in throat swabs. At the same time, peripheral blood leukapheresis was collected from both groups of patients, and their IL-6, IL-8, IL-1β, and TNF-α levels were detected, along with the levels of T-cell differentiation markers CD4 and CD8, to assess the influence of influenza virus on the immune function of elderly COPD patients and its relevance to the acute exacerbation of airway inflammatory response in elderly COPD patients.ResultsResults showed that the expression of inflammatory cytokines IL-6, IL-8, IL-1β and TNF-α was significantly higher in the viral group compared with the non-infected group (P < 0.05, P < 0.01). The levels of T cell differentiation type markers CD4 and CD8 were significantly lower in the infected group compared with the uninfected group.ConclusionInfluenza virus further exacerbated airway inflammatory response and decreased the immune function of T cells by activating intrinsic immune molecules such as IL-6, IL-8, IL-1β and TNF-α.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1988-1998"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505260","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":"Technological integration in timing of endoscopy: Predictive modeling for acute upper gastrointestinal bleeding outcomes.","authors":"Yangde Miao, Ajun Gu, Guang Yu, Binbin Tang","doi":"10.1177/09287329251316050","DOIUrl":"10.1177/09287329251316050","url":null,"abstract":"<p><p>BackgroundTechnological advancements have revolutionized the management of acute upper gastrointestinal bleeding (AUGIB). However, the impact of endoscopic timing on treatment outcomes remains a critical area of exploration.ObjectiveThis study evaluated the role of endoscopic timing in improving treatment outcomes for AUGIB and introduces a predictive model incorporating clinical and technological insights.MethodsA retrospective analysis of AUGIB patients treated between December 2020 and December 2023 was conducted. Logistic regression identified significant predictors of outcomes, and receiver operating characteristic (ROC) analysis evaluated their predictive value. A predictive model was developed based on these findings.ResultsAmong 145 patients, 35 (24.1%) experienced rebleeding. Key predictors included endoscopic timing, active bleeding, shock on admission, and bleeding volume (p < 0.05). The predictive model demonstrated robust performance (C-index: 0.885, 95% CI: 0.810-0.956), emphasizing the clinical relevance of precise timing in endoscopic intervention.ConclusionThis study underscores the importance of integrating technology with clinical practice to optimize endoscopic timing and improve AUGIB outcomes. The predictive model offers a valuable tool for risk stratification and clinical decision-making in modern healthcare settings.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2026-2033"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505261","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":"ADET MODEL: Real time autism detection via eye tracking model using retinal scan images.","authors":"Jesu Mariyan Beno Ranjana, Rajendran Muthukkumar","doi":"10.1177/09287329241301678","DOIUrl":"10.1177/09287329241301678","url":null,"abstract":"<p><p>BackgroundDeficits in concentration with social stimuli are more common in children affected by autism spectrum disorder (ASD). Developing visual attention is one of the most vital elements for detecting autism. Eye tracking technology is a potential method to identify an early autism biomarker based on children's abnormal visual patterns.ObjectiveEye tracking retinal scan path images can be generated by eyeball movement during the time of watching the screen and capture the eye projection sequences, which helps to analyze the behavior of the children. The Shi-Tomasi corner detection methodology uses open CV to identify the corners of the eye gaze movement in the images.MethodsIn the proposed ADET model, the corner detection-based vision transformer (CD-ViT) technique is utilized to diagnose autism at an early stage. Generally, the transformer model divides the input images into patches, which can be fed into the transformer encoder process. The vision transformer is fine-tuned to resolve binary classification issues once the features are extracted via remora optimization. Specifically, the vision transformer model acts as the cornerstone of the proposed work with the help of the corner detection technique. This study uses a dataset with 547 eye-tracking retinal scan path images for both autism and non-autistic children.ResultsExperimental results show that the suggested ADET frameworkachieves a better classification accuracy of 38.31%, 23.71%, 13.01%, 1.56%, 18.26%, and 44.56% than RM3ASD, MLP, SVM, CNN, SVM, and our proposed ADET methods.ConclusionsThis screening method strongly suggests that it be used to assist medical professionals in providing efficient and accurate autism detection.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1661-1678"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460409","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":"Application of enhanced recovery after surgery in pediatric patients with obstructive sleep apnea-hypopnea syndrome.","authors":"Xiang Qi, Changjun Yang, Wei Huang, Wei Han, Yujie Li","doi":"10.1177/09287329251314265","DOIUrl":"10.1177/09287329251314265","url":null,"abstract":"<p><p>BackgroundEnhanced Recovery After Surgery (ERAS) has demonstrated effectiveness in accelerating recovery and reducing complications across surgical fields, with limited application in Ear-Nose-Throat surgeries. Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS), a prevalent condition affecting pediatric patients, calls for innovative management due to its impact on health and the need for surgical interventions like tonsillectomy.ObjectiveThe present study aimed to investigate the efficacy of ERAS in pediatric patients with OSAHS.MethodsReview and analyze 1100 cases of pediatric patients with OSAHS who underwent plasma-coblation tonsillectomy and adenoidectomy using nasal endoscopy from June 2016 to June 2022 in our hospital. Among these cases, a total of 564 patients were managed according to ERAS theory, while 536 patients were treated with classical theory. The incidence of preoperative discomfort, postoperative pain, bleeding, and other complications between the two groups were compared.ResultsERAS group showed comparable preoperative-discomfort rates to the control (<i>P </i>= 0.799). However, ERAS patients exhibited significantly lower pain scores at 24-, 48-, and 72-h post-operation (<i>P </i>< 0.05). Mental state scores were similar between ERAS and control 4 h pre-surgery (<i>P </i>> 0.05), but notably lower in ERAS at 30 min pre-op and 6-, 12-, and 24-h post-operation (<i>P </i>< 0.05). ERAS had lower complication rates and intra/postoperative bleeding, quicker ambulation/oral intake, and shorter hospital stays than control (<i>P </i>< 0.05).ConclusionERAS management in patients with OSAHS resulted in notable reductions in postoperative pain and incidence of complications, along with improved postoperative recovery and shorter hospital stays.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1755-1762"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459996","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":"Artificial intelligence based BCI using SSVEP signals with single channel EEG.","authors":"Venkatesh Kanagaluru, Sasikala M","doi":"10.1177/09287329241302740","DOIUrl":"10.1177/09287329241302740","url":null,"abstract":"<p><p>BackgroundBrain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices. Steady-state visual-evoked potentials (SSVEPs) are particularly useful in BCIs because of their rapid communication capabilities and minimal calibration requirements. Although SSVEP-based BCIs are highly effective, traditional classification methods face challenges in maintaining high accuracy with minimal EEG channels, especially in real-world applications. There is a growing need for improved classification techniques to enhance performance and efficiency.ObjectiveThe aim of this research is to improve the classification of SSVEP signals using machine-learning algorithms. This involves extracting dominant frequency features from SSVEP data and applying classifiers such as Decision Tree (DT), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to achieve high accuracy while reducing the number of EEG channels required, making the method practical for BCI applications.MethodsSSVEP data were collected from the Benchmark Dataset at Tsinghua BCI Lab using 64 EEG channels per subject. The Oz channel was selected as the dominant channel for analysis. Wavelet decomposition (db4) was used to extract frequency features in the range 7.8 Hz to 15.6 Hz. The frequency of the maximum amplitude within a 5-s window was extracted as the key feature, and machine learning models (DT, LDA, and SVM) were applied to classify these features.ResultsThe proposed method achieved a high classification accuracy, with 95.8% for DT and 96.7% for both LDA and SVM. These results show significant improvement over existing methods, indicating the potential of this approach for BCI applications.ConclusionThis study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1905-1916"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460106","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":"DABiG: Breath pattern classification using the hybrid deep learning with optimal feature selection.","authors":"P Kaleeswari, R Ramalakshmi, T Arun Prasath, A Muthukumar, R Kottaimalai, M Thanga Raj","doi":"10.1177/09287329241303368","DOIUrl":"10.1177/09287329241303368","url":null,"abstract":"<p><p>BackgroundA person's breathing pattern can be a reflection of their emotional and physical well-being because it shows the frequency, intensity, and rhythm of their breathing.ObjectiveThis research article presents a comprehensive approach to breathe pattern classification utilizing gyroscope and accelerometer readings obtained from individuals using two distinct sensors. The study encompasses the acquisition of six diverse breathing patterns, with a focus on data pre-processing through Min-Max normalization.MethodsTo select essential features from the normalized data, an innovative optimization algorithm, Adaptive Chimp Optimization (AdCO), is introduced. AdCO integrates an adaptive weighting strategy into the conventional Chimp optimization algorithm, enhancing convergence rates and enabling global optimal feature selection. Furthermore, the article introduces the application of the selected features in breath pattern classification using a hybrid deep learning mechanism, DABiG. DABiG leverages the Bidirectional Gated Recurrent Unit (BiGRU), a neural network architecture capable of processing sequential data bi-directionally.ResultsSpatial and temporal attention mechanisms are incorporated into DABiG to enhance its ability to focus on relevant spatial regions and time steps within the breath pattern data.ConclusionSpatial attention assigns weights to spatial regions, while temporal attention assigns weights to time steps, improving feature extraction and classification accuracy.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1612-1625"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460111","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}