Technology and Health Care最新文献

{"title":"Clinical decision system for chronic kidney disease staging using machine learning.","authors":"E Chandralekha, T R Saravanan, N Vijayaraj","doi":"10.1177/09287329251316447","DOIUrl":"10.1177/09287329251316447","url":null,"abstract":"<p><strong>Background: </strong>Chronic Kidney Disease (CKD) is a prevalent health condition that requires personalized treatment planning at each of its five stages. Machine Learning (ML) and Generative AI have shown promise in predicting CKD progression based on patient data. However, existing prediction models have limitations on generalizability, interpretability, and resource requirements.</p><p><strong>Objective: </strong>This study aims to develop a clinical support system using ML models to classify CKD stages accurately. The research focuses on feature selection strategies and model performance evaluation to enhance prediction accuracy and guide personalized treatment planning for CKD patients.</p><p><strong>Methods: </strong>The study utilizes ML algorithms, including Gradient Boosting, XGBoost, CatBoost, and GAN AML, to categorize CKD stages. Various feature selection techniques such as Recursive Feature Elimination, chi-square test, and SHAP are employed to identify relevant features for improved prediction accuracy. The models are evaluated based on precision, recall, F1-score, accuracy, and AUC-ROC metrics.</p><p><strong>Conclusions: </strong>The findings demonstrate the effectiveness of CatBoost and GAN AML in accurately classifying CKD stages, highlighting the importance of expert knowledge in selecting feature selection strategies to enhance ML model performance. Future research directions include validating diverse datasets, integrating with clinical practice, and improving interpretability and explainability in CKD prediction models.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1959-1987"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505258","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":"An efficient sparse code shrinkage technique for ECG denoising using empirical mode decomposition.","authors":"Vibha Tiwari, Divya Jain, Deepak Sharma, Mohamed M Hassan, Fayez Althobaiti, Akshay Varkale, Mahmoud Ahmad Al-Khasawneh, Ravi Kumar Tirandasu","doi":"10.1177/09287329241302749","DOIUrl":"10.1177/09287329241302749","url":null,"abstract":"<p><p>Accurate denoising of Electrocardiogram (ECG) signals is essential for reliable cardiac diagnostics, but traditional methods often struggle with high-frequency noise and artifacts, leading to potential misinterpretations. It is often impeded by interference such as power line interference (PLI) and Gaussian noise. To address this challenge, we suggest a novel ECG denoising technique that combines empirical mode decomposition (EMD) with wavelet domain sparse code shrinking. Our approach first decomposes the noisy ECG signal into Intrinsic Mode Functions (IMFs) using EMD. These IMFs are then transformed into the wavelet domain, where a sparse code shrinking function is applied to effectively reduce both Gaussian noise and PLI while preserving the integrity of the original signal. The effectiveness of the technique is assessed on the MIT-BIH database, where it shows marked improvements in Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Percentage Root Mean Square Difference (PRD). The suggested approach demonstrates improved SNR and reduced MSE when compared to prior approaches, which suggests that the ECG signals are clearer and more precise. This method presents a rather effective approach to enhancing ECG analysis as it is important for diagnosis and interpretation. At 10 dB SNR, the suggested technique achieves an MSE of 0.005, which is much less than the 0.076 and 0.0025 MSEs obtained by EMD wavelet adaptive thresholding and soft thresholding correspondingly. This indicates that the proposed approach effectively eliminates noise while preserving significant signal characteristics, leading to an improved and less erroneous signal reconstruction. Furthermore, the proposed method outperformed conventional techniques and demonstrated improved noise reduction and signal clarity, achieving an SNR of 19.24 and a PRD of 20.38 at 10 dB SNR.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1773-1786"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460356","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":"Development of an active-control foot press trainer and comparison of lower limb muscle activity by exercise type and intensity.","authors":"Byung-Woo Ko, Young-Hyeon Bae, BumChul Yoon, Hyun-Soo Yoon, Joon-Ho Shin","doi":"10.1177/09287329241296731","DOIUrl":"https://doi.org/10.1177/09287329241296731","url":null,"abstract":"<p><p>BackgroundAddressing lower limb disabilities in stroke survivors is crucial for enhancing gait patterns and overall mobility.ObjectiveThis study aimed to develop an active-control foot press trainer (AFPT) to strengthen lower extremity muscles to improve rehabilitation outcomes.MethodsThis study utilized AFPT to examine the effects of different exercise types (forefoot exercise, FFE; Rearfoot Exercise, RFE) and intensities (30 and 15 repetition maxima) on lower limb muscle activity. Ten healthy women participated in the evaluation of muscle activation during ankle exercises in the standing position.ResultsThe FFE primarily activated the Tibialis Anterior (TA), Gastrocnemius Medialis (GM), and Gastrocnemius Lateralis (GL), whereas the RFE engaged the Rectus Femoris (RF). Increased exercise intensity led to higher activity in the GM and GL in the FFE and TA in the RFE, indicating the potential for tailored exercise protocols.ConclusionsAFPT is a valuable tool for personalized rehabilitation, allowing variable exercise methods and intensities. Further research is needed to assess the efficacy of AFPT in a broader population, including stroke survivors. Moreover, enhancing AFPT technology and developing diverse training programs are crucial as these advancements will significantly support the rehabilitation of individuals requiring lower limb and gait training, including stroke survivors.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 4","pages":"1895-1904"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576802","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":"Polycystic ovary syndrome in women is associated with longer anogenital distance, a new potential biomaker for PCOS.","authors":"Shumin Chen, Qiulin Cui, Yue Wu, Weiwen Fan","doi":"10.1177/09287329251317142","DOIUrl":"10.1177/09287329251317142","url":null,"abstract":"<p><p>BackgroundFetal androgen exposure plays a pivotal role in Polycystic ovary syndrome (PCOS) development and may result in elevated Anogenital distance (AGD). This meta-analysis aimed to investigate the clinical link between PCOS and AGD.MethodsA literature search was performed across various databases to identify studies evaluating AGD in adults with PCOS and without, regardless of language, up to December 2024. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS) scoring system. Random-effects models were utilized to determine mean differences (MDs) and 95% confidence intervals (CIs) in cases of high heterogeneity. This meta-analysis encompassed 4 studies involving a total of 837 participants.ResultsThe pooled analysis found a noteworthy increase of the AGD-ac and AGD-af in PCOS patient compared with the control groups, with an overall MD of AGD-ac = 5.23, 95% CI (2.60, 7.85), P-value < 0.0001, I²= 57%, and with an overall MD of AGD-af = 2.19, 95% CI (0.04, 4.35), P-value = 0.05, I²= 89%.ConclusionThe meta-analysis results indicated that women diagnosed with PCOS exhibit elongated AGD. This potential association between AGD and PCOS could serve as a novel clinical marker for the diagnosis of PCOS. Fetal androgen exposure may play a role in the pathogenesis of PCOS.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 4","pages":"1938-1948"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576803","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":"Diabetic foot ulcer classification assessment employing an improved machine learning algorithm.","authors":"Raj Kumar Gudivaka, Rajya Lakshmi Gudivaka, Basava Ramanjaneyulu Gudivaka, Dinesh Kumar Reddy Basani, Sri Harsha Grandhi, Faheem Khan","doi":"10.1177/09287329241296417","DOIUrl":"10.1177/09287329241296417","url":null,"abstract":"<p><p>BackgroundDiabetic foot ulcers (DFU) are a severe consequence of diabetes that, if left untreated, can lead to amputation, blindness, renal failure, and other serious complications. The high treatment expense and length of treatment for this therapeutic technique are both disadvantages. Despite the effectiveness of this strategy, a distant, cost-effective, and comfortable DFU diagnostic therapy is necessary.ObjectiveThis study proposed the Advanced Machine Learning Practical Method for Diabetic Foot Ulcer Classification.MethodsThis unique and cost-effective healthcare solution uses Practical Methodologies with the reinforcement learning algorithm for DFU imaging. The categorization was based on constant technological advancements, and the benefits of Machine Learning (ML) for use in DFU treatment are numerous, including enhanced clinical decision-making based on Ulcer classification and healing progress. The ML greatly impacted DFU data analysis, with categorization and risk assessment among the findings.ResultsThe machine-learning technique can potentially create a paradigm shift by providing a 92.5% classification accuracy evaluation in the diabetic foot Ulcer problem. According to Clustering Scenario Analysis of Diabetic Foot Ulcer, when compared to Mild To Moderate Localized Cellulitis (Cluster 1 produces classification efficiency from 71% to 88%), Moderate To Severe Cellulitis (Cluster 2 delivers classification efficiency from 85% to 97%), Moderate To Severe Cellulitis With Ischemia (Cluster 3 produces classification efficiency from 90% to 98%), and Life-Or Limb-Threatening Infection (Cluster 4), the results were promising (Cluster 4 makes classification efficiency from 93.5% to 98.2%). The efficiency of this is Cluster 78.45 percent higher than the existing procedure.ConclusionsThe proposed Advanced Machine Learning Practical Method demonstrates significant improvements in DFU classification accuracy and efficiency, presenting a cost-effective and effective alternative to traditional diagnostic approaches.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1645-1660"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460074","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":"Research on the realization path of open public health data in China.","authors":"Liyang Zhao","doi":"10.1177/09287329241310379","DOIUrl":"10.1177/09287329241310379","url":null,"abstract":"<p><p>Purpose/SignificancePublic health data is an important part of China's public data. It can help improve China's public data open system, realize the value that public health data should have, and achieve the goal of \"building a community of shared future for human health\" as soon as possible.Method/ProcessUsing the topic mining model, the bibliographic information related to \"public health data openness\" in the CNKI and WOS is processed to find the influencing factors that affect the realization of public health data openness. Secondly, the mutual influence relationship between these factors is found, and using system dynamics, a causal relationship diagram and a system flow diagram are constructed to simulate and analyze the value realization model of China's public health data openness, and then the value realization path of China's public health data openness is summarized.ResultThe causal relationship diagram mainly includes four types of circuits, and 43 equation designed models have been determined through multiple simulation simulations. At the 22nd month, the value of \"public health data\" began to gradually emerge. At the same time, the slope of the line between \"data security\" and \"timely response to data concerns\" is also rapidly increasing. In the fourth month, the value of \"public health data\" remained relatively low and stable.ConclusionThe realization path of China's public health data openness can be carried out from three aspects, namely, the \"safety standards\" on the supply side, the \"quality and quantity of citizens\" on the demand side, and the improvement of the relevant supervision mechanisms that connect the supply and demand sides, so as to effectively tap the potential value of China's public health data.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1679-1692"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460128","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":"Intelligent classification of lung cancer pathology images through comparative morphological feature learning.","authors":"Fangfang Peng, Saihong Li","doi":"10.1177/09287329241303371","DOIUrl":"10.1177/09287329241303371","url":null,"abstract":"<p><p>Lung cancer pathology images are categorized with enhanced accuracy in this research, addressing the significant challenge posed by the limited availability of labeled images, a limitation exacerbated by the complexity of cellular morphologies.</p><p><strong>Background: </strong>The accurate classification of lung cancer pathology images is of paramount importance for both diagnostic and therapeutic purposes. However, the development of robust classification models is often hindered by the intricate cellular morphologies and the scarcity of labeled images, which is a critical bottleneck in the field.</p><p><strong>Objectives: </strong>The study is designed to incorporate unlabeled data into the training process, thereby enhancing the classification of lung cancer pathology images through the use of comparative learning techniques.</p><p><strong>Methods: </strong>A methodology is introduced wherein confidently classified unlabeled images are integrated with labeled ones, enriching the training dataset. This approach draws on principles of farthest and nearest neighbor contrastive learning to cultivate a more challenging learning environment and to augment the variability of contrastive samples. To effectively extract key cellular morphological features, an encoder based on the ResNet50 architecture, fortified with deformable and dynamic convolutional techniques, is utilized.</p><p><strong>Results: </strong>Demonstrated by experimental results, the proposed classification strategy achieves a significant improvement in the accuracy of lung cancer image classification, even under conditions characterized by a limited availability of labeled data, thus underscoring the robustness of the method.</p><p><strong>Conclusion: </strong>The integration of comparative learning with both labeled and unlabeled images, complemented by the application of advanced convolutional techniques, is shown to be a promising avenue for enhancing the classification of lung cancer pathology images. This research is presented as a practical solution to the urgent need for accurate and efficient diagnostic tools in the field of oncology.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1589-1611"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460022","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":"FHD deep learning prognosis approach: Early detection of fetal heart disease (FHD) using ultrasonography image-based IROI combined multiresolution DCNN.","authors":"Someshwaran G, Sarada V","doi":"10.1177/09287329241310981","DOIUrl":"10.1177/09287329241310981","url":null,"abstract":"<p><p>Fetal Heart Disease (FHD) is the most prevalent root cause of infant demise which accounts for 21% of all congenital abnormalities, with most instances being catastrophic, thereby rendering the need for early prognosis. Ultrasonography is the forefront imaging modality for assessing fetal growth in four-chamber and blood vessel malformation. Clinically diagnosing the abnormality is time-consuming and requires the skill of a radiologist. In subsequent, numerous preceding research strategies ideal to meta-heuristic and deep learning's Faster Artificial Neural Network (FANN), Dense Recurrent Neural Network (DRNN), Mask-Regional Convolution Neural Network (M RCNN) and Enhanced Deep Learning-assisted CNN aid in the identification of FHD. However, the prediction models have encountered multiple challenges owing to imprecise hinders and irrelevant adhesion. Hence, we propose the automated hierarchical network-driven findings of FHD in four-chamber and blood vessels using ultrasonic 2D imaging which undergoes 3 consequential processes of Enhanced-Adaptive Median Filtering (EAMF) pre-process concerning noise variations i.e., test for SNR distortion and image enhancement i.e., visual quality, Intensified Region of Interest (IROI) segmentation for exploiting feature selection via spatial mask-labeling and Multiresolution Deep Convolutional Neural Network (MDCNN) classification in the detection of diseased pattern via confusion metrics (CM). The lesion findings of CM is determined using MATLAB R2023b with an overall substantial efficiency of 99.79% in both normal and abnormal conditions with a significant potential to assist cardiologists in the prognosis of FHD.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1999-2014"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505259","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":"Machine learning for improved medical device management: A focus on infant incubators.","authors":"Lemana Spahić, Una Sredović, Zijad Kurpejović, Emina Mrdanović, Gurbeta Pokvić, Almir Badnjević","doi":"10.1177/09287329241292168","DOIUrl":"10.1177/09287329241292168","url":null,"abstract":"<p><p>BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance.ObjectiveTo address the issue of faulty infant incubators being undetected and used between yearly post-market surveillance, an automated system based on machine learning was developed for prediction of infant incubator performance status.MethodsIn total, 1997 samples were collected during the inspection process of infant incubator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) for the development of the automated system.ResultsThe aforementioned algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The 0.93 AUC of Naïve Bayes indicates that it is overall stronger in predictive capabilities than decision tree and random forest which displayed superior accuracy in comparison to Naïve Bayes.ConclusionThe results of this study demonstrate that machine learning algorithms can be effectively used to predict infant incubator performance status on the basis of measurements taken during post-market surveillance. Adoption of these automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of infant incubators that are already being used in healthcare institutions.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2034-2040"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537954","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 computer-aided tongue image system for severe <i>mycoplasma pneumoniae</i> pneumonia.","authors":"Li Peng, Qinghua Liu, Baixiang Liu, Lin Lin, Lili Zhong","doi":"10.1177/09287329251351525","DOIUrl":"https://doi.org/10.1177/09287329251351525","url":null,"abstract":"<p><p>Background<i>Mycoplasma pneumoniae</i> pneumonia (MPP) represents the predominant form of community-acquired pneumonia in children. Clinical challenges in identifying severe MPP (SMPP) critically threaten pediatric health.ObjectiveThis study aimed to evaluate the application of the computer-aided system for standardizing tongue image characteristics and diagnosing SMPP.MethodsWe enrolled hospitalized children with general MPP (GMPP, n = 243) and SMPP (n = 371) between 2023 and 2024. The SMF-III system was employed to quantify tongue image features. Univariate logistic regression analysis was performed to identify key independent risk factors for SMPP, followed by correlation analysis. ROC curve analysis was conducted to assess diagnostic efficacy.ResultsSignificant differences in tongue features were observed between the GMPP and SMPP groups. SMPP patients predominantly exhibited red/crimson tongue coloration, yellow-white/yellow coatings, thin-greasy/thick coating textures, and reduced or absent moisture with higher total tongue image scores. Logistic regression confirmed the scores, CRP, NLR, IL-6, and IFN-γ as independent risk factors for SMPP. The scores were positively correlated with CRP, NLR, IL-6, and IFN-γ. Notably, combining tongue image scores with CRP enhanced predictive accuracy for SMPP.ConclusionTongue image variations reflect pediatric MPP disease progression. The computer-aided tongue diagnostic system provides a rapid, cost-effective, and reliable tool for auxiliary SMPP diagnosis.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251351525"},"PeriodicalIF":1.4,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508956","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}