Informatics in Medicine Unlocked最新文献

{"title":"3D SPECT-based machine learning approach to early Parkinson’s diagnosis","authors":"Jihad Boucherouite,&nbsp;Abdelilah Jilbab,&nbsp;Atman Jbari","doi":"10.1016/j.imu.2025.101722","DOIUrl":"10.1016/j.imu.2025.101722","url":null,"abstract":"<div><h3>Purpose:</h3><div>Dopamine transporter binding measurements in the striatum play a critical role in identifying dopaminergic deficits in Parkinson’s disease (PD), particularly during early stages. Using 3D SPECT imaging data from the PPMI database (928 subjects), we developed an automated feature extraction pipeline that captures subtle morphological changes characteristic of early neurodegeneration.</div></div><div><h3>Approach:</h3><div>Our methodology involved a sub-volume from each 3D SPECT scan and applied several image processing techniques to each slice. Unlike approaches relying on external datasets or derived features, we focused exclusively on direct geometric and shape features. We extracted 30 novel discriminative features, including volumetric measures, global thresholds, circularity, compactness, and curvature, with rigorous statistical validation using parametric tests (t-tests, ANOVA). The top 19 features, prioritized through MRMR to eliminate redundancy, were used to train and evaluate 34 machine learning classifiers.</div></div><div><h3>Results:</h3><div>For binary classification (Healthy Control (HC) vs. early PD), our Medium Gaussian SVM achieved excellent testing performance (98.56% accuracy, 98.81% precision), significantly higher than recent state-of-the-art approaches. Meanwhile, for three-class classification (HC, PD stage 1, and PD stage 2), the Subspace Discriminant Ensemble achieved the highest accuracy of 76.26%, solving a critical real-world scenario.</div></div><div><h3>Conclusions:</h3><div>This stage-aware approach advances the field by enabling automatic bilateral analysis of the striatum and accurate classification of early-stage PD, potentially supporting earlier clinical intervention. It also addresses a critical gap in the literature where most methods focus solely on binary classification (HC vs. PD) without capturing disease progression.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"60 ","pages":"Article 101722"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating cultural impact on subject-independent EEG-based emotion recognition approaches","authors":"Anshul Sheoran ,&nbsp;Camilo E. Valderrama","doi":"10.1016/j.imu.2025.101728","DOIUrl":"10.1016/j.imu.2025.101728","url":null,"abstract":"<div><div>Culture plays a crucial role in shaping emotional expression and recognition, influencing how individuals perceive and regulate emotions. Electroencephalography (EEG) can capture electrical activity associated with human emotion processing from the scalp. The electrical activity can be processed using deep learning models to predict emotional states. Two approaches can be employed to develop these deep learning models: subject-dependent and subject-independent. The subject-independent approach is more practical as it trains the model on data from some individuals and tests it on entirely different individuals, ensuring it generalizes well to new users. However, because of the high variability of EEG across individuals, the subject-independent approach tends to yield low performance. Recent studies suggest incorporating demographic information along with EEG signals is one way to overcome this issue. By using the subject-independent approach, this study investigates how cultural factors impact emotion prediction. Specifically, we used a stacking model that combines deep learning with multinomial logistic regression to predict positive, neutral, and negative emotions among 15 Chinese, 8 French, and 8 German subjects. Our approach achieved accuracies of 77.3% for Chinese subjects, 73% for French subjects, and 65% for German subjects, which are comparable to or exceed accuracies reported by previous studies. Our approach highlighted that incorporating cultural information increases the likelihood of predicting positive emotions for Chinese participants and negative emotions for Europeans. Moreover, French and German subjects exhibited similar neural patterns across all emotions, suggesting a more common cultural sharing between those subjects. Overall, our findings emphasize the importance of integrating cultural considerations into emotion recognition models. This inclusion not only improves emotion prediction accuracy for subject-independent approaches but also promotes inclusivity and ethical practices in emotion recognition systems.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"60 ","pages":"Article 101728"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing diabetes risk prediction: A comparative evaluation of bagging, boosting, and ensemble classifiers with SMOTE oversampling","authors":"Rabia Asif ,&nbsp;Darshana Upadhyay ,&nbsp;Marzia Zaman ,&nbsp;Srini Sampalli","doi":"10.1016/j.imu.2025.101661","DOIUrl":"10.1016/j.imu.2025.101661","url":null,"abstract":"<div><div>Diabetes is a major global health concern, with millions of individuals at risk of developing this chronic condition. Early prediction and intervention are essential for effective diabetes management. This study explores advanced machine learning techniques, specifically bagging, boosting, and ensemble methods to improve diabetes risk prediction. Using three diverse datasets, namely, the Centers for Disease Control and Prevention (CDC) Diabetes Health Indicators dataset, the Early Stage Diabetes Risk Prediction System (ESDRP) dataset, and the PIMA Indian Diabetes dataset are utilized to evaluate the adaptability and robustness of the proposed models. Our approach addresses critical gaps in existing research, including the handling of highly imbalanced datasets through the Synthetic Minority Over-sampling Technique (SMOTE), the necessity of feature selection, and the underutilization of the CDC dataset in diabetes studies. We find that applying SMOTE to the CDC dataset significantly enhances model performance, with the CATBoost algorithm achieving an accuracy of 91 %. For the ESRPS dataset, ensemble methods demonstrate even stronger results, achieving 98 % accuracy using the top five features. This study not only contributes to the development of more accurate predictive models for diabetes risk but also provides insights into enhancing the robustness of machine learning methods in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101661"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable deep learning models for HER2 IHC scoring in breast cancer diagnosis","authors":"Md Serajun Nabi ,&nbsp;Mohammad Faizal Ahmad Fauzi ,&nbsp;Hezerul Bin Abdul Karim ,&nbsp;Phaik-Leng Cheah ,&nbsp;Seow-Fan Chiew ,&nbsp;Lai-Meng Looi","doi":"10.1016/j.imu.2025.101700","DOIUrl":"10.1016/j.imu.2025.101700","url":null,"abstract":"<div><div>Accurate and interpretable HER2 IHC scoring is crucial for guiding breast cancer treatment. However, manual evaluation remains inconsistent and subjective. This study proposes a deep learning framework that integrates both a custom Convolutional Neural Network (CNN) and a fine-tuned DenseNet121 model for automated HER2 scoring using the HER-IHC-40x dataset. Preprocessing involves HSV-based patch filtering and expert validation to ensure data relevance. To improve transparency and address the black-box nature of AI models, we employed explainable AI (XAI) techniques. Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) provide visual explanations at the pixel and region levels. These techniques enhance interpretability, ensuring clinical confidence by clearly visualizing and attributing model decisions, particularly in borderline HER2 cases (Class 1+ and 2+), where manual misinterpretations are common. The experimental results show that both CNN and DenseNet121 achieved 93% accuracy with excellent class-wise consistency. CNN, in particular, demonstrated higher prediction confidence and lower training loss, indicating superior calibration. The integration of explainability modules ensures improved clinical transparency and improves trust in AI-driven decision-making. Comparison with the existing literature confirms the strength of the proposed method in predictive capacity and interpretability, contributing to a robust AI-assisted breast cancer diagnosis.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101700"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression and classification of Windkessel parameters from non-invasive cardiovascular quantities using a fully connected neural network","authors":"Ahmed Gdoura ,&nbsp;Stefan Bernhard","doi":"10.1016/j.imu.2025.101614","DOIUrl":"10.1016/j.imu.2025.101614","url":null,"abstract":"<div><div>Despite their simplicity, three-element Windkessel models (WK-3) provide an effective and straightforward representation of the aortic input impedance. The WK-3 model not only captures valuable information about the mechanical and structural characteristics of the aortic arch but also generates reliable estimations of the central blood pressure (cBP) wave, a significant cardiovascular risk indicator. However, fitting the parameters of the WK-3 model typically requires invasively collected data, which carries substantial risk and high cost for patients.</div><div>This study aims to enable non-invasive impedance estimation of the WK-3 model using cardiovascular signals. As a proof of concept, we developed and trained a fully connected neural network (FCNN) on an in-silico dataset to predict the WK-3 parameters: characteristic impedance, peripheral arterial resistance, and arterial compliance. These predictions are based on non-invasive parameters, including zero-flow pressure intercept, heart rate, stroke volume, and left ventricular ejection time.</div><div>The proposed model achieved an overall accuracy of 80% with an average area under the curve (AUC) of <span><math><mrow><mn>0</mn><mo>.</mo><mn>91</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>11</mn></mrow></math></span>. The implementation and best-fitting model are available for download from <span><span>this link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101614"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A longitudinal analysis of morphological shape variation of spleen in patients with fontan surgery","authors":"Varatharajan Nainamalai ,&nbsp;Håvard Bjørke Jenssen ,&nbsp;Mostafa Rezaeitaleshmahalleh ,&nbsp;Djinaud Prophete ,&nbsp;Jordan Gosnell ,&nbsp;Sarah Khan ,&nbsp;Marcus Haw ,&nbsp;Jingfeng Jiang ,&nbsp;Joseph Vettukattil","doi":"10.1016/j.imu.2025.101665","DOIUrl":"10.1016/j.imu.2025.101665","url":null,"abstract":"<div><h3>Background</h3><div>Splenic size serves as a surrogate biomarker for predicting portal vein hyper-tension and liver abnormalities in subjects with Fontan Associated Liver Disease (FALD). We analyze the long-term shape variation of the spleen in FALD subjects using morphological shape features of radiomic features.</div></div><div><h3>Methods</h3><div>We used 154 (84 from computed tomography and 70 from magnetic resonance) image volumes obtained from 36 individuals who underwent stage 3 Fontan procedure and 145 computed tomography images from controls to assess splenomegaly. To understand the splenomegaly, thirteen shape features of the spleen over three 10-year intervals, and variations between controls and FALD subjects were analyzed.</div></div><div><h3>Results</h3><div>The spleen enlargement was observed in all intervals of the post-surgery period. Also, a significant difference (level of significance <em>α</em> = 0.05, <em>p &lt; α</em>) was observed between the morphological shape features of controls and the Fontan Associated Liver Disease subjects.</div></div><div><h3>Conclusion</h3><div>Morphological shape features clearly distinguish between controls and subjects after Fontan stage 3 correction.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101665"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations","authors":"Maryem Ouhmouk ,&nbsp;Shakuntala Baichoo ,&nbsp;Mounia Abik","doi":"10.1016/j.imu.2025.101679","DOIUrl":"10.1016/j.imu.2025.101679","url":null,"abstract":"<div><div>Artificial intelligence, particularly deep learning, is becoming increasingly prominent in multi-omics research, especially since traditional statistical models struggle to handle the complexity and high dimensionality of such data. By effectively combining different types of omics data, AI techniques can unveil hidden connections, detect biomarkers, and improve disease prediction through the integration of multi-omics layers and modalities, which can lead to significant advancements in precision medicine. In this review, we gathered published methods of deep learning-based multi-omics integration specialized in oncology since 2020. We concentrated exclusively on studies utilizing cancer omics data mainly sourced from The Cancer Genome Atlas (TCGA) database. As a result, we identified 32 articles that generally fulfilled the criteria. We studied their techniques and their ability to handle challenges in analyzing multi-omics data, particularly regarding missing data, dimensionality, and processing workflows. We also discuss how well these methods consider explainability, interpretability, and ethical aspects in developing solutions that treat private medical and sensitive information.</div><div>From the 32 studies, we can divide deep learning-based multi-omics integration methods into two types: non-generative and generative models. Non-generative approaches, such as feedforward neural networks (FFNs), graph convolutional networks (GCNs), and autoencoders, are designed to extract features and perform classification directly. On the other hand, generative methods such as variational autoencoders (VAEs), generative adversarial networks (GANs), and generative pretrained transformers (GPTs) focus on creating adaptable representations that can be shared across multiple modalities. These methods have advanced the handling of missing data and dimensionality, outperforming traditional approaches. However, most reviewed models remain at the proof-of-concept stage, with limited clinical validation or real-world deployment.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101679"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography","authors":"Germán Enrique Galvis Ruiz ,&nbsp;Johana Benavides-Cruz ,&nbsp;Daniela Muñoz Corredor ,&nbsp;Esteban Morales-Mendoza ,&nbsp;Héctor Daniel Alejandro Cotrino Palma ,&nbsp;Andrés Cely-Jiménez","doi":"10.1016/j.imu.2024.101605","DOIUrl":"10.1016/j.imu.2024.101605","url":null,"abstract":"<div><div>Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (<span><math><mrow><mi>n</mi><mo>=</mo><mn>500</mn></mrow></math></span>), atelectasis (<span><math><mrow><mi>n</mi><mo>=</mo><mn>499</mn></mrow></math></span>); and images without opacities (<span><math><mrow><mi>n</mi><mo>=</mo><mn>298</mn></mrow></math></span>). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101605"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust assessment of cervical precancerous lesions from pre- and post-acetic acid cervicography by combining deep learning and medical guidelines","authors":"Siti Nurmaini ,&nbsp;Patiyus Agustiyansyah ,&nbsp;Muhammad Naufal Rachmatullah ,&nbsp;Firdaus Firdaus ,&nbsp;Annisa Darmawahyuni ,&nbsp;Bambang Tutuko ,&nbsp;Ade Iriani Sapitri ,&nbsp;Anggun Islami ,&nbsp;Akhiar Wista Arum ,&nbsp;Rizal Sanif ,&nbsp;Irawan Sastradinata ,&nbsp;Legiran Legiran ,&nbsp;Radiyati Umi Partan","doi":"10.1016/j.imu.2024.101609","DOIUrl":"10.1016/j.imu.2024.101609","url":null,"abstract":"<div><div>Cervical cancer remains a major public health challenge, particularly in low-resource settings where access to regular screening and expert medical evaluation is limited. Traditional visual inspection with acetic acid (VIA) has been widely used for cervical cancer screening but is subjective and highly dependent on the expertise of the healthcare provider. This study presents a comprehensive methodology for decision-making regarding cervical precancerous lesions using cervicograms taken before and after the application of acetic acid. By leveraging the power of the deep learning (DL) model with You Only Look Once (Yolo) version 8, Slicing Aided Hyper Inference (SAHI), and oncology medical guidelines, the system aims to improve the accuracy and consistency of VIA assessments. The method involves training a Yolov8xl model on our cervicogram dataset, annotated by two oncologists using VIA screening results, to distinguish between the cervical area, columnar area, and lesions. The model is designed to process cervicography images taken both before and after the application of acetic acid, capturing the dynamic changes in tissue appearance indicative of precancerous conditions. The automated evaluation system demonstrated high sensitivity and specificity in detecting cervical lesions with 90.78 % accuracy, 91.67 % sensitivity, and 90.96 % specificity, outperforming other existing methods. This work represents a significant step towards deploying AI-driven solutions in cervical cancer screening, potentially reducing the global burden of the disease. It can be integrated into existing screening programs, providing a valuable tool for early detection and intervention, especially in regions with limited access to trained medical personnel.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101609"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the onset of the RSV epidemic with meteorological data using deep neural networks","authors":"Kazuo Yonekura ,&nbsp;Miya Nishio ,&nbsp;Momoko Kashiwado ,&nbsp;Takuya Naruto ,&nbsp;Masaaki Mori","doi":"10.1016/j.imu.2025.101659","DOIUrl":"10.1016/j.imu.2025.101659","url":null,"abstract":"<div><h3>Background</h3><div>Respiratory syncytial virus (RSV) is a contagious virus that infects nearly all children by the age of two and is a leading cause of hospitalization and mortality among young children. Despite the recent approval of RSV vaccines for elderly and pregnant individuals, immune prophylaxis remains essential for pediatric cases. In Japan, the typical RSV season has shifted, making timely prediction crucial for effective clinical intervention.</div></div><div><h3>Objective</h3><div>This study aims to predict the onset of RSV epidemics in Japan using meteorological data, based on the hypothesis that meteorological data affect the spread of RSV.</div></div><div><h3>Methods</h3><div>We collected weekly RSV case counts from the Japanese National Institute of Infectious Diseases and daily meteorological data from the Japan Meteorological Agency for the period 2012–2023. Using aggregated weather features (mean, max, min), we constructed a binary classification task to identify the onset of RSV spread. Machine learning models including a support vector machine (SVM), XGBoost, and a deep neural network (DNN) were evaluated.</div></div><div><h3>Results</h3><div>The DNN outperformed other models, achieving the highest F1 score (0.71) and recall (0.83), particularly with a 3-week-ahead prediction horizon. The model demonstrated early detection capability across multiple prefectures, although performance varied geographically, with lower F1 scores in some northern regions.</div></div><div><h3>Conclusion</h3><div>Meteorological data can be effectively utilized to predict the onset of RSV epidemics in Japan. The proposed DNN-based model offers a promising tool for supporting timely prophylactic measures, although further refinement and integration of additional factors are needed to improve generalizability.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101659"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}