Intelligence-based medicine最新文献

{"title":"Hybrid deep learning and active contour approach for enhanced breast lesion segmentation and classification in mammograms","authors":"Abdala Nour,&nbsp;Boubakeur Boufama","doi":"10.1016/j.ibmed.2025.100224","DOIUrl":"10.1016/j.ibmed.2025.100224","url":null,"abstract":"<div><div>Accurate segmentation and classification of breast lesions in mammography images are crucial steps in effective breast cancer screening and diagnosis. This study presents a hybrid deep learning and active contour approach to automated mammogram analysis. The proposed methodology leverages the powerful feature extraction capabilities of deep convolutional neural networks and the precise boundary delineation of active contour models. A U-Net is trained on a large dataset of mammogram images to learn discriminative features and generate initial segmentation masks for breast lesions. Subsequently, an active contour refinement stage is employed to fine-tune the segmentation boundaries and enhance lesion delineation accuracy. This integration of active contour models (ACM) with deep learning techniques overcomes traditional image segmentation limitations. Morphological operations and energy minimization techniques are applied to the initial segmentation mask, resulting in highly accurate and refined lesion segmentation. This study investigates the synergistic integration of deep learning with Adaptive Contour Modeling for breast lesion segmentation. Our proposed U-Net_ACM model leverages the strengths of both approaches, demonstrating state-of-the-art performance and outperforming methods relying solely on deep learning or traditional image processing techniques. Evaluation on a test set reveals a 97.34 % accuracy, a Dice coefficient of 0.813, and an Intersection over Union of 0.891 for the U-Net_ACM model. These results surpass the performance of established pre-trained deep learning models such as VGG16, VGG19, and DeepLabV3, highlighting the benefits of the combined approach. This hybrid methodology offers a robust, automated solution for mammogram analysis, potentially improving breast cancer screening outcomes. The superior segmentation quality and overall performance demonstrated by the U-Net_ACM model suggest its potential for enhancing breast cancer screening and diagnosis in clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454805","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":"Machine learning to predict haemorrhage after injury: So many models, so little dynamism","authors":"Greta Safoncik ,&nbsp;Yeswanth Akula ,&nbsp;Jared M. Wohlgemut ,&nbsp;Allan Pang ,&nbsp;Max Marsden","doi":"10.1016/j.ibmed.2025.100241","DOIUrl":"10.1016/j.ibmed.2025.100241","url":null,"abstract":"<div><div>Accurately predicting the need for blood transfusion in bleeding patients remains a critical challenge in emergency care. Machine learning (ML) models show promise for improving decision support in these scenarios, but a gap remains between research and practical application. Existing models frequently overlook the dynamic nature of clinical data, hindering their ability to provide accurate predictions for blood transfusion needs in emergency settings. We conducted a scoping review to examine ML models that integrate time-varying variables to predict blood transfusion needs in trauma patients. We discuss challenges in data collection, particularly the limitations of electronic health records (EHRs) in capturing high-quality time-series data and emphasise the need for explainable artificial intelligence (AI). We suggest future directions for research that include advancing computational approaches, improving data collection, and enhancing the interpretability of ML models to ensure their clinical relevance and utility.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783419","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":"New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data","authors":"R. Kishore Kanna ,&nbsp;Ayodeji Olalekan Salau","doi":"10.1016/j.ibmed.2025.100215","DOIUrl":"10.1016/j.ibmed.2025.100215","url":null,"abstract":"<div><div>The brain is one of the most important organs in the human body. It governs all actions whether one is aware of the action or not. Brain tumors occur when the system of cell division in the brain is disrupted. Brain tumors are frequently associated with severe malignancies worldwide. The uncontrolled accumulation and growth of these cells can lead to the formation of seizures or tumors with impaired brain function.</div><div>Magnetic resonance imaging (MRI) is a common technology used to detect brain lesions; however, manual analysis of MRI images by physicians is challenging due to uncertainty and time constraints. The aim of this paper is to introduce machine learning (ML) algorithms designed to increase the speed and cognitive statistical methods for brain tumor classification.</div><div>In this study, we proposed a novel penguin search-optimized quantum-enhanced support vector machine (PSO-QESVM) to categorize brain tumor using MRI data. We used a publicly accessible brain MR image dataset for brain tumor classification tasks which we obtained from an online source. A median filter (MF) was used as part of the pre-processing step to eliminate noise from the data. Using ResNet and VGG16, features were extracted from the pre-processed data.</div><div>The proposed method was implemented using Python 3.7+ software. A comparison was made between the suggested approach and other conventional algorithms. The results show the proposed method achieved a superior efficiency with regards to recall (98.9 %), accuracy (98.90 %), f1-score (98.5 %), and precision (98.7 %).</div><div>The study demonstrated the applicability of the suggested strategy for brain tumor classification. The suggested cognitive computational strategy achieved a promising performance. To reduce the size of the model and implement it on a real-time medical diagnosis framework, we intend to employ knowledge distillation techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175220","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":"Interrogation of coded healthcare data to facilitate identification of patients with a rare neurotransmitter disorder; Aromatic L-Amino acid decarboxylase deficiency","authors":"M. Monaghan ,&nbsp;A. Ahmad ,&nbsp;G. Aldersley ,&nbsp;A. Basu ,&nbsp;E. Chakkarapani ,&nbsp;Y. Collins-Sawaragi ,&nbsp;I. Dey ,&nbsp;R. Grayson ,&nbsp;K. Forrest ,&nbsp;A. Lording ,&nbsp;D. Ram ,&nbsp;M. Taylor ,&nbsp;T. Thazin ,&nbsp;E. Wassmer ,&nbsp;S. Amin","doi":"10.1016/j.ibmed.2025.100225","DOIUrl":"10.1016/j.ibmed.2025.100225","url":null,"abstract":"<div><h3>Background and aims</h3><div>Aromatic L-Amino Acid Decarboxylase deficiency (AADCd) is a rare, phenotypically heterogenous neurotransmitter disorder, posing challenges for diagnosis. We aimed to assess the efficacy and acceptability of interrogating routine hospital data to identify possible AADCd patients.</div></div><div><h3>Methods</h3><div><strong>Design:</strong> Mixed methods feasibility study.</div><div><strong>Setting:</strong> UK Secondary and tertiary care hospitals.</div><div><strong>Procedure:</strong> A Structured Query Language (SQL) query was applied to hospital datasets to produce filtered lists of patients, ranked according to the presence of AADCd-consistent diagnostic and procedural codes. Findings were collected using a study proforma. No patient data was reported to the research team.</div></div><div><h3>Results</h3><div>Seven sites (five tertiary) participated. Data collection spanned June 01, 2022 to October 31, 2023. 340 medical records were reviewed, of which 76 patients had previously been investigated for a possible neurotransmitter disorder, 4 were currently being investigated, 31 were suitable for investigation, with 9 subsequently approached for further testing. No patients were identified as having AADCd. Thematic feedback included accuracy and technical, application challenges. Three sites reported this method could help identify AADCd patients.</div></div><div><h3>Conclusions</h3><div>Medical record interrogation to identify potential AADCd patients is feasible. Challenges including operational capacity, technical issues and uncertainty regarding efficacy remain.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488627","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":"DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification","authors":"Mohammad Amin ,&nbsp;Khalid M.O. Nahar ,&nbsp;Hasan Gharaibeh ,&nbsp;Ahmad Nasayreh ,&nbsp;Neda'a Alsalmanc ,&nbsp;Alaa Alomar ,&nbsp;Majd Malkawi ,&nbsp;Noor Alqasem ,&nbsp;Aseel Smerat ,&nbsp;Raed Abu Zitar ,&nbsp;Shawd Nusier ,&nbsp;Absalom E. Ezugwu ,&nbsp;Laith Abualigah","doi":"10.1016/j.ibmed.2024.100192","DOIUrl":"10.1016/j.ibmed.2024.100192","url":null,"abstract":"<div><div>Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174360","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":"CFMKGATDDA: A new collaborative filtering and multiple kernel graph attention network-based method for predicting drug-disease associations","authors":"Van Tinh Nguyen,&nbsp;Duc Huy Vu,&nbsp;Thi Kim Phuong Pham,&nbsp;Trong Hop Dang","doi":"10.1016/j.ibmed.2024.100194","DOIUrl":"10.1016/j.ibmed.2024.100194","url":null,"abstract":"<div><div>Drug-disease association prediction is increasingly recognized as crucial for a comprehensive understanding of the functions and mechanisms of drugs. However, the process of obtaining approval for a new drug to deal with a disease is often laborious, time-consuming and expensive. As a consequence, there is a growing interest among researchers from diverse fields in developing computational methods to identify drug-disease interactions. Thus, in this work, a new CFMKGATDDA method was proposed to unveil drug-disease associations. It firstly uses a collaborative filtering algorithm for mitigating the impact sparse associations. It secondly provides a new way to fuse multiple similarities of drugs and diseases to obtain integrated similarities for drugs and diseases. Finally, it learns drugs and diseases’ embeddings by combining multiple kernels and graph attention networks to predict high quality drug-disease associations. It attains a noticeable performance of drug-disease interaction prediction with remarkable averaged AUC and AUPR values of 0.9931 and 0.9334, respectively, on the Cdataset. When comparing on the same Cdataset, it outperforms other approaches in both metrics of AUC and AUPR. Thus, it can be regarded a useful tool for revealing drug-disease associations.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174361","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":"ASDvit: Enhancing autism spectrum disorder classification using vision transformer models based on static features of facial images","authors":"Hayder Ibadi,&nbsp;Amir Lakizadeh","doi":"10.1016/j.ibmed.2025.100226","DOIUrl":"10.1016/j.ibmed.2025.100226","url":null,"abstract":"<div><div>This study embarks on an exploratory journey into autism spectrum disorder (ASD), a multifaceted neurological developmental disorder with a spectrum of manifestations. Recognizing the transformative impact of early diagnosis and tailored medical interventions on the lives of children diagnosed with ASD and their families, The intersection of early diagnosis and tailored medical intervention can substantially enhance the quality of life for children diagnosed with ASD and their families. This study embarks on an innovative approach to augmenting the diagnostic process, specifically through the analysis of static features extracted from facial photographs of autistic children. By employing Vision Transformers (ViT) enhanced with Squeeze-and-Excitation (SE) blocks, our research delves into the potential of facial features as a biomarker for distinguishing autistic children from their typically developing counterparts. The fusion of ViT with SE mechanisms aims to amplify the model's sensitivity toward the subtle yet diagnostically crucial facial cues associated with ASD. Through comprehensive experimentation on a curated dataset, categorized into “autistic” and “non-autistic” groups, our approach demonstrates remarkable proficiency in identifying ASD, thereby opening new avenues for employing facial image analysis as a scalable biomarker in ASD diagnosis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488628","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":"Development of binary-based prediction models for colorectal polyps","authors":"Aaron Morelos-Gomez ,&nbsp;Kohjiro Tokutake ,&nbsp;Ken-ichi Hoshi ,&nbsp;Akira Matsushima ,&nbsp;Armando David Martinez-Iniesta ,&nbsp;Michio Katouda ,&nbsp;Syogo Tejima ,&nbsp;Morinobu Endo","doi":"10.1016/j.ibmed.2025.100236","DOIUrl":"10.1016/j.ibmed.2025.100236","url":null,"abstract":"<div><h3>Background and aims</h3><div>Even though several colorectal cancer (CRC) screening strategies can lower CRC mortality, screening rates remain low. Removing polyps to achieve a clean colon is effective in preventing CRC. This study evaluated the possibility of using artificial intelligence to select features and threshold values required to construct an optimal screening model to prevent colorectal neoplasia.</div></div><div><h3>Methods</h3><div>The collected data consisted of medical check-ups, blood analysis, demographics, colonoscopy observations, and fecal immunochemical test (FIT). The data was divided according to sex and used to construct a screening model that converted each feature into a zero or a one based on a threshold value obtained through particle swarm optimization and the best group of features was selected by sequential combinations. Three optimization targets were evaluated: Mathew's correlation coefficient, the area under the curve, and the minimum between sensitivity and specificity.</div></div><div><h3>Results</h3><div>Using the minimum between sensitivity and specificity as an optimization target the obtained models yielded better overall prediction metrics. The optimization algorithm selected three features for women and ten features for men. The optimized models for both sexes agree that obesity is determinant for predicting polyps according to the selected features. In addition, both models outperform traditional FIT which is used for colorectal cancer screening.</div></div><div><h3>Conclusions</h3><div>The developed algorithm is effective in creating polyp screening models for men and women based on medical data with higher prediction metrics than FIT. In addition, the obtained threshold values and prediction probability can act as a guide for medical practitioners.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697552","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":"Development of an explainable machine learning model for predicting neurological deterioration in spontaneous intracerebral hemorrhage","authors":"Ming Jie, Jonathan Yeo ,&nbsp;Chun Peng Goh ,&nbsp;Christine Xia Wu ,&nbsp;Francis Phng ,&nbsp;Ping Yong ,&nbsp;Shiong Wen Low","doi":"10.1016/j.ibmed.2025.100237","DOIUrl":"10.1016/j.ibmed.2025.100237","url":null,"abstract":"<div><h3>Background</h3><div>Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. Early prediction of neurological deterioration (ND)—defined as a decline of at least 2 points on the Glasgow Coma Scale (GCS) within 48 h of admission or mortality at discharge—is essential for timely intervention and improved outcomes.</div></div><div><h3>Methods</h3><div>We developed an explainable machine learning model to predict ND using clinical, laboratory, and radiological data extracted from electronic medical records (EMR) of a retrospective cohort of 491 ICH patients, with ND observed in 52.3 % of cases. Multiple machine learning algorithms—including random forests, extra trees, and CatBoost—were trained, and model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC-ROC) and F1-score. Shapley Additive Explanations (SHAP) were employed to enhance interpretability.</div></div><div><h3>Results</h3><div>The final model, a blended ensemble, achieved an AUC-ROC of 0.8743, an F1-score of 0.8077, and a sensitivity of 0.8182 on the test set. Key predictors included initial GCS, hematoma volume, age, and the presence of intraventricular hemorrhage. SHAP analysis provided insights into the relative contributions of these predictors, reinforcing the model's clinical relevance.</div></div><div><h3>Conclusions</h3><div>Our model demonstrates promising predictive performance, suggesting its potential utility for early risk stratification and guiding interventions in ICH management. Further validation in diverse clinical settings is warranted.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725919","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":"“Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review.”","authors":"Peace Ezeobi Dennis ,&nbsp;Angella Musiimenta ,&nbsp;Wasswa William ,&nbsp;Stella Kyoyagala","doi":"10.1016/j.ibmed.2024.100195","DOIUrl":"10.1016/j.ibmed.2024.100195","url":null,"abstract":"<div><div>About 2.9 million neonates die every year worldwide, and most of these deaths occur in low resource settings where it causes about 30–50 % of the total neonatal deaths annually. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, timely diagnosis is critical. The gold standard test for diagnosing neonatal sepsis is blood culture, which takes at least 72 h. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality.</div><div>Matching articles were identified by searching PubMed, IEEE, and Cochrane bibliography databases. Full-text articles with the following criteria were included for analysis based on 1) the subject population are neonates. 2) the study provided a clear definition of neonatal sepsis. 3) the study provides neonatal sepsis onset definition (i.e., time of onset). 4) the study clearly described the predictor variables used. 5) the study clearly described machine learning models used or evaluated any of the consolidated screening parameters. 6) the study must have provided diagnostic performance results. Thirty-one studies met full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests.</div><div>A combination of predictor variables has shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174756","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}