Intelligence-based medicine最新文献

{"title":"Oral cancer segmentation and identification system based on histopathological images using MaskMeanShiftCNN and SV-OnionNet","authors":"R. Dharani ,&nbsp;K. Danesh","doi":"10.1016/j.ibmed.2024.100185","DOIUrl":"10.1016/j.ibmed.2024.100185","url":null,"abstract":"<div><h3>Background</h3><div>Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer and a significant threat to public health because of its high mortality rate. Early detection of OSCC is crucial for successful treatment and improved survival rates, but traditional diagnostic methods, such as biopsy, are time-consuming and require expert analysis. Deep learning algorithms have shown promise in detecting various cancers, including OSCC. However, accurately detecting OSCC on histopathological images remains challenging because of tumor heterogeneity.</div></div><div><h3>Methods</h3><div>This study proposes two new deep learning approaches, MaskMeanShiftCNN and SV-OnionNet, for segmenting and identifying OSCC. MaskMeanShiftCNN uses color, texture, and shape features to segment OSCC regions from input images, while SV-OnionNet is suitable for identifying OSCC at an early stage from histopathological images.</div></div><div><h3>Results</h3><div>The proposed approaches outperformed existing methods for OSCC detection, achieving a classification accuracy of 98.94 %, sensitivity of 98.96 %, specificity of 97.18 %, and error rate of 1.05 %. These results demonstrate the effectiveness of the proposed approaches in accurately detecting OSCC and potentially improving the efficiency of OSCC diagnosis.</div></div><div><h3>Conclusion</h3><div>The proposed deep learning approaches, MaskMeanShiftCNN and SV-OnionNet accurately detected OSCC in input and histopathological images. These approaches can improve the efficiency and accuracy of OSCC diagnosis, ultimately improving patient outcomes.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554775","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":"Cascaded regression with dual CNN frame work for time effective detection of gliomas cancers","authors":"V.K. Deepak ,&nbsp;R. Sarath","doi":"10.1016/j.ibmed.2024.100168","DOIUrl":"10.1016/j.ibmed.2024.100168","url":null,"abstract":"<div><div>The determination of brain tumor growth primarily relies on the histopathological examination of biopsy samples. Tumor segmentation in the brain presents a significant challenge in medical image analysis due to its complexity. The ultimate goal is to accurately identify and isolate tumor regions. For the segmentation of brain tumors, a variety of deep-learning techniques have been developed, and they have produced promising results. However, achieving accurate segmentation requires the integration of multiple image modalities with varying contrasts. This makes manual segmentation impractical for larger studies, despite its accuracy. Deep learning's exceptional performance has made it an attractive method for quantitative analysis. The field of medical image analysis presents distinctive challenges that must be overcome to achieve optimal results. The ongoing strategy is obtrusive, tedious and inclined to manual mistakes. These weaknesses show that it is so fundamental to play out a completely computerized technique for the multi-characterization of cerebrum cancers in view of deep learning. Thus, this paper presents an efficient time-optimized and deep-learning model based on cascade regression (DLCR) to segment the tumor grade in the following stages: Data Acquisition in which data were obtained from the well-known brain repository BRATS2017, which included 215 HGG (High-Grade Gliomas) and 80 LGG (Low-Grade Gliomas) glioma cases. Fully Convolutional Neural Network (FCNN) preprocessing was used to remove noise and anomalies from the raw data, and Gaussian Mixture Model feature extraction was used to extract features from the preprocessed image and finally the proposed DLCR model for grade identification. Experimental findings indicate that the suggested system surpasses other pre-existing models in various aspects (accuracy: 0.96, sensitivity:0.97, precision:0.88).</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532837","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 multioutput classifier model for breast cancer treatment prediction","authors":"Emad Abd Al Rahman ,&nbsp;Nur Intan Raihana Ruhaiyem ,&nbsp;Majed Bouchahma","doi":"10.1016/j.ibmed.2024.100158","DOIUrl":"10.1016/j.ibmed.2024.100158","url":null,"abstract":"<div><p>A growing number of new cases and fatalities occur each year due to breast cancer, making it the most frequent malignancy globally. Utilizing a multioutput classifier technique with algorithms such as CatBoost, XGBoost, NN, and NN Binary, this work presents a new model for predicting breast cancer treatments: surgery, radiotherapy, and chemotherapy. We tackle the pressing need for accurate medical treatments by developing a model to enhance the predicted accuracy of breast cancer treatment outcomes. The model accomplishes impressive results in predicting surgical outcomes; in particular, Neural Networks (NN and NN Binary) perform exceptionally well in terms of recall and precision, reaching 97 % accuracy and 98 % F1-scores. While the model's accuracy is only about 63 % for radiotherapy, it shows a promising recall of up to 84 %. Accuracy and precision in chemotherapy predictions remain stable at 82 %, with AUC-ROC values of up to 89 %, suggesting excellent discrimination ability. By combining multioutput classifiers with sophisticated algorithms, we hope to make treatment prediction models more tailored to individual breast cancer patient profiles, which might usher in a new era of tailored treatment plans and meet the rising demand for precision medicine in cancer care.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000255/pdfft?md5=495fcee4686f4acc2b598a0adea6e4ab&pid=1-s2.0-S2666521224000255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839308","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 skin cancer invasiveness: A comparative study among the regions of Brazil","authors":"Marcus Augusto Padilha Mata,&nbsp;Plinio Sa Leitao-Junior","doi":"10.1016/j.ibmed.2024.100157","DOIUrl":"10.1016/j.ibmed.2024.100157","url":null,"abstract":"<div><h3>Context</h3><p>Skin cancer is the most incident neoplasia in Brazil, and their invasiveness can be impacted by various factors, including geographical aspects. Identifying these factors is important for improving diagnosis and treatment.</p></div><div><h3>Objective</h3><p>The research focused on analyzing the impact of region on the invasiveness of skin cancer in Brazil, through the identification of regional predictive patterns.</p></div><div><h3>Methods</h3><p>An analysis and processing of data from the Hospital Cancer Registries (RHC) of Brazil's National Cancer Institute (INCA) were conducted, followed by the application of machine learning algorithms. The SHapley Additive exPlanations (SHAP) approach was employed to provide explanations for the developed artificial intelligence models.</p></div><div><h3>Results</h3><p>It was revealed that geography plays a significant role in predicting the invasiveness of skin cancer, reinforcing the need to consider regional specificities in future studies.</p></div><div><h3>Conclusions</h3><p>The study identified that regional characteristics of Brazil impacts the prediction of the invasiveness of skin cancer. Despite limitations, such as the issue of data imbalance, the findings are important for developing more effective policies in the fight against skin cancer in the Brazil.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000243/pdfft?md5=bb190d67c65ebb2d6e3c68d16c1ed3cd&pid=1-s2.0-S2666521224000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842490","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":"AIoT-based embedded systems optimization using feature selection for Parkinson's disease diagnosis through speech disorders","authors":"Shawki Saleh ,&nbsp;Zakaria Alouani ,&nbsp;Othmane Daanouni ,&nbsp;Soufiane Hamida ,&nbsp;Bouchaib Cherradi ,&nbsp;Omar Bouattane","doi":"10.1016/j.ibmed.2024.100184","DOIUrl":"10.1016/j.ibmed.2024.100184","url":null,"abstract":"<div><div>This study aims to build a pre-diagnosis tool for predicting Parkinson's disease based on a speech disorder which appears as a symptom in approximately 90 % of people with this disease. Recently, some technologies such as AIoT and IoMT aim to integrate Artificial Intelligence and the Internet of Things or Internet of Medical Things to provide an intelligent remote diagnosis for enhancing medical services. Thus, the classification speed and reliability of the systems in these fields are highly recommended. In this work, we compared five ML algorithms (LR, RF, XGB, SVM, KNN) based on their performance, classification speed and reliability. We employed the sequential forward feature selection in order to select the optimal relevant feature for reducing the dimensionality of the used acoustic dataset to enhance both the performance and computation cost for the proposed system. Furthermore, the stratified cross-validation approach has been used to obtain a fair estimation for the proposed system across each point in the dataset. In this paper, we used a vocal dataset of Parkinson's disease consisting of 195 samples and 22 features. We found that 10 features provide the optimal performance. So, we proposed the K-Nearest Neighbours algorithm as a classifier for our system. It reached 98.46 %, 99.33 % and 98.67 % of the accuracy, sensitivity and precision respectively. Moreover, this work provides a detailed explanation of the employed techniques and the obtained results. The novelty of this work, compared to the existing literature, is to enhance both computation cost and performance for building a real-world system to diagnose Parkinson's disease through speech disorder.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532613","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":"MedTransCluster: Transfer learning for deep medical image clustering","authors":"Mojtaba Jahanian ,&nbsp;Abbas Karimi ,&nbsp;Nafiseh Osati Eraghi ,&nbsp;Faraneh Zarafshan","doi":"10.1016/j.ibmed.2024.100139","DOIUrl":"10.1016/j.ibmed.2024.100139","url":null,"abstract":"<div><p>This work introduces the “MedTransCluster” framework, a novel approach to medical image clustering in chest radiography through the application of transfer learning, leveraging the capabilities of pre-trained deep learning models. Our evaluation encompassed a variety of neural networks, considering their adaptability to the nuances of medical imaging data. The study incorporated four renowned clustering algorithms and an expanded set of evaluation metrics, offering a comprehensive comparison and a refined analysis of these models’ ability to cluster complex diagnostic features. Notably, EfficientNetB0 coupled with DBSCAN clustering algorithm achieved a silhouette score of 0.924131, and ResNet152 with KMeans displayed a Calinski Harabasz score of 9655.213964, indicating their superior proficiency in capturing the intricacies of medical features. These results emphasize the critical importance of model refinement within the healthcare imaging sphere and underscore the potential of methodologies like MedTransCluster in enhancing diagnostic accuracy and patient outcomes.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000061/pdfft?md5=7177c2ff66f6399232cf11114c5cfa1a&pid=1-s2.0-S2666521224000061-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141057623","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":"Combining a forward supervised filter learning with a sparse NMF for breast cancer histopathological image classification","authors":"ArunaDevi Karuppasamy ,&nbsp;Abdelhamid Abdesselam ,&nbsp;Hamza zidoum ,&nbsp;Rachid Hedjam ,&nbsp;Maiya Al-Bahri","doi":"10.1016/j.ibmed.2024.100174","DOIUrl":"10.1016/j.ibmed.2024.100174","url":null,"abstract":"<div><div>Histopathological images play a important role in clinical diagnosis, particularly in identifying and assessing the severity of abnormal conditions like benign lesions and malignant tumors. Traditional machine learning techniques for processing histopathology images involve the extraction of manual features from these images, which is typically done with the assistance of industry experts. Recent advancements in Deep Learning (DL), especially with Convolutional Neural Networks (CNN), have enabled the automatic extraction of multi-level abstract features directly from raw data. This capability significantly enhances the performance of complex computer vision tasks. Classic CNN models like AlexNet and VggNet employ back-propagation algorithms to learn filters in the training phase. However, these algorithms demand large labeled datasets, resulting in extensive computational processing. Additionally, they often face the vanishing gradient problem, which can negatively impact the quality of the learning process. Besides, in many domains, acquiring enough labeled images for conducting properly the training phase is a real challenge. To address these challenges, a feed-forward propagation approach was proposed using Non-Negative Matrix Factorization(NMF). The NMF technique factorizes the input data into two latent factors (non-negative matrices). It has been shown that by enforcing constraints such as sparsity on the latent factors, dominant features that are mostly correlated with tumors types can be extracted. In this work, a novel model combining sparse NMF and Support Vector Machine (SVM) was developed for classifying histopathological images. We have derived a mathematical model of a novel feed-forward filter learning approach that combines sparse NMF (SNMF) and Support Vector Machine technique (SVM). The model was used to design and implement a feed-forward CNN classifier to classify histopathology images. This model has been evaluated on the histopathology images from Sultan Qaboos University Hospital (SQUH dataset) and the public BreaKHis dataset. The experiments we have conducted demonstrate the efficiency of the proposed model, especially on small-sized SQUH datasets achieving an AUC of 0.90, 0.89, 0.85, and 0.86 on 4x,10x, 20x, and 40x magnifications, respectively, and achieving an AUC of 0.95 BreaKHis dataset.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100174"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532835","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":"Hyperplastic and tubular polyp classification using machine learning and feature selection","authors":"Refika Sultan Doğan ,&nbsp;Ebru Akay ,&nbsp;Serkan Doğan ,&nbsp;Bülent Yılmaz","doi":"10.1016/j.ibmed.2024.100177","DOIUrl":"10.1016/j.ibmed.2024.100177","url":null,"abstract":"<div><h3>Purpose</h3><div>The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms.</div></div><div><h3>Methods</h3><div>This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers.</div></div><div><h3>Results</h3><div>The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower.</div></div><div><h3>Conclusion</h3><div>This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446010","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":"Real-time artificial intelligence validation of critical view of safety in laparoscopic cholecystectomy","authors":"George Leifman ,&nbsp;Tomer Golany ,&nbsp;Ehud Rivlin ,&nbsp;Wisam Khoury ,&nbsp;Ahmad Assalia ,&nbsp;Petachia Reissman","doi":"10.1016/j.ibmed.2024.100153","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100153","url":null,"abstract":"<div><h3>Background</h3><p>Critical View of Safety (CVS) is the accepted strategy to avoid bile duct injury during Laparoscopic Cholecystectomy (LC). In this study, we sought to investigate the accuracy and performance of a trained Artificial Intelligent (AI) model in validation of the CVS achievement during elective LC in a real time operating room setting.</p></div><div><h3>Study design</h3><p>A deep learning neural network which was previously trained on annotated segments of 700 LC videos to identify the CVS criteria, was integrated into the operating room laparoscopic video system, for continuous monitoring and real-time validation of CVS achievement during elective LC procedures. The system's feedback and surgeon's report were recorded and compared, as well as the overall rate of CVS achievement.</p></div><div><h3>Results</h3><p>Of 40 consecutive LC, CVS was reported by the surgeons in 34 (85 %). In all the 6 cases where CVS was not achieved due to severe inflammation or anatomy distortion, the AI model agreed with surgeon's report and did not identify CVS. Out of the 34 cases where CVS was achieved, the AI model identified 33. Thus, the AI model detected the CVS achievement with a specificity of 100 % [95%-CI 98.1 %, 100 %] and sensitivity of 97 % [95%-CI 96.1 %, 98.2 %].</p></div><div><h3>Conclusions</h3><p>A trained AI model can identify CVS during elective LC with very high accuracy in a real time OR setting. Additionally, its use may result in high rates of CVS achievement, thereby improving LC procedure's safety and outcome.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000206/pdfft?md5=f07707d889089060ef7b66be4c734e24&pid=1-s2.0-S2666521224000206-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605524","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":"Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation","authors":"Nathan Phelps ,&nbsp;Stephanie Marrocco ,&nbsp;Stephanie Cornell ,&nbsp;Dalton L. Wolfe ,&nbsp;Daniel J. Lizotte","doi":"10.1016/j.ibmed.2024.100137","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100137","url":null,"abstract":"<div><p>Reinforcement learning (RL) has helped improve decision-making in several domains but can be challenging to apply; this is the case for rehabilitation of people with a spinal cord injury (SCI). Among other factors, applying RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few detailed records of longitudinal treatments and outcomes (i.e., limited training data). Applying Fitted Q Iteration in this domain with linear models and the most natural state and action representation results in problems with convergence and overfitting. However, isolating treatments from one another can mitigate the convergence issue, and treatments for SCIs have meaningful groupings that can be used to combat overfitting. We propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. After re-interpreting the data using these treatment grouping approaches in conjunction with our process that isolates the treatment groups, we use Fitted Q Iteration to train an agent that learns to select better treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that agents trained after using either grouping method can help improve the treatment decisions of individual physiotherapists, but the approach based on domain knowledge offers better performance. Our findings provide a proof of concept that applying RL has the potential to help improve the treatment of those with an SCI and indicates that continued efforts to gather data and apply RL to this domain are worthwhile.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000048/pdfft?md5=0e9b4fe44a6fce7ea5f3e30e6224f595&pid=1-s2.0-S2666521224000048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291377","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}