Journal of X-Ray Science and Technology最新文献

{"title":"Three-dimensional analysis of puncture needle path through safety triangle approach PLD and design of puncture positioning guide plate.","authors":"Penghui Yu, Yanbing Li, Qidong Zhao, Xia Chen, Liqin Wu, Shuai Jiang, Libing Rao, Yihua Rao","doi":"10.3233/XST-230267","DOIUrl":"10.3233/XST-230267","url":null,"abstract":"<p><strong>Objective: </strong>In this study, the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process was discussed using digital technology. Additionally, the positioning guide plate was designed and 3D printed in order to simulate the surgical puncture of specimens. This plate served as an important reference for the preoperative simulation and clinical application of percutaneous laser decompression (PLD).</p><p><strong>Method: </strong>The CT data were imported into the Mimics program, the 3D model was rebuilt, the ideal puncture line N and the associated central axis M were developed, and the required data were measured. All of these steps were completed. A total of five adult specimens were chosen for CT scanning; the data were imported into the Mimics program; positioning guide plates were generated and 3D printed; a simulated surgical puncture of the specimens was carried out; an X-ray inspection was carried out; and an analysis of the puncture accuracy was carried out.</p><p><strong>Results: </strong>(1) The angle between line N and line M was 42°~55°, and the angles between the line M and 3D plane were 1°~2°, 5°~12°, and 78°~84°, respectively; (2) As the level of the lumbar intervertebral disc decreases, the distance from point to line and point to surface changes regularly; (3) The positioning guide was designed with the end of the lumbar spinous process and the posterior superior iliac spine on both sides as supporting points. (4) Five specimens were punctured 40 times by using the guide to simulate surgical puncture, and the success rate was 97.5%.</p><p><strong>Conclusion: </strong>By analyzing the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process, the guide plate was designed to simulate surgical puncture, and the individualized safety positioning of percutaneous puncture was obtained.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"825-837"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140190348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary artery segmentation in CCTA images based on multi-scale feature learning.","authors":"Bu Xu, Jinzhong Yang, Peng Hong, Xiaoxue Fan, Yu Sun, Libo Zhang, Benqiang Yang, Lisheng Xu, Alberto Avolio","doi":"10.3233/XST-240093","DOIUrl":"10.3233/XST-240093","url":null,"abstract":"<p><strong>Background: </strong>Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy.</p><p><strong>Objective: </strong>A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries.</p><p><strong>Methods: </strong>The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance.</p><p><strong>Results: </strong>The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets.</p><p><strong>Conclusion: </strong>Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"973-991"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reference-free calibration method for asynchronous rotation in robotic CT.","authors":"Xuan Zhou, Yuedong Liu, Cunfeng Wei, Qiong Xu","doi":"10.3233/XST-240023","DOIUrl":"10.3233/XST-240023","url":null,"abstract":"<p><strong>Background: </strong>Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators.</p><p><strong>Objective: </strong>We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model.</p><p><strong>Methods: </strong>We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached.</p><p><strong>Results: </strong>In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality.</p><p><strong>Conclusion: </strong>We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1239-1252"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141601992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomical changes and dosimetric analysis of the neck region based on FBCT for nasopharyngeal carcinoma patients during radiotherapy.","authors":"Aoqiang Chen, Xuemei Chen, Xiaobo Jiang, Yajuan Wang, Feng Chi, Dehuan Xie, Meijuan Zhou","doi":"10.3233/XST-230280","DOIUrl":"10.3233/XST-230280","url":null,"abstract":"<p><strong>Background: </strong>The study aimed to investigate anatomical changes in the neck region and evaluate their impact on dose distribution in patients with nasopharyngeal carcinoma (NPC) undergoing intensity modulated radiation therapy (IMRT). Additionally, the study sought to determine the optimal time for replanning during the course of treatment.</p><p><strong>Methods: </strong>Twenty patients diagnosed with NPC underwent IMRT, with weekly pretreatment kV fan beam computed tomography (FBCT) scans in the treatment room. Metastasized lymph nodes in the neck region and organs at risk (OARs) were redelineation using the images from the FBCT scans. Subsequently, the original treatment plan (PLAN0) was replicated to each FBCT scan to generate new plans labeled as PLAN 1-6. The dose-volume histograms (DVH) of the new plans and the original plan were compared. One-way repeated measure ANOVA was utilized to establish threshold(s) at various time points. The presence of such threshold(s) would signify significant change(s), suggesting the need for replanning.</p><p><strong>Results: </strong>Progressive volume reductions were observed over time in the neck region, the gross target volume for metastatic lymph nodes (GTVnd), as well as the submandibular glands and parotids. Compared to PLAN0, the mean dose (Dmean) of GTVnd-L significantly increased in PLAN5, while the minimum dose covering 95% of the volume (D95%) of PGTVnd-L showed a significant decrease from PLAN3 to PLAN6. Similarly, the Dmean of GTVnd-R significantly increased from PLAN4 to PLAN6, whereas the D95% of PGTVnd-R exhibited a significant decrease during the same period. Furthermore, the dose of bilateral parotid glands, bilateral submandibular glands, brainstem and spinal cord was gradually increased in the middle and late period of treatment.</p><p><strong>Conclusion: </strong>Significant anatomical and dosimetric changes were noted in both the target volumes and OARs. Considering the thresholds identified, it is imperative to undertake replanning at approximately 20 fractions. This measure ensures the delivery of adequate doses to target volumes while mitigating the risk of overdosing on OARs.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"783-795"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140061028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries.","authors":"Wenxiu Li, Fangfang Gou, Jia Wu","doi":"10.3233/XST-230194","DOIUrl":"10.3233/XST-230194","url":null,"abstract":"<p><strong>Background: </strong>In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources.</p><p><strong>Objective: </strong>This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records.</p><p><strong>Methods: </strong>The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records.</p><p><strong>Results: </strong>The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency.</p><p><strong>Conclusions: </strong>The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"395-413"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection.","authors":"Haruyuki Watanabe, Yuina Ezawa, Eri Matsuyama, Yohan Kondo, Norio Hayashi, Sho Maruyama, Toshihiro Ogura, Masayuki Shimosegawa","doi":"10.3233/XST-230431","DOIUrl":"10.3233/XST-230431","url":null,"abstract":"<p><strong>Background: </strong>Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation.</p><p><strong>Objective: </strong>To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation.</p><p><strong>Methods: </strong>Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods.</p><p><strong>Results: </strong>The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%.</p><p><strong>Conclusions: </strong>Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1151-1162"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of benign and malignant pulmonary nodule based on local-global hybrid network.","authors":"Xin Zhang, Ping Yang, Ji Tian, Fan Wen, Xi Chen, Tayyab Muhammad","doi":"10.3233/XST-230291","DOIUrl":"10.3233/XST-230291","url":null,"abstract":"<p><strong>Background: </strong>The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules.</p><p><strong>Objective: </strong>In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules.</p><p><strong>Methods: </strong>First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features.</p><p><strong>Results: </strong>Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%.</p><p><strong>Conclusion: </strong>The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"689-706"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139566189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.","authors":"Ting Fang, Naijia Liu, Shengdong Nie, Shouqiang Jia, Xiaodan Ye","doi":"10.3233/XST-230119","DOIUrl":"10.3233/XST-230119","url":null,"abstract":"<p><strong>Background: </strong>Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments.</p><p><strong>Objective: </strong>We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS.</p><p><strong>Methods: </strong>Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region.</p><p><strong>Results: </strong>The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96).</p><p><strong>Conclusions: </strong>This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"17-30"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensembled CNN with artificial bee colony optimization method for esophageal cancer stage classification using SVM classifier.","authors":"A Chempak Kumar, D Muhammad Noorul Mubarak","doi":"10.3233/XST-230111","DOIUrl":"10.3233/XST-230111","url":null,"abstract":"<p><strong>Background: </strong>Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians.</p><p><strong>Objective: </strong>To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages.</p><p><strong>Methods: </strong>The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance.</p><p><strong>Results: </strong>The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method.</p><p><strong>Conclusion: </strong>This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"31-51"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.","authors":"Tianyu Lu, Jianbing Ma, Jiajun Zou, Chenxu Jiang, Yangyang Li, Jun Han","doi":"10.3233/XST-230326","DOIUrl":"10.3233/XST-230326","url":null,"abstract":"<p><strong>Background: </strong>The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration.</p><p><strong>Objective: </strong>This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models.</p><p><strong>Methods: </strong>We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated.</p><p><strong>Results: </strong>Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature.</p><p><strong>Conclusions: </strong>Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"597-609"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}