Physical and Engineering Sciences in Medicine最新文献

{"title":"Early detection of colorectal cancer using a hybrid model with enhanced image quality and optimized classification.","authors":"Ahmet Bozdag, Mucahit Karaduman, Soner Kiziloluk, Gulsah Karaduman, Muhammed Yildirim, Ozal Yildirim, Ru-San Tan, U Rajendra Acharya","doi":"10.1007/s13246-025-01617-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01617-y","url":null,"abstract":"<p><p>Colorectal cancer starts in the large intestine and rectum. It develops when small, usually harmless growths called polyps become cancerous over time. Early diagnosis increases the chances of successfully treating colorectal cancer. A new hybrid model was developed to detect colorectal tissue types. In the first step of the model, the quality of the images was increased using Denoising Convolutional Neural Network (DNCNN) networks. The feature maps of the images were then obtained using DarkNet53 and shrunk using the Gorilla Troops Optimization Algorithm (GTO) to speed up the proposed model's performance and boost the performance. Finally, a support vector machine (SVM) classifier was used to classify the feature maps. The proposed model obtained an accuracy of 95.5% in classifying eight tissue types in colorectal cancer histopathology specimens (Adipose, Complex, Debris, Empty, Lympho, Mucosa, Stroma, and Tumor). To make the developed model more generalizable, robust, and accurate, it needs to be tested with a huge dataset collected from various centers and races.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144817979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational eye state classification model using EEG signal based on data mining techniques: comparative analysis.","authors":"Subhash Mondal, Amitava Nag","doi":"10.1007/s13246-025-01619-w","DOIUrl":"https://doi.org/10.1007/s13246-025-01619-w","url":null,"abstract":"<p><p>Artificial Intelligence has shown great promise in healthcare, particularly in non-invasive diagnostics using bio signals. This study focuses on classifying eye states (open or closed) using Electroencephalogram (EEG) signals captured via a 14-electrode neuroheadset, recorded through a Brain-Computer Interface (BCI). A publicly available dataset comprising 14,980 instances was used, where each sample represents EEG signals corresponding to eye activity. Fourteen classical machine learning (ML) models were evaluated using a tenfold cross-validation approach. The preprocessing pipeline involved removing outliers using the Z-score method, addressing class imbalance with SMOTETomek, and applying a bandpass filter to reduce signal noise. Significant EEG features were selected using a two-sample independent t-test (p < 0.05), ensuring only statistically relevant electrodes were retained. Additionally, the Common Spatial Pattern (CSP) method was used for feature extraction to enhance class separability by maximizing variance differences between eye states. Experimental results demonstrate that several classifiers achieved strong performance, with accuracy above 90%. The k-Nearest Neighbours classifier yielded the highest accuracy of 97.92% with CSP, and 97.75% without CSP. The application of CSP also enhanced the performance of Multi-Layer Perceptron and Support Vector Machine, reaching accuracies of 95.30% and 93.93%, respectively. The results affirm that integrating statistical validation, signal processing, and ML techniques can enable accurate and efficient EEG-based eye state classification, with practical implications for real-time BCI systems and offering a lightweight solution for real-time healthcare wearable applications healthcare applications.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced detection of ovarian cancer using AI-optimized 3D CNNs for PET/CT scan analysis.","authors":"Mohammad Hossein Sadeghi, Sedigheh Sina, Reza Faghihi, Mehrosadat Alavi, Francesco Giammarile, Hamid Omidi","doi":"10.1007/s13246-025-01615-0","DOIUrl":"https://doi.org/10.1007/s13246-025-01615-0","url":null,"abstract":"<p><p>This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging using large imaging datasets. Specifically, we compare six conventional convolutional neural network (CNN) architectures-ResNet, DenseNet, GoogLeNet, U-Net, VGG, and AlexNet-with OCDA-Net, an enhanced model designed for [¹⁸F]FDG PET image analysis. The OCDA-Net, an advancement on the ResNet architecture, was thoroughly compared using randomly split datasets of training (80%), validation (10%), and test (10%) images. Trained over 100 epochs, OCDA-Net achieved superior diagnostic classification with an accuracy of 92%, and staging results of 94%, supported by robust precision, recall, and F-measure metrics. Grad-CAM ++ heat-maps confirmed that the network attends to hyper-metabolic lesions, supporting clinical interpretability. Our findings show that OCDA-Net outperforms existing CNN models and has strong potential to transform ovarian cancer diagnosis and staging. The study suggests that implementing these DL models in clinical practice could ultimately improve patient prognoses. Future research should expand datasets, enhance model interpretability, and validate these models in clinical settings.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of artificial intelligence assistance on bone scintigraphy diagnosis.","authors":"Yosita Uchuwat, Natthanan Ruengchaijatuporn, Chanan Sukprakun, Sira Vachatimanont, Maythinee Chantadisai, Kanaungnit Kingpetch, Tawatchai Chaiwatanarat, Supatporn Tepmongkol, Chanittha Buakhao, Kitwiwat Phuangmali, Sira Sriswasdi, Yothin Rakvongthai","doi":"10.1007/s13246-025-01621-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01621-2","url":null,"abstract":"<p><p>Bone scintigraphy is an important tool for detecting bone lesions. This study aimed to improve and evaluate the performance of our previously-developed deep learning-based model called MaligNet in helping nuclear medicine (NM) physicians interpret bone scan. Bone scintigraphy of 553 patients with imaging data from six-month follow-up records were split into training, validation, and test sets in a ratio of 353:100:100 to re-train MaligNet. Seven nuclear medicine physicians, including two junior and five senior physicians, were asked to segment and classify lesions in the test set images without and with AI assistance, which was the prediction of MaligNet. The improved performance of MaligNet was evaluated using the precision-recall (PR) and receiver operating characteristic (ROC) curves for lesion-based and patient-based classifications, respectively. The impact of AI assistance on physician reading was evaluated using reading time per case and malignancy diagnostic performance metrics. The re-trained MaligNet yielded considerably higher area under the PR curve (0.334 vs. 0.225) and higher area under the ROC curve (0.881 vs. 0.789) than the original model. For patient-based classification, AI assistance improved the average accuracy, sensitivity, specificity, and precision of the physician by 2.14%, 0.89%, 2.38%, and 1.97%, respectively, while reducing the average reading time by 31.14%. For lesion-based classification, it improved physicians' average precision by 2.95%, but did not improve sensitivity. With AI assistance, junior physicians achieved diagnostic performances comparable to those of senior physicians. AI assistance with MaligNet improved bone scintigraphy diagnostic performance and showed promise in clinical practice.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated analysis of ECG signals using nonlinearity and nonstationarity features fed into the MobilenetV2 CNN powered by transfer learning.","authors":"Richel T Nguimdo, Alain Tiedeu, Janvier Fotsing","doi":"10.1007/s13246-025-01610-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01610-5","url":null,"abstract":"<p><p>Atrial fibrillation (AFB) and atrial flutter (AFL) are cardiac arrhythmias very often associated with the aggravation of other cardiac pathologies and increase the risk of stroke and heart failure. Their detection is therefore crucial. Automated analysis of the ECG signal has been suggested to assist cardiologists in the diagnosis of AFB and AFL. In this paper, a novel automated electrocardiogram (ECG) signal analysis method to aid in the detection of AFB and AFL is presented. The first step of the method consists of processing the original ECG signal. The second step carries out the classification using a modified MobileNetV2 convolutional neural network (CNN) powered by transfer learning. This CNN classifies the fed-in ECG signals into atrial fibrillation (AFB), atrial flutter (AFL), other (OTH), normal sinus rhythms (NOR), and noisy (NOI) recordings. The performance of the proposed method was assessed and scored using the Physio Net/Computing in Cardiology (CinC) 2017 dataset and the MIT-BIH Atrial Fibrillation Database (MIT-BIH). The experimental results showed that the proposed method gave an F1 score of 96.08%, sensitivity of 97.1%, specificity of 99.53%, and accuracy of 95.1% for atrial fibrillation, for the CinC 2017 dataset. For the MIT-BIH dataset, an F1 score of 99.54%, sensitivity of 99.51%, specificity of 99.64%, and accuracy of 99.5% were obtained. The results disclosed above on 2 databases prove that the proposed algorithm is efficient, robust, and can be used to assist cardiologists.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetric evaluation of synthetic kilo-voltage CT images generated from megavoltage CT for head and neck tomotherapy using a conditional GAN network.","authors":"Yazdan Choghazardi, Mohamad Bagher Tavakoli, Iraj Abedi, Mahnaz Roayaei, Simin Hemati, Ahmad Shanei","doi":"10.1007/s13246-025-01603-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01603-4","url":null,"abstract":"<p><p>The lower image contrast of megavoltage computed tomography (MVCT), which corresponds to kilovoltage computed tomography (kVCT), can inhibit accurate dosimetric assessments. This study proposes a deep learning approach, specifically the pix2pix network, to generate high-quality synthetic kVCT (skVCT) images from MVCT data. The model was trained on a dataset of 25 paired patient images and evaluated on a test set of 15 paired images. We performed visual inspections to assess the quality of the generated skVCT images and calculated the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Dosimetric equivalence was evaluated by comparing the gamma pass rates of treatment plans derived from skVCT and kVCT images. Results showed that skVCT images exhibited significantly higher quality than MVCT images, with PSNR and SSIM values of 31.9 ± 1.1 dB and 94.8% ± 1.3%, respectively, compared to 26.8 ± 1.7 dB and 89.5% ± 1.5% for MVCT-to-kVCT comparisons. Furthermore, treatment plans based on skVCT images achieved excellent gamma pass rates of 99.78 ± 0.14% and 99.82 ± 0.20% for 2 mm/2% and 3 mm/3% criteria, respectively, comparable to those obtained from kVCT-based plans (99.70 ± 0.31% and 99.79 ± 1.32%). This study demonstrates the potential of pix2pix models for generating high-quality skVCT images, which could significantly enhance Adaptive Radiation Therapy (ART).</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of two bolus types for radiotherapy following immediate breast reconstruction.","authors":"Kasia Bobrowski, Jonathon Lee","doi":"10.1007/s13246-025-01604-3","DOIUrl":"https://doi.org/10.1007/s13246-025-01604-3","url":null,"abstract":"<p><p>Immediate breast Reconstruction is increasing in use in Australia and accounts for almost 10% of breast cancer patients (Roder in Breast 22:1220-1225, 2013). Many treatments include a bolus to increase dose to the skin surface. Air gaps under bolus increase uncertainty in dosimetry and many bolus types are unable to conform to the shape of the breast or are not flexible throughout treatment if there is a swelling induced contour change. This study investigates the use of two bolus types that can be manufactured in house-wet combine and ThermoBolus. Wet combine is a material composed of several water soaked dressings. ThermoBolus is a product developed in-house that consists of thermoplastic encased in silicone. Plans using a volumetric arc therapy technique were created for each bolus and dosimetry performed with thermoluminescent detectors (TLDs) and EBT-3 film over three fractions. Wax was used to simulate swelling and allow analysis of the flexibility of the bolus materials. ThermoBolus had a range of agreement with calculation from -2 to 4% for film measurement and -5.6 to 1.0% for TLDs. Wet combine had a range of agreement with calculation from 1.6 to 10.5% for film measurement and -13.5 to 13.1% for TLDs. It showed consistent conformity and flexibility for all fractions and with induced contour but air gaps of 2-3 mm were observed between layers of the material. ThermoBolus and wet combine are able to conform to contour change without the introduction of large air gaps between the patient surface and bolus. ThermoBolus is reusable and can be remoulded if the patient undergoes significant contour change during the course of treatment. It is able to be modelled accurately by the treatment planning system. Wet combine shows inconsistency in manufacture and requires more than one bolus to be made over the course of treatment, reducing accuracy in modelling and dosimetry.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling single cell dosimetry and DNA damage of targeted alpha therapy using Monte-Carlo techniques.","authors":"Adam L Jolly, Andrew L Fielding","doi":"10.1007/s13246-025-01605-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01605-2","url":null,"abstract":"<p><p>Targeted alpha therapy (TαT) employs alpha particle-emitting radioisotopes conjugated to tumour-specific carriers to precisely irradiate tumour cells. Monte-carlo techniques have been used to accurately simulate absorbed dose and DNA damage for the four promising TαT radionuclides, Actinium-225 (²²⁵Ac), Radium-223, (²²³Ra), Lead-212 (²¹²Pb) and Astatine-211, (²¹¹At). TOPAS and TOPAS-nBio, based on the Geant4 and Geant4-DNA monte-carlo codes respectively, were used to model the radioactive decay and alpha particle transport within a simplified spherical cell model. Four different sites within the cell model were used for the initial radionuclide distributions: the cell membrane layer, within the cytoplasm volume, on the nucleus surface, and within the nucleus volume. Results indicate higher absorbed doses to the nucleus per decay when radionuclides are initially located on the nucleus wall or within the nucleus volume. ²²⁵Ac and ²²³Ra, with longer decay chains and higher alpha yields, exhibit higher doses to the nucleus per decay compared to ²¹²Pb and ²¹¹At. Notably, ²¹¹At, particularly when initially distributed within the nucleus volume or at its surface, demonstrates high relative efficacy, indicated by the absorbed dose to the nucleus per decay and number of single and double-strand breaks. These findings suggest that tumour-specific molecules should ideally target the nucleus to optimize efficacy.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An open-source tool for converting 3D mesh volumes into synthetic DICOM CT images for medical physics research.","authors":"Michael John James Douglass","doi":"10.1007/s13246-025-01599-x","DOIUrl":"https://doi.org/10.1007/s13246-025-01599-x","url":null,"abstract":"<p><p>Access to medical imaging data is crucial for research, training, and treatment planning in medical imaging and radiation therapy. However, ethical constraints and time-consuming approval processes often limit the availability of such data for research. This study introduces DICOMator, an open-source Blender add-on designed to address this challenge by enabling the creation of synthetic CT datasets from 3D mesh objects. DICOMator aims to provide researchers and medical professionals with a flexible tool for generating customisable and semi-realistic synthetic CT data, including 4D CT datasets from user defined static or animated 3D mesh objects. The add-on leverages Blender's powerful 3D modelling environment, utilising its mesh manipulation, animation and rendering capabilities to create synthetic data ranging from simple phantoms to accurate anatomical models. DICOMator incorporates various features to simulate common CT imaging artefacts, bridging the gap between 3D modelling and medical imaging. DICOMator voxelises 3D mesh objects, assigns appropriate Hounsfield Unit values, and applies artefact simulations. These simulations include detector noise, metal artefacts and partial volume effects. By incorporating these artefacts, DICOMator produces synthetic CT data that more closely resembles real CT scans. The resulting data is then exported in DICOM format, ensuring compatibility with existing medical imaging workflows and treatment planning systems. To demonstrate DICOMator's capabilities, three synthetic CT datasets were created: a simple lung phantom to illustrate basic functionality, a more realistic cranial CT scan to demonstrate dose calculations and CT image registration on synthetic data in treatment planning systems. Finally, a thoracic 4D CT scan featuring multiple breathing phases was created to demonstrate the dynamic imaging capabilities and the quantitative accuracy of the synthetic datasets. These examples were chosen to highlight DICOMator's versatility in generating diverse and complex synthetic CT data suitable for various research and educational purposes, from basic quality assurance to advanced motion management studies. DICOMator offers a promising solution to the limitations of patient CT data availability in medical physics research. By providing a user-friendly interface for creating customisable synthetic datasets from 3D meshes, it has the potential to accelerate research, validate treatment planning tools such as deformable image registration, and enhance educational resources in the field of radiation oncology medical physics. Future developments may include incorporation of other imaging modalities, such as MRI or PET, further expanding its utility in multi-modal imaging research.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of differences in computed tomography value-electron density/physical density conversion tables on calculate dose in low-density areas.","authors":"Mia Nomura, Shunsuke Goto, Mizuki Yoshioka, Yuiko Kato, Ayaka Tsunoda, Kunio Nishioka, Yoshinori Tanabe","doi":"10.1007/s13246-025-01611-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01611-4","url":null,"abstract":"<p><p>In radiotherapy treatment planning, the extrapolation of computed tomography (CT) values for low-density areas without known materials may differ between CT scanners, resulting in different calculated doses. We evaluated the differences in the percentage depth dose (PDD) calculated using eight CT scanners. Heterogeneous virtual phantoms were created using LN-300 lung and - 900 HU. For the two types of virtual phantoms, the PDD on the central axis was calculated using five energies, two irradiation field sizes, and two calculation algorithms (the anisotropic analytical algorithm and Acuros XB). For the LN-300 lung, the maximum CT value difference between the eight CT scanners was 51 HU for an electron density (ED) of 0.29 and 8.8 HU for an extrapolated ED of 0.05. The LN-300 lung CT values showed little variation in the CT-ED/physical density data among CT scanners. The difference in the point depth for the PDD in the LN-300 lung between the CT scanners was < 0.5% for all energies and calculation algorithms. Using Acuros XB, the PDD at - 900 HU had a maximum difference between facilities of > 5%, and the dose difference corresponding to an LN-300 lung CT value difference of > 20 HU was > 1% at a field size of 2 × 2 cm². The study findings suggest that the calculated dose of low-density regions without known materials in the CT-ED conversion table introduces a risk of dose differences between facilities because of the calibration of the CT values, even when the same CT-ED phantom radiation treatment planning and treatment devices are used.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}