Physical and Engineering Sciences in Medicine最新文献

{"title":"A lightweight 1D convolutional neural network model for arrhythmia diagnosis from electrocardiogram signal.","authors":"Beaudelaire Saha Tchinda, Daniel Tchiotsop","doi":"10.1007/s13246-025-01525-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01525-1","url":null,"abstract":"<p><p>Electrocardiogram (ECG) is used by cardiologist to diagnose heart diseases. The use of ECG signal in an artificial intelligence system can permit to automatically analyze these signals and thereby improve diagnosis quality. For this purpose, many models have been proposed in the literature. But many of these models are complex enough for implementation in an embedded system dedicated to medical diagnosis. Still others have performances that remain to be improved. To solve this problem of complexity, while improving performance, we propose a simple 1D convolutional neural network model for cardiac arrhythmia diagnosis. The proposed model combines two convolution layers, two max pooling layers, three dense layers, two dropout layers and a flatten layer. We apply the proposed model on the public MIT-BIH database for inter-patient classification of five distinct types of heartbeat rhythms which are consistent with the association for advancement of medical instrumentation (AAMI) standard. We also apply our model on the PTB database in order to evaluate its generalization capability. On the MIT-BIH database, the results provide an accuracy of 0.9842, a precision of 0.9523, a sensitivity of 0.8760, a specificity of 0.9869, a negative predictive value (NPV) of 0.9936, an average area under the ROC curve (AUC) of 0.99 and a F1-measure of 0.9095. The accuracy, precision, sensitivity, specificity, NPV, and AUC on the PTB dataset are 0.9924, 0.9938, 0.9957, 0.9844, 0.9892, and 1, respectively. Compared to other existing models, for unbalanced data, the performances obtained by our model are quite interesting for an inter-patient classification.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494183","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":"Enhancing intrafraction position monitoring for prostate radiotherapy on a conventional linear accelerator: an optimization study.","authors":"Sankar Arumugam","doi":"10.1007/s13246-025-01527-z","DOIUrl":"https://doi.org/10.1007/s13246-025-01527-z","url":null,"abstract":"<p><p>To compare the intrafraction prostate motion monitoring capabilities between intrafraction Cone Beam Computed Tomography (IF-CBCT) and SeedTracker-based real-time monitoring, and to optimize imaging doses in real-time monitoring using the IF-CBCT image acquisition method. Simulations of static and dynamic intrafraction prostate motions were conducted on a phantom using a robotic arm. The study utilized the XVI imaging system of the Elekta linear accelerator for IF-CBCT and SeedTracker-based monitoring during hypofractionation and Stereotactic Body Radiation Therapy (SBRT). The optimal imaging frequency for real-time monitoring was determined by calculating VMAT gantry traverse times. The effective dose resulting from IF-CBCT and SeedTracker-based monitoring approaches were compared. IF-CBCT showed static offsets as seed duplications and the offsets calculated using 'Seed' automatic image registration available XVI system depend on the initial position of the seeds in verification and localisation image sets. This dependency resulted in large differences (up to 4.9 mm) between actual and calculated position offsets. Dynamic offsets resulted in blurring or duplication of seeds in IF-CBCT images depending on the type of the dynamic motion. SeedTracker-based real-time monitoring successfully identified position deviation events as they occurred during treatment. For hypofractionation and SBRT treatments, IF-CBCT imaging resulted in an effective dose of 54.3 mSv and 13.6 mSv, respectively. Optimized imaging frequency for real-time monitoring led to a dose reduction of up to 86.2% and 97.2% for hypofractionation and SBRT regimens, respectively, compared to the IF-CBCT approach. SeedTracker real-time monitoring effectively identified target position deviations in real-time, surpassing the capabilities of the IF-CBCT approach. Moreover, the SeedTracker imaging approach significantly reduced imaging doses compared to IF-CBCT.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483783","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":"Subclinical tremor differentiation using long short-term memory networks.","authors":"Gerard Ruchin Randil Nanayakkara, Ping Yi Chan","doi":"10.1007/s13246-025-01526-0","DOIUrl":"https://doi.org/10.1007/s13246-025-01526-0","url":null,"abstract":"<p><p>Subclinical amplitudes complicate the differentiation between essential tremor (ET) and Parkinson's disease (PD) tremor, which is uncertain even when the tremors are apparent. Despite their prevalence-up to 30% of PD cases exhibit subclinical tremors-these tremors remain inadequately studied. Therefore, this study explores the potential of artificial intelligence (AI) to address this differentiation uncertainty. Our objective is to develop a deep learning model that can differentiate among subclinical tremors due to PD, ET, and normal physiological tremors. Subclinical tremor data were obtained from inertial sensors placed on the hands and arms of 51 PD, 15 ET, and 58 normal subjects. The AI architecture used was designed using a long short-term memory network (LSTM) and was trained on the short-time Fourier transformed subclinical tremor data as the input features. The network was trained separately to differentiate firstly between PD and ET tremors and then between PD, ET, and physiological tremors and yielded accuracies of 95% and 93%, respectively. Comparative analysis with existing convolutional LSTM demonstrated the superior performance of our work. The proposed method has 30-50% better accuracies when classifying low amplitude tremors as compared to the reference method. Future enhancements aim to enhance model interpretability and validate on larger, more diverse datasets, including action tremors. The proposed work can potentially serve as a valuable tool for clinicians, aiding in the differentiation of subclinical tremors common in Parkinson's disease, which in turn enhances diagnostic accuracy and informs treatment decisions.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484218","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":"Evolved size-specific dose estimates for patient-specific organ doses from chest CT scans based on hybrid patient size vectors.","authors":"Wencheng Shao, Ke Yang, Lizhi Lou, Xin Lin, Liangyong Qu, Weihai Zhuo, Haikuan Liu","doi":"10.1007/s13246-025-01522-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01522-4","url":null,"abstract":"<p><p>This study aims to develop a neural network-based method for predicting patient-specific organ doses from chest CT scans, utilizing hybrid patient size vectors for enhanced computational efficiency, accuracy, and generality. A dataset of 705 chest CT scans was retrospectively analyzed to construct predictive models for organ dose estimation. The proposed approach employs high dimensional hybrid vectors to represent patient size, combining muti-slice parameters regarding lateral dimension, anteroposterior dimension, and water-equivalent diameter (D_w). These vectors are used to train fully-connected neural networks, which are designed to correlate high-dimensional patient size features with reference organ doses obtained from Monte Carlo simulations. The performance of the neural networks was evaluated using separate test cohorts, with metrics such as mean absolute percentage error (MAPE) and coefficient of determination (R²) to evaluate predictive accuracy and generality. For the left lung, right lung, heart, and spinal cord, the trained neural networks respectively achieve MAPE values of 2.94%, 2.79%, 7.04%, and 6.76%, and R² values of 0.98, 0.99, 0.93, and 0.91. The maximal discrepancy between reference and predicted values is less than 10% for the left and right lungs, and less than 20% for the heart and spinal cord. With 5-fold cross-validation, the maximal perturbation does not exceed 1% in MAPE and 0.05 in R². By incorporating hybrid patient size vectors, the neural network models achieve superior accuracy in organ dose estimation compared with traditional size specific dose estimates, paving the way for online swift organ dose screening in clinical practice.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483785","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":"PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.","authors":"Seyyed Ali Hosseini, Ghasem Hajianfar, Pardis Ghaffarian, Milad Seyfi, Elahe Hosseini, Atlas Haddadi Aval, Stijn Servaes, Mauro Hanaoka, Pedro Rosa-Neto, Sanjeev Chawla, Habib Zaidi, Mohammad Reza Ay","doi":"10.1007/s13246-024-01475-0","DOIUrl":"10.1007/s13246-024-01475-0","url":null,"abstract":"<p><p>The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1613-1625"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.","authors":"Mohammad Hossein Sadeghi, Sedigheh Sina, Mehrosadat Alavi, Francesco Giammarile, Chai Hong Yeong","doi":"10.1007/s13246-024-01485-y","DOIUrl":"10.1007/s13246-024-01485-y","url":null,"abstract":"<p><p>Accurate segmentation of ovarian cancer (OC) lesions in PET/CT images is essential for effective disease management, yet manual segmentation for radiomics analysis is labor-intensive and time-consuming. This study introduces the application of a 3D U-Net deep learning model, leveraging advanced 3D networks, for multi-class semantic segmentation of OC in PET/CT images and assesses the stability of the extracted radiomics features. Utilizing a dataset of 3120 PET/CT images from 39 OC patients, the dataset was divided into training (70%), validation (15%), and test (15%) subsets to optimize and evaluate the model's performance. The 3D U-Net model, especially with a VGG16 backbone, achieved notable segmentation accuracy with a Dice score of 0.74, Precision of 0.76, and Recall of 0.78. Additionally, the study demonstrated high stability in radiomics features, with over 85% of PET and 84% of CT image features showing high intraclass correlation coefficients (ICCs > 0.8). These results underscore the potential of automated 3D U-Net-based segmentation to significantly enhance OC diagnosis and treatment planning. The reliability of the extracted radiomics features from automated segmentation supports its application in clinical decision-making and personalized medicine. This research marks a significant advancement in oncology diagnostics, providing a robust and efficient method for segmenting OC lesions in PET/CT images. By addressing the challenges of manual segmentation and demonstrating the effectiveness of 3D networks, this study contributes to the growing body of evidence supporting the application of artificial intelligence in improving diagnostic accuracy and patient outcomes in oncology.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1739-1749"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298927","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":"Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.","authors":"Ammini Renjini, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman","doi":"10.1007/s13246-024-01455-4","DOIUrl":"10.1007/s13246-024-01455-4","url":null,"abstract":"<p><p>The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distinguishing lung sounds and supporting earlier detection of various respiratory pathologies. The frequency spread and the component magnitudes are revealed from the analysis of eighty-five bronchial (BS) and pleural rub (PS) lung sounds employing the power spectral density (PSD) plot and wavelet scalogram. The low-frequency spread, and persistence of the high-intensity frequency components are visible in BS sounds emanating from the uniform cross-sectional area of the trachea. The frictional rub between the pleurae causes a higher frequency spread of low-intensity intermittent frequency components in PS signals. From the complex networks of BS and PS, the extracted graph features are - graph density ([Formula: see text], transitivity ([Formula: see text], degree centrality ([Formula: see text]), betweenness centrality ([Formula: see text], eigenvector centrality ([Formula: see text]), and graph entropy (E_n). The high values of [Formula: see text] and [Formula: see text] show a strong correlation between distinct segments of the BS signal originating from a consistent cross-sectional tracheal diameter and, hence, the generation of high-intense low-spread frequency components. An intermittent low-intense and a relatively greater frequency spread in PS signal appear as high [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] values. With these complex network parameters as input attributes, the supervised machine learning techniques- discriminant analyses, support vector machines, k-nearest neighbors, and neural network pattern recognition (PRNN)- classify the signals with more than 90% accuracy, with PRNN having 25 neurons in the hidden layer achieving the highest (98.82%).</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1447-1459"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141493979","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":"EPSM 2023, Engineering and Physical Sciences in Medicine : 5-8 November 2024, Ōtautahi Christchurch, New Zealand.","authors":"","doi":"10.1007/s13246-024-01460-7","DOIUrl":"10.1007/s13246-024-01460-7","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1793-1904"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917885","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":"Deep learning architecture with shunted transformer and 3D deformable convolution for voxel-level dose prediction of head and neck tumors.","authors":"Liting Chen, Hongfei Sun, Zhongfei Wang, Te Zhang, Hailang Zhang, Wei Wang, Xiaohuan Sun, Jie Duan, Yue Gao, Lina Zhao","doi":"10.1007/s13246-024-01462-5","DOIUrl":"10.1007/s13246-024-01462-5","url":null,"abstract":"<p><p>Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for <math><msub><mi>D</mi> <mn>99</mn></msub> </math> , 1.54% for <math><msub><mi>D</mi> <mn>95</mn></msub> </math> , 1.87% for <math><msub><mi>D</mi> <mn>1</mn></msub> </math> , 1.87% for <math><msub><mi>D</mi> <mrow><mi>mean</mi></mrow> </msub> </math> , 1.89% for <math><msub><mi>D</mi> <mrow><mn>0.1</mn> <mi>c</mi> <mi>c</mi></mrow> </msub> </math> , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1501-1512"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890590","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":"Unifying gamma passing rates in patient-specific QA for VMAT lung cancer treatment based on data assimilation.","authors":"Tomohiro Ono, Takanori Adachi, Hideaki Hirashima, Hiraku Iramina, Noriko Kishi, Yukinori Matsuo, Mitsuhiro Nakamura, Takashi Mizowaki","doi":"10.1007/s13246-024-01448-3","DOIUrl":"10.1007/s13246-024-01448-3","url":null,"abstract":"<p><p>This study aimed to identify systematic errors in measurement-, calculation-, and prediction-based patient-specific quality assurance (PSQA) methods for volumetric modulated arc therapy (VMAT) on lung cancer and to standardize the gamma passing rate (GPR) by considering systematic errors during data assimilation. This study included 150 patients with lung cancer who underwent VMAT. VMAT plans were generated using a collapsed-cone algorithm. For measurement-based PSQA, ArcCHECK was employed. For calculation-based PSQA, Acuros XB was used to recalculate the plans. In prediction-based PSQA, GPR was forecasted using a previously developed GPR prediction model. The representative GPR value was estimated using the least-squares method from the three PSQA methods for each original plan. The unified GPR was computed by adjusting the original GPR to account for systematic errors. The range of limits of agreement (LoA) were assessed for the original and unified GPRs based on the representative GPR using Bland-Altman plots. For GPR (3%/2 mm), original GPRs were 94.4 ± 3.5%, 98.6 ± 2.2% and 93.3 ± 3.4% for measurement-, calculation-, and prediction-based PSQA methods and the representative GPR was 95.5 ± 2.0%. Unified GPRs were 95.3 ± 2.8%, 95.4 ± 3.5% and 95.4 ± 3.1% for measurement-, calculation-, and prediction-based PSQA methods, respectively. The range of LoA decreased from 12.8% for the original GPR to 9.5% for the unified GPR across all three PSQA methods. The study evaluated unified GPRs that corrected for systematic errors. Proposing unified criteria for PSQA can enhance safety regardless of the methods used.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1337-1348"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141427990","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}