Physical and Engineering Sciences in Medicine最新文献

{"title":"Maximum-likelihood estimation of glandular fraction for mammography and its effect on microcalcification detection.","authors":"Bryce J Smith, Joyoni Dey, Lacey Medlock, David Solis, Krystal Kirby","doi":"10.1007/s13246-025-01540-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01540-2","url":null,"abstract":"<p><p>Breast tissue is mainly a mixture of adipose and fibro-glandular tissue. Cancer risk and risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists must report the total volume glandular fraction or a BI-RADS classification in screening and diagnostic mammography. In this work, a Maximum Likelihood algorithm accounting for count statistics and scatter is shown to estimate the pixel-wise glandular fraction from mammographic images. The pixel-wise glandular fraction provides information that helps localize dense tissue. The total volume glandular fraction can be calculated from pixel-wise glandular fraction. The algorithm was implemented for images acquired with an anti-scatter grid, and those without using the anti-scatter grid but followed by software scatter removal. The work also studied if presenting the pixel-wise glandular fraction image alongside the usual mammographic image has the potential to improve the contrast-to-noise ratio on micro-calcifications in the breast. The algorithms are implemented and evaluated with TOPAS Geant4 generated images with known glandular fractions. These images are also taken with and without microcalcifications present to study the effects of glandular fraction estimation on microcalcification detection. The algorithm was then applied to clinical images with and without microcalcifications. For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 6.6% for the with anti-scatter-grid cases and 7.6% for the software scatter removal (no anti-scatter grid) cases for a range of 2-9 cm compressed breast thickness. Average absolute errors were 4.5% and 4.7% for a range of 2-9 cm compressed breast thickness respectively for the anti-scatter grid and software scatter-removal methods. For higher thickness and glandular fraction, the errors were higher. For the extreme case of 9 cm thickness, the glandular fraction estimation yielded 5%, 13% and 16% mean absolute errors for 20%, 30% and 50% glandular fraction. These errors lowered to 1.5%, 9% and 13.2% for a narrower spectrum for the 9 cm. Results from clinical images (where the true glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in clinical images and 5.1-88% in TOPAS images. A method for accurately estimating the pixel-wise glandular fraction in images, which provides localization information about breast density, was demonstrated. The glandular fraction images also showed an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006989","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 adaptive segmentation scheme based on recurring action potentials for sEMG controlled movement decoding.","authors":"Anil Sharma, Nikhil Vivek Shrivas, Ila Sharma","doi":"10.1007/s13246-025-01557-7","DOIUrl":"https://doi.org/10.1007/s13246-025-01557-7","url":null,"abstract":"<p><p>An electromyography (EMG) controlled decoding system requires signal pre-processing, feature extraction, and classification as fundamental steps and requires high accuracy and minimum delay. The conventional system relies on the constant width segmentation scheme for feature extraction, which does not cover the complexities associated with the random behavior of EMG signals. An adaptive segmentation based on the repeating patterns of action potentials can be a promising solution. This work proposes a novel adaptive segmentation approach that captures the occurrence of these action potentials for segmentation and feature extraction. The proposed work is validated experimentally with 12 subjects performing eight different movements. Twenty-time domain features are extracted to verify the study. Linear Discriminant Analysis (LDA), k-nearest neighbor (kNN), and Decision Tree (DT) classifiers are used to observe the performance of the proposed scheme in terms of precision, recall, F1 score, and accuracy. The proposed method gives an average segmentation width of 124 ms across all subjects with 124 ± 5.4 (± 4.35 %) margin of error at 95 % confidence level. The average F1 score across all subjects for eight movements is 82.078 % for LDA, 81.51 % for kNN, and 80.81 % for DT classifiers. The 5-fold cross-validated accuracies for LDA, kNN, and DT classifiers are 78.3 %, 78.2 %, and 76.70 %, respectively. The calculated accuracies are compared with a constant width segmentation scheme with a window size of 200 ms. The t-test suggests significant improvement in the performance of the classifiers with the proposed method.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046543","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":"The application of multi-arc volumetric modulated arc therapy and fixed-field intensity modulated radiotherapy in the treatment of gynecologic cancer with large planning target volume.","authors":"Jia-Ling Song, Yi-Zhao Zhang, Zhi-Long Zhang, Zheng-Feng Fu, Peng-Fei Sun","doi":"10.1007/s13246-025-01538-w","DOIUrl":"https://doi.org/10.1007/s13246-025-01538-w","url":null,"abstract":"<p><p>To investigate the dosimetry and delivery efficiency differences between multi-arc volumetric modulated arc therapy (VMAT) and fixed-field intensity modulated radiotherapy (IMRT) in the treatment of gynecological cancer with large planning target volume (PTV). Thirteen patients with gynecological cancer (9 cervical and 4 vulvar) with a PTV greater than 1600 cm³ were retrospectively selected. Three-arc VMAT (3ARC) and seven-field IMRT plans were generated using identical objective functions from clinical two-arc VMAT (2ARC) plans to allow a rigorous comparison for each patient. Target coverage, OARs sparing, integral dose and delivery efficiency were compared through dose-volume histogram (DVH) analysis. Compared with 2ARC plans, IMRT exhibited a slightly superior target coverage with higher D_98%, CI and lower D_2%, D_50%, V_110% and HI (P < 0.01). For OARs, IMRT produced lower V_40Gy and D_mean to the bladder and rectum (P < 0.01) and lower V_40Gy to bone marrow than 2ARC (P < 0.05). No significant differences were observed for the colon, small bowel and femoral heads, while 2ARC performed worse at the low dose and integral dose to normal tissue (V_5Gy, V_10Gy and NTID, P < 0.01). Nevertheless, IMRT increased MUs by 1.65% and EDT by 107 s compared to 2ARC. Compared with 2ARC, 3ARC showed no improvement in target dose coverage, including D_98%, D_2%, D_50%, V_110%, CI and HI to PTV, but increased the doses to OARs (D_mean to the bladder, rectum and bone marrow, V_40Gy to the bone marrow and D_5% to both the left and right femoral heads, P < 0.05), low dose and integral dose to normal tissue (V_10Gy,V_15Gy,V_20Gy and NTID, P < 0.01) and simultaneously prolonged the EDT (P < 0.001). In the treatment of gynecological cancer with a large planning target volume, the IMRT technique can be delivered superior conformal dose to the target with somewhat better OARs sparing but increasing the estimated delivery time.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022835","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":"Training and testing of machine learning techniques to aid in prediction of patients requiring adaptive treatments for head and neck radiotherapy.","authors":"Mark Ashburner, Omer Ali, Gill Dobbie, Jacinta Zhang, Xinyi Guo","doi":"10.1007/s13246-025-01537-x","DOIUrl":"https://doi.org/10.1007/s13246-025-01537-x","url":null,"abstract":"<p><p>Adaptive radiotherapy (ART) offers a tailored approach to radiotherapy treatment and has been shown to be beneficial to patients undergoing treatment for head and neck carcinoma. The challenge lies in prospectively identifying patients who will benefit from ART intervention at the planning stage. This study presents the assessment of AI-based predictive models aimed to address this challenge. Retrospective data from 100 head and neck patients were analysed, encompassing various patient features, including weight, neck dimensions, body volume, and target volumes. The training phase began with a decision tree algorithm, which was compared to a selection of other suitable classifiers, being: random forest, bagging, adaBoost and gradient boosting. Model performance was assessed using accuracy, F1 score, and cross-validation accuracy. Initial features in the classifier were selected based on expert (RO) opinion; feature selection was done to refine the final model. The final model was tested on new patient data (N = 110). Final performance was assessed using precision, recall, specificity, and sensitivity. The initial model exhibited F1 score 65%, test accuracy 60%, and cross-validation accuracy 72%. However, when tested on new data, a notable prevalence of false positives (21 cases) was observed. Analysis of these cases revealed a spectrum of adaptive interventions leading to reclassification of these instances, indicating the model's ability to discern patients requiring varying levels of intervention at the local centre. The Random Forest Decision Tree demonstrates promise in identifying head and neck carcinoma patients who are likely to require ART. The high number of false positives, initially perceived as inaccuracies, underscores the model's ability to detect patients in need of ART, even when a complete rescan is not warranted. This offers the potential to shift from a reactive to a proactive approach to ART. The ML-based predictive model offers a nuanced approach to patient selection, ensuring those who require ART, in varying degrees, are identified and treated accordingly. The transition from a reactive to a proactive approach has potential to improve patient outcomes and streamline clinical practice in ART.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040966","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":"What do users in a polycystic ovary syndrome (PCOS) forum think about the treatments they tried: Analysing treatment sentiment using machine learning.","authors":"Rebecca H K Emanuel, Paul D Docherty, Helen Lunt, Rebecca E Campbell","doi":"10.1007/s13246-025-01539-9","DOIUrl":"https://doi.org/10.1007/s13246-025-01539-9","url":null,"abstract":"<p><p>Polycystic ovary syndrome (PCOS) is a heterogenous condition that is estimated to effect up to 21% of reproductive aged people with ovaries. In previous work, a dataset of PCOS features was derived from approximately 100,000 PCOS subreddit users via machine learning. In this study, an exploration of treatment response within the PCOS subreddit was undertaken with the derived dataset. The treatment or symptom features in the dataset had sentiment labels indicating when a treatment was perceived to improve or worsen a condition or symptom. When different features were mentioned within two sentences of each other without conflicting sentiment, it could be assumed that they were related. This assumption allowed for a broad analysis of the perceived effect of popular treatments on the most frequently mentioned symptoms. In general, lifestyle changes and supplements were the most positively regarded, while contraceptives were frequently associated with considerable negative sentiment. For PCOS weight loss, unspecified dieting (RR 5.19, 95% CI 3.28-8.19, n = 99) and intermittent fasting (RR 33.50, 95% CI 8.54-131.34, n = 69) were the most successful interventions. Inositol was associated with a large range of favourable outcomes and was one of the few treatments associated with improved mental health [depression (RR 4.25, 95% CI 1.72-10.51, n = 21), anxiety (RR 5.83, 95% CI 2.76-12.35, n = 41) and mood issues (RR 25.00, 95% CI 3.65-171.10, n = 26)]. Combined oral contraceptive pills as a whole were strongly associated with adverse effects such as worsening depression (RR 0.06, 95% CI 0.02-0.25, n = 33), anxiety (RR 0.10, 95% CI 0.03-0.36, n = 23), fatigue (RR 0, n = 45) and low libido (RR 0.03, 95% CI 0.01-0.24, n = 30). However, combined contraceptives with anti-androgenic progestins were associated with more favourable experiences. This study demonstrates the utility of machine learning to derive measurable patient experience data from an internet forum. While patient experience data derived using machine learning is not a substitute for traditional clinical trials, it is useful for mass validation and hypothesis generation. This paper may serve as the first exploration into this category of clinical internet forum research.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143991203","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":"Integrated wearable PPG: a multi-vital sign monitoring based on group sparse mode decomposition framework in remote health care using PPG signal.","authors":"Pankaj, Pratibha Maan, Manjeet Kumar, Ashish Kumar, Rama Komaragiri","doi":"10.1007/s13246-025-01534-0","DOIUrl":"https://doi.org/10.1007/s13246-025-01534-0","url":null,"abstract":"<p><p>Monitoring vital signs using a photoplethysmogram (PPG) signal has gained considerable attention, allowing users to monitor anyone, anywhere, and anytime with an objective. In recent years, advances in wearable technology and signal processing techniques have paved the way for accurate and reliable vital sign monitoring using PPG signals. Early detection of cardiovascular diseases can help the physician treat the disease promptly; thus, realtime monitoring of vital signs has emerged. Any deviation in the threshold value of vital signs can indicate potential threats to the cardiovascular system. The need to monitor vital signs in realtime using wearable devices has attracted the interest of the healthcare industry in developing simple and efficient vital sign estimation algorithms. This research introduces a framework to estimate the following important vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), and blood oxygen saturation (SpO2), concurrently by overcoming the limitations posed by state-of-the-art techniques that primarily focus on individual or two vital sign estimations. Our proposed approach leverages signal processing techniques to determine the above-mentioned vital signs seamlessly and accurately. This innovation enhances the efficiency of vital sign monitoring and presents a unified solution for comprehensive health assessment. The widespread use of wearable devices for monitoring realtime health status in everyday life manifests in using PPG sensor-enabled wearable devices to perform more complex computational tasks. To date, the algorithms proposed to process an input PPG signal often use multiple processing steps to estimate any vital signs. This can increase the computational complexity of these algorithms, making it challenging to deploy devices with limited computational resources. The proposed work introduces a computationally efficient framework to estimate all four vital signs using the signal framework. The experimental results obtained with the proposed framework demonstrate that the proposed work outperforms the state-of-the-art estimation accuracy and computational complexity.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796689","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":"Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.","authors":"Ngo Fung Daniel Lam, Jing Cai, Kwan Hoong Ng","doi":"10.1007/s13246-025-01535-z","DOIUrl":"https://doi.org/10.1007/s13246-025-01535-z","url":null,"abstract":"<p><p>Current clinical practice in imaging medical physics is concerned with quality assurance, image processing and analysis, radiation dosimetry, risk assessment and radiation protection, and in-house training and research. Physicist workloads are projected to increase as medical imaging technologies become more sophisticated. Artificial intelligence (AI) is a rising technology with potential to assist medical physicists in their work. Exploration of AI integration into imaging medical physicist workloads is limited. In this review paper, we provide an overview of AI techniques, outline their potential usage in imaging medical physics, and discuss the limitations and challenges to clinical adoption of AI technologies.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701862","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":"AOCMP-SEACOMP 2024 : 10-13 October 2024, Penang, Malaysia.","authors":"","doi":"10.1007/s13246-024-01502-0","DOIUrl":"https://doi.org/10.1007/s13246-024-01502-0","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701861","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":"Analysis of material composition and attenuation characteristics of anthropomorphic torso phantoms for dosimetry using dual energy CT technology.","authors":"Koji Ono, Yasuki Asada","doi":"10.1007/s13246-025-01533-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01533-1","url":null,"abstract":"<p><p>Anthropomorphic phantoms are often used to estimate organ absorbed doses. However, the material composition of these phantoms is not identical to that of the human body, which may cause errors in the measurement results. The purpose of this study was to analyze the material composition of several anthropomorphic torso phantoms using dual energy computed tomography (DECT), and to clarify the differences in attenuation characteristics among the phantoms. Anthropomorphic torso phantoms (ATOM, RANDO, and PBU-60) from different manufacturers were scanned with DECT. The target organs were lung, soft tissue, liver, bone, and bone surface, and a spectral Hounsfield unit curve (HU curve) was created from the relationship between energy and CT values. Ideal CT values were estimated from the mass attenuation coefficient and density proposed by the International Commission on Radiation Units and Measurements report 44 (ideal value) and compared with the values of each phantom. There were large differences in attenuation characteristics among the phantoms for soft tissue, liver, and bone. The respective ideal, ATOM, RANDO, and PBU-60 CT values of soft tissue were 59.82, 14.17, 34.22, and - 70.11 at 45 keV; and 53.13, 24.41, 3.97, and - 5.75 at 70 keV. The phantom closest to the ideal value may differ depending on the energy. Differences in HU curve and CT values indicate that some organs in the phantom have different material composition and attenuation characteristics to human tissues. When the phantoms available for dosimetry are limited, it is important to understand the attenuation characteristics of each phantom used.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659198","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":"Characteristics of the electron beam outside the applicator in an Elekta Versa HD Linac.","authors":"Kapil Dev Maharaj, Simon Goodall, Mahsheed Sabet, Joshua Dass, Mounir Ibrahim, Talat Mahmood, Pejman Rowshanfarzad","doi":"10.1007/s13246-025-01530-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01530-4","url":null,"abstract":"<p><p>Radiotherapy is an essential component of cancer treatment, but healthy tissues can be exposed to out-of-field doses, potentially causing adverse effects and secondary cancers. This study investigates peripheral doses outside the electron beam applicator in an Elekta Versa HD linear accelerator. Peripheral doses outside an electron applicator were measured using 6, 9, and 12 MeV beams at their respective maximum dose depths while maintaining a 100 cm source-to-surface distance. Measurements employed EBT3 films within Plastic Water DT phantoms. The influence of field size on penumbra width and peripheral doses were examined using various cutouts (6 × 6 cm², 10 × 10 cm², and a 5 cm diameter circle) within a 10 × 10 cm² applicator, with gantry and collimator angles set to 0 degrees. Additionally, the impact of collimator angles on penumbra width and peripheral doses was explored, enhancing the understanding of dose distribution. Measured profiles were also compared with those calculated using Monaco treatment planning system. Findings showed that both penumbra width and peripheral dose values increased with energy across different field sizes and collimator angles. Root Mean Square Deviation (RMSD) analysis indicated deviations of 1.8 mm for penumbra and 1.1% for peripheral doses between measured profiles and Treatment Planning System (TPS) predictions for all field sizes. Peripheral doses remained below 5% of the maximum dose at distances ranging from 10 to 15 mm away from the field edges, indicating acceptable tolerance levels (ICRU report 24). However, further dose reduction may be possible with additional shielding to keep doses as low as reasonably achievable. This study highlights the critical importance of considering peripheral doses in radiotherapy, emphasizing the need to evaluate the impact on healthy tissues outside the primary treatment area to ensure patient safety and mitigate long-term treatment-related side effects. The findings underscore the necessity of implementing appropriate measures to minimize peripheral doses.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659202","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}