Physical and Engineering Sciences in Medicine最新文献_第8页

Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network. 基于头皮脑电图集合的卷积神经网络强迫症检测。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-28 DOI: 10.1007/s13246-025-01627-w

Faezeh Ghasemi, Ahmad Shalbaf, Ali Esteki

{"title":"Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network.","authors":"Faezeh Ghasemi, Ahmad Shalbaf, Ali Esteki","doi":"10.1007/s13246-025-01627-w","DOIUrl":"https://doi.org/10.1007/s13246-025-01627-w","url":null,"abstract":"Obsessive-compulsive disorder (OCD) causes unwanted thoughts and repetitive actions and leads to many problems in a person's life. In this study, Electroencephalography (EEG) signals and deep learning methods were used to diagnose OCD patients early. Three popular pre-trained convolutional neural network (CNN) models are developed for scalp-EEG data analysis: EEGNet, Shallow ConvNet, and Deep ConvNet. Three pre-trained CNNs were utilized as transfer learning models. Following the fine-tuning of models with our raw EEG data, an ensemble of three scalp EEG-based CNN models was used, employing weighted majority voting, in which weights of these base classifiers were optimized by the Differential Evolution (DE) algorithm. Shallow ConvNet has the highest performance with an accuracy of 85.91±0.72, sensitivity of 82.19±0.72, and specificity of 93.34±2.91 among all models. Ensemble these three scalp EEG-based CNN models achieved superior performance with an accuracy of 87.03±0.46, sensitivity of 82.21±0.56, and specificity of 96.69±1.28. Consequently, a hybrid proposed model based on pre-treatment raw EEG signals can independently extract distinctive characteristics and accurately identify OCD patients.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974768","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}

引用次数: 0

Comparative study of multi-headed and baseline deep learning models for ADHD classification from EEG signals. 多头与基线深度学习模型在ADHD脑电信号分类中的比较研究。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-26 DOI: 10.1007/s13246-025-01609-y

Lamiaa A Amar, Ahmed M Otifi, Shimaa A Mohamed

{"title":"Comparative study of multi-headed and baseline deep learning models for ADHD classification from EEG signals.","authors":"Lamiaa A Amar, Ahmed M Otifi, Shimaa A Mohamed","doi":"10.1007/s13246-025-01609-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01609-y","url":null,"abstract":"The prevalence of Attention-Deficit/Hyperactivity Disorder among children is rising, emphasizing the need for early and accurate diagnostic methods to address associated academic and behavioral challenges. Electroencephalography-based analysis has emerged as a promising noninvasive approach for detecting Attention-Deficit/Hyperactivity Disorder; however, utilizing the full range of electroencephalography channels often results in high computational complexity and an increased risk of model overfitting. This study presents a comparative investigation between a proposed multi-headed deep learning framework and a traditional baseline single-model approach for classifying Attention-Deficit/Hyperactivity Disorder using electroencephalography signals. Electroencephalography data were collected from 79 participants (42 healthy adults and 37 diagnosed with Attention-Deficit/Hyperactivity Disorder) across four cognitive states: resting with eyes open, resting with eyes closed, performing cognitive tasks, and listening to omniarmonic sounds. To reduce complexity, signals from only five strategically selected electroencephalography channels were used. The multi-headed approach employed parallel deep learning branches-comprising combinations of Bidirectional Long Short-Term Memory, Long Short-Term Memory, and Gated Recurrent Unit architectures-to capture inter-channel relationships and extract richer temporal features. Comparative analysis revealed that the combination of Long Short-Term Memory and Bidirectional Long Short-Term Memory within the multi-headed framework achieved the highest classification accuracy of 89.87%, significantly outperforming all baseline configurations. These results demonstrate the effectiveness of integrating multiple deep learning architectures and highlight the potential of multi-headed models for enhancing electroencephalography-based Attention-Deficit/Hyperactivity Disorder diagnosis.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974728","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}

引用次数: 0

Integrating frequency and dynamic characteristics of EMG signals as a new inter-muscular coordination feature. 肌电信号的频率与动态特征集成作为一种新的肌间协调特征。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-21 DOI: 10.1007/s13246-025-01620-3

Shaghayegh Hassanzadeh Khanmiri, Peyvand Ghaderyan, Alireza Hashemi Oskouei

{"title":"Integrating frequency and dynamic characteristics of EMG signals as a new inter-muscular coordination feature.","authors":"Shaghayegh Hassanzadeh Khanmiri, Peyvand Ghaderyan, Alireza Hashemi Oskouei","doi":"10.1007/s13246-025-01620-3","DOIUrl":"https://doi.org/10.1007/s13246-025-01620-3","url":null,"abstract":"The impairment of inter-muscular coordination and changes in frequency components are two major pathological symptoms associated with knee injuries; however, an effective method to simultaneously quantify these changes has yet to be developed. Moreover, there is a need to propose a reliable automated system for identifying knee injuries to eliminate human errors and enhance reliability and consistency. Hence, this study introduces two novel inter-muscular coordination features: Dynamic Time Warping (DTW) and Dynamic Frequency Warping (DFW), which integrate time and frequency characteristics with a dynamic matching procedure. The support vector machine classifier and two types of dynamic neural network classifiers have also been used to evaluate the effectiveness of the proposed features. The proposed system has been tested using a public dataset that includes five channels of electromyogram (EMG) signals from 33 uninjured subjects and 28 individuals with various types of knee injuries. The experimental results have demonstrated the superiority of DFW and cascade forward neural network, achieving an accuracy rate of 92.03% for detection and 94.42% for categorization of different types of knee injuries. The reliability of the proposed feature has been confirmed in identifying knee injuries using both inter-limb and intra-limb EMG channels. This highlights the potential to offer a trade-off between high detection performance and cost-effective procedures by utilizing fewer channels.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974752","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}

引用次数: 0

Monte Carlo prediction and experimental characterisation of long-lived waste byproducts arising from cyclotron production of zirconium-89 utilising a commercially available yttrium foil. 利用市售钇箔对锆-89回旋产生的长寿命废副产品进行蒙特卡罗预测和实验表征。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-21 DOI: 10.1007/s13246-025-01630-1

Andrew Chacon, Sylvia Gong, Artur Cichocki, Talia Enright, Harris Panopoulos, Nathan Sonnberger, Andrew M Scott, Graeme O'Keefe

{"title":"Monte Carlo prediction and experimental characterisation of long-lived waste byproducts arising from cyclotron production of zirconium-89 utilising a commercially available yttrium foil.","authors":"Andrew Chacon, Sylvia Gong, Artur Cichocki, Talia Enright, Harris Panopoulos, Nathan Sonnberger, Andrew M Scott, Graeme O'Keefe","doi":"10.1007/s13246-025-01630-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01630-1","url":null,"abstract":"Zirconium-89 is presently undergoing pre-clinical investigation for its potential application as a positron emission tomography (PET) theranostic radioisotope. A critical consideration in the increasing number of trials and eventual clinical implementations is a comprehensive understanding of the radioactive waste byproducts and their quantification. This study focuses on the investigation and characterisation of the waste isotopes generated during the production of Zirconium-89, employing a combination of Geant4 Monte Carlo simulation and experimental methodologies utilising commercially obtainable starting materials from Thermofisher. Post cyclotron production samples of waste were taken and measured using a high purity germanium detector. Subsequent spectrum analysis consistently revealed the presence of the following isotopes in units of kBq per GBq of Zirconium-89 produced: cobalt-56 (13 ± 2, 14 ± 2, 15 ± 1), cobalt-57 (0.087 ± 0.004, 0.097 ± 0.004, 0.086 ± 0.007), rhenium-183 (2.61 ± 0.06, 3.29 ± 0.06, 2.47 ± 0.09), scandium-48 (27 ± 0.9, 21.1 ± 0.4), yttrium-88 (0.67 ± 0.06, 1.1 ± 0.4, 0.73 ± 0.06) and zirconium-88 (90 ± 5, 1340 ± 60, 35 ± 2). All the waste isotopes were able to reasonably be estimated using Geant4 Monte Carlo simulations or the deviation was able to be justified. The repeatability and predictability of isotopes and activities will enable informed decision-making regarding storage and disposal procedures in accordance with local legislative requirements.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974832","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}

引用次数: 0

A lightGBM-based method for the signal quality assessment of wrist photoplethysmography. 一种基于lightgbm的腕部光体积脉搏波信号质量评估方法。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-19 DOI: 10.1007/s13246-025-01616-z

Wang Jun, Hui Hui, Yang Handong, Xie Pengfei, Ji Zhong

{"title":"A lightGBM-based method for the signal quality assessment of wrist photoplethysmography.","authors":"Wang Jun, Hui Hui, Yang Handong, Xie Pengfei, Ji Zhong","doi":"10.1007/s13246-025-01616-z","DOIUrl":"10.1007/s13246-025-01616-z","url":null,"abstract":"In the application of wrist-based Photoplethysmography (PPG) devices for health monitoring, assessing the quality of PPG signals is essential for accurately monitoring cardiovascular parameters. However, the wrist-based PPG signal is susceptible to motion and light interference in practical applications. A machine learning-based signal quality assessment algorithm for wrist PPG signals was proposed to improve the accuracy and reliability of the monitoring data. The algorithm's performance was evaluated on two datasets: the publicly available Wearable and Clinical Signals (WCS) dataset, containing 3,038 wrist-based PPG segments collected from 18 volunteers using an Empatica E4 device; our LAB dataset, comprising 2,426 wrist-based PPG segments acquired from 12 volunteers under varied interference conditions via a custom-developed wearable watch system. Data pre-processing encompassed denoising and normalization, followed by the extraction of 11 mathematical statistical features in time and frequency domains based on pulse wave morphology and 2 features based on template matching (Euclidean Distance and Correlation Coefficient). The classifier, constructed using the LightGBM algorithm, achieved high performance under rigorous leave-one-subject-out cross-validation (LOSO-CV) on the WCS dataset (accuracy = 92.6%, precision = 96.6%, recall = 89.8%, F1-score = 91.4%, AUC = 0.925) and the LAB dataset (accuracy = 96.1%, precision = 98.1%, recall = 95.2%, F1-score = 96.6%, AUC = 0.941). The results show that the machine learning algorithm for wrist-based PPG signal quality assessment, combining the mathematical statistical features in time and frequency domains and the template matching features, can effectively enhance the performance of signal quality assessment, and provides a powerful tool for improving the accuracy of wearable devices in cardiovascular health monitoring.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884119","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}

引用次数: 0

Advanced fiber optic systems for efficient medical image transmission: a telemedicine perspective. 用于高效医学图像传输的先进光纤系统：远程医疗视角。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-18 DOI: 10.1007/s13246-025-01622-1

Bengana Abdelfatih, Debbal Mohammed, Bouregaa Moueffeq, Bemmoussat Chemseddine

{"title":"Advanced fiber optic systems for efficient medical image transmission: a telemedicine perspective.","authors":"Bengana Abdelfatih, Debbal Mohammed, Bouregaa Moueffeq, Bemmoussat Chemseddine","doi":"10.1007/s13246-025-01622-1","DOIUrl":"10.1007/s13246-025-01622-1","url":null,"abstract":"The increasing demand for secure, high-quality medical image transmission across healthcare institutions has posed a significant challenge to modern telemedicine systems. Traditional network infrastructures often fail to provide sufficient bandwidth and low latency required for transferring large volumes of high-resolution medical images, such as MRI and CT scans, over long distances. To address this limitation, a fiber-optic transmission framework was designed and evaluated with the objective of enhancing the speed, reliability, and accuracy of inter-hospital medical image sharing. In this study, a simulation-based approach was employed using OPTISYSTEM and MATLAB to model the optical transmission chain, including stages of image digitization, modulation, fiber propagation, and optical-to-electrical conversion at the receiving end. Various performance parameters such as Bit Error Rate (BER), Quality Factor (Q), transmission power, and noise levels were analyzed for different image resolutions and transmission distances. The results showed that Q-Factor values between 8.5 and 9.5 were obtained, with BER reaching values as low as 10⁻20, even for high-resolution images transmitted over distances up to 90 km. These results were compared to existing benchmarks in the literature and demonstrated superior performance. The proposed system exhibited strong robustness in handling large image datasets, with minimal signal distortion and negligible transmission errors. It was concluded that the adoption of this fiber-optic architecture could significantly improve the efficiency of telemedicine applications, offering a reliable and high-capacity solution for real-time diagnostic collaboration and patient monitoring between geographically distributed medical facilities.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144876056","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}

引用次数: 0

PET-based radiomic analysis in multicentre lung cancer study and impact of feature domain harmonization. 基于pet的多中心肺癌研究放射学分析及特征域协调的影响。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-18 DOI: 10.1007/s13246-025-01625-y

Pooja Dwivedi, Sagar Barage, Rajshri Singh, Ashish Jha, Sayak Choudhury, Archi Agrawal, Venkatesh Rangarajan

{"title":"PET-based radiomic analysis in multicentre lung cancer study and impact of feature domain harmonization.","authors":"Pooja Dwivedi, Sagar Barage, Rajshri Singh, Ashish Jha, Sayak Choudhury, Archi Agrawal, Venkatesh Rangarajan","doi":"10.1007/s13246-025-01625-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01625-y","url":null,"abstract":"Radiomic biomarkers have demonstrated significant potential in non-invasively assessing tumor biology and providing essential insights for precision medicine. However, the clinical translation is often hindered by challenges in multicenter studies, primarily due to a lack of standardization, such as variations in scanner models, acquisition protocols, reconstruction techniques, etc. This study aims to assess the impact of various harmonization methods in multicenter, 18 F-FDG PET-based radiomics for the classification of lung cancer histological subtypes using a machine learning model. Retrospective data included 178 lung cancer cohorts, comprising 117 adenocarcinomas and 61 squamous cell carcinomas from three different centers. PET DICOM image data was preprocessed with 3D ROI segmentation of the lung tumor and healthy liver, followed by the extraction of 111 radiomic features. Subsequently, Z-Score, Quantile, and ComBat were applied to generate three different harmonized datasets. Feature distribution was analyzed, and the top ten features were selected using recursive feature elimination. An eXtreme gradient boosting model was built on each dataset, and performance was assessed using accuracy, precision, sensitivity, specificity, and AUC with a 95% confidence interval. Variations in radiomic feature distribution and feature selection were observed after applying different harmonization methods. During validation of the trained model, AUC improved from 0.556 [95% CI 0.551-0.563] in the unharmonized data to 0.719 [95% CI 0.710-0.720], 0.952 [95% CI 0.951-0.954], and 0.996 [95% CI 0.995-0.996] in Z-Score, Quantile, and ComBat harmonized data, respectively, for classifying adenocarcinoma and squamous cell carcinoma subtypes. The study indicates that feature selection was affected by the different harmonization methods. The ComBat method was shown to significantly enhance the performance of AI-assisted PET radiomics.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144876057","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}

引用次数: 0

Synthetic film-based testing of film quality assurance software accuracy. 基于合成薄膜测试的薄膜质量保证软件的准确性。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-13 DOI: 10.1007/s13246-025-01607-0

O Kamst, D Firth

{"title":"Synthetic film-based testing of film quality assurance software accuracy.","authors":"O Kamst, D Firth","doi":"10.1007/s13246-025-01607-0","DOIUrl":"https://doi.org/10.1007/s13246-025-01607-0","url":null,"abstract":"Film quality assurance software is an integral component of patient-specific quality assurance for various radiation techniques where high degrees of geometrical and dosimetric accuracy are required. Evaluating the accuracy of film quality assurance software products has relied on various techniques ranging from comparative analyses, measurements with phantoms and other detectors, along with confidence from industry-standard peer reviews. The aim of this work was to determine if a series of synthetically created film and DICOM images can be used to test the accuracy of certain patient specific quality assurance metrics used in film quality assurance software packages. The synthetic images have been engineered to simulate radiographic film scanned TIFF images and treatment planning system exported DICOM files. Each pair of images were designed to test a particular component of the software's ability to process curve fitting, dosimetric differences, distance to agreement, percentage threshold and gamma analysis. It was found that synthetic film could simulate radiographic scanned films and treatment planning system DICOM planes and provide the physicist with empirical data on the accuracy of the mentioned metrics. The series of tests also assisted the physicist in identifying optimal calibration models, validating geometric and dosimetric variations, and offering insights into potential differences in lower dose penumbras.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838282","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}

引用次数: 0

Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease. 从轻度认知障碍到阿尔茨海默病转换的多模态预测的可解释的分层机器学习方法。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-11 DOI: 10.1007/s13246-025-01618-x

Soheil Zarei, Mohsen Saffar, Reza Shalbaf, Peyman Hassani Abharian, Ahmad Shalbaf

{"title":"Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease.","authors":"Soheil Zarei, Mohsen Saffar, Reza Shalbaf, Peyman Hassani Abharian, Ahmad Shalbaf","doi":"10.1007/s13246-025-01618-x","DOIUrl":"https://doi.org/10.1007/s13246-025-01618-x","url":null,"abstract":"Alzheimer's disease (AD) is a neurodegenerative disorder that challenges early diagnosis and intervention, yet the black-box nature of many predictive models limits clinical adoption. In this study, we developed an advanced machine learning (ML) framework that integrates hierarchical feature selection with multiple classifiers to predict progression from mild cognitive impairment (MCI) to AD. Using baseline data from 580 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), categorized into stable MCI (sMCI) and progressive MCI (pMCI) subgroups, we analyzed features both individually and across seven key groups. The neuropsychological test group exhibited the highest predictive power, with several of the top individual predictors drawn from this domain. Hierarchical feature selection combining initial statistical filtering and machine learning based refinement, narrowed the feature set to the eight most informative variables. To demystify model decisions, we applied SHAP-based (SHapley Additive exPlanations) explainability analysis, quantifying each feature's contribution to conversion risk. The explainable random forest classifier, optimized on these selected features, achieved 83.79% accuracy (84.93% sensitivity, 83.32% specificity), outperforming other methods and revealing hippocampal volume, delayed memory recall (LDELTOTAL), and Functional Activities Questionnaire (FAQ) scores as the top drivers of conversion. These results underscore the effectiveness of combining diverse data sources with advanced ML models, and demonstrate that transparent, SHAP-driven insights align with known AD biomarkers, transforming our model from a predictive black box into a clinically actionable tool for early diagnosis and patient stratification.","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":"144817988","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}

引用次数: 0

A novel method for measuring the backscatter factor on a curved surface for diagnostic X-rays using a flexible scintillator sheet. 一种利用柔性闪烁片测量诊断x射线曲面后向散射系数的新方法。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-08-11 DOI: 10.1007/s13246-025-01624-z

Kohei Nakanishi, Seiichi Yamamoto, Masato Yoshida, Kenta Miwa, Ryuichi Nishii

{"title":"A novel method for measuring the backscatter factor on a curved surface for diagnostic X-rays using a flexible scintillator sheet.","authors":"Kohei Nakanishi, Seiichi Yamamoto, Masato Yoshida, Kenta Miwa, Ryuichi Nishii","doi":"10.1007/s13246-025-01624-z","DOIUrl":"10.1007/s13246-025-01624-z","url":null,"abstract":"The ESD is calculated using the backscatter factor (BSF). However, BSFs for flat surfaces have been used even though simulations have shown that the BSFs for curved surfaces, which represent the human body more accurately, do not match those for flat surfaces. Measuring these values in practice presents a challenge because conventional dosimeters used for BSF measurement have sensitive volumes that cannot conform to curved surfaces. In this study, we measured the BSF for a curved surface using a flexible scintillator. The scintillator, composed of Gd₃Al₂Ga₃O₁₂ (GAGG) scintillator powder mixed with a silicone adhesive, was securely attached to the curved surface of a cylindrical phantom. Diagnostic X-rays were irradiated onto the scintillator, and the BSFs were evaluated as the ratio of the light output with and without the phantom. We successfully measured BSFs on a curved surface using a flexible scintillator. The mean difference between the BSFs obtained from the experiments using the flexible scintillator and those obtained from the simulations for the cylindrical phantom was 0.43%. The maximum difference was 1.47%, which was observed at a tube voltage of 40 kV. Thus, the BSFs measured using the flexible scintillator agree well with the simulated results. Our scintillator is useful for measuring BSFs on curved surfaces and contributes to dose management.","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":"144817978","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}

引用次数: 0