Physical and Engineering Sciences in Medicine最新文献

{"title":"A novel method for early prediction of sudden cardiac death through nonlinear feature extraction from ECG signals.","authors":"Fatemeh Danesh Jablo, Hamed Danandeh Hesar","doi":"10.1007/s13246-025-01517-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01517-1","url":null,"abstract":"<p><p>Sudden cardiac death (SCD) is a critical cardiovascular issue affecting approximately 3 million individuals globally each year, often occurring without prior noticeable symptoms. While the precise etiology of SCD remains elusive, ventricular fibrillation is believed to play a pivotal role in its pathophysiology. Given that symptoms typically manifest only an hour before the event, timely prediction is crucial for effective cardiac resuscitation. This study aims to predict SCD using time-frequency analysis of ECG signals. We utilized two online datasets: the Sudden Cardiac Death Holter dataset and the MIT-BIH Normal Sinus Rhythm dataset. Our proposed method involves segmenting the 60-min interval preceding ventricular fibrillation into one-minute segments, which are then decomposed into time-frequency sub-bands using empirical mode decomposition (EMD). Nonlinear features are extracted from these decomposed signals, followed by classification using support vector machines (SVM) and K-nearest neighbors (KNN) algorithms. To enhance classification accuracy, we employed two statistical feature selection techniques: T-test and ANOVA. Results indicate that using the ANOVA feature selection method in conjunction with SVM and KNN algorithms achieves high accuracy in predicting SCD. Specifically, the average accuracy rates for the 60 min preceding SCD were 93.51% for ANOVA-SVM and 93% for ANOVA-KNN. With T-test feature selection, the average accuracy rates were 93.29% for SVM and 93.41% for KNN. These findings demonstrate the promising performance of our proposed approach in predicting SCD, potentially contributing to improved early intervention strategies and patient outcomes.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383558","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":"Towards ultra-low-dose CT for detecting pulmonary nodules using DenseNet.","authors":"Ching-Ching Yang","doi":"10.1007/s13246-025-01520-6","DOIUrl":"https://doi.org/10.1007/s13246-025-01520-6","url":null,"abstract":"<p><p>Low-radiation techniques should be used to detect and follow lung nodules on CT images, but reducing radiation dose to ultra-low-dose CT with submilliSievert dose level would drastically impede image quality and sensitivity for nodule detection. This study investigated the feasibility of using DenseNet to suppress image noise in ultra-low-dose CT for lung cancer screening. DenseNet was trained using input-label pairs from 1, 2, 4, and 6 patients. After training, the model was tested with chest CT from 14 patients that were not used in training process. Seven patients have solid nodules and 7 patients have subsolid nodules. Root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) were calculated to quantify the difference between reference and test images. The contrast-to-noise ratio (CNR) between lung nodule and lung parenchyma was calculated to evaluate the target detectability of chest CT. Subjective image quality assessment was performed using 4-point ranking scale to evaluate the visual quality of CT images perceived by end user. Substantial improvements in RMSE and PSNR were observed after denoising. The lung nodules in denoised images could be distinguished more easily in comparison with those in the original ultra-low-dose CT, which is supported by the CNRs and subjective image quality scores. The comparison of intensity profiles for lung nodules demonstrated that the image noise in ultra-low-dose CT could be suppressed effectively after denoising without causing edge blurring or variation in Hounsfield unit (HU) values. A two-sample t-test revealed no statistically significant differences between full-dose CT and denoised ultra-low-dose CT in the evaluation of lung nodules, lung parenchyma, paraspinal muscle, or vertebral body. Since the linear no-threshold model suggests that no amount of ionizing radiation is entirely risk-free, the quest for further dose reduction remains a consistently important focus in radiology. Overall, our findings suggest that DenseNet could be a viable approach for reducing image noise in ultra-low-dose CT scans used for lung cancer screening.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383564","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":"Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.","authors":"Mohammadreza Mostafavi, Seok-Bum Ko, Shahriar Baradaran Shokouhi, Ahmad Ayatollahi","doi":"10.1007/s13246-024-01501-1","DOIUrl":"https://doi.org/10.1007/s13246-024-01501-1","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190961","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 new HCM heart sound classification method based on weighted bispectrum features.","authors":"Fang Yu, Huang Zhiyuan, Leng Hongxia, Dongbo Liu, Wang Weibo","doi":"10.1007/s13246-024-01506-w","DOIUrl":"https://doi.org/10.1007/s13246-024-01506-w","url":null,"abstract":"<p><p>Hypertrophic cardiomyopathy (HCM), including obstructive HCM and non-obstructive HCM, can lead to sudden cardiac arrest in adolescents and athletes. Early diagnosis and treatment through auscultation of different types of HCM can prevent the occurrence of malignant events. However, it is challenging to distinguish the pathological information of HCM related to differential left ventricular outflow tract pressure gradients. To address this issue, a classification method based on weighted bispectrum features of heart sounds (HSs) is proposed for efficient and cost-effective HCM analysis. Preprocessing is first applied to remove background noise during HS acquisition. Then, the bispectrum contour map is calculated, and 56-dimensional features are extracted to represent the pathological information of HCM. Next, an adaptive threshold weighting mutual information method is proposed for feature selection and weighted fusion. Finally, the CNN-RF classifier model is built to automatically identify different types of HCM cases. A clinical dataset of normal and two types of HCM HSs is utilized for validation. The results show that the proposed method performs well, with a classification accuracy reaching 94.4%. It provides a reliable reference for HCM diagnosis in young patients in clinical settings.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068837","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":"Estimation of dose to a bystander from F-18 FDG patients using Monte Carlo simulation in clinical exposure scenarios.","authors":"K W N S Samaranayake, Erin Mckay, Thomas Hennessy","doi":"10.1007/s13246-025-01518-0","DOIUrl":"https://doi.org/10.1007/s13246-025-01518-0","url":null,"abstract":"<p><p>The radiation exposure to bystanders from nuclear medicine patients is a common concern raised in nuclear medicine departments. The GATE (Geant4 Application for Tomographic Emission) Monte Carlo radiation transport application was used to estimate the dose to a bystander. Two voxelised phantoms were utilised in a GATE Monte Carlo simulation as the radiation source and target. The absorbed dose to the target phantom from radiation emitted by the source phantom was calculated. Three experimental setups of increasing complexity, with the last one replicating clinical dose rate measurements, were used to validate the simulation results. Four clinical scenarios were simulated to estimate the dose to a healthcare worker from F-18 FDG patients: an ultrasound procedure, two surgical procedures (head and chest), and a face-to-face consultation. The mean absorbed dose to the foetus was also estimated using the same method and pregnant female phantoms as target for ultrasound scan scenario. The effective dose to a healthcare worker from an FDG PET patient who has had 250 MBq of FDG injection 3 h post procedures was estimated as: 18.1 ± 0.1 µSv for 30-minute ultrasound scan, 36.5 ± 0.3 µSv for 1-hour chest surgical procedure, and 9.3 ± 0.1 µSv for 15-minute face to face consultation scenario. This method can be easily extended to estimate the dose to bystanders from nuclear medicine patients injected with various radioisotopes in different clinical scenarios.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068840","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":"Measurement and spectral analysis of medical shock wave parameters based on flexible PVDF sensors.","authors":"Liansheng Xu, Fei Shen, Fan Fan, Qiong Wu, Li Wang, Fengji Li, Yubo Fan, Haijun Niu","doi":"10.1007/s13246-025-01519-z","DOIUrl":"https://doi.org/10.1007/s13246-025-01519-z","url":null,"abstract":"<p><p>Extracorporeal shock wave therapy (ESWT) achieves its therapeutic purpose mainly through the biological effects produced by the interaction of shock waves with tissues, and the accurate measurement and calculation of the mechanical parameters of shock waves in tissues are of great significance in formulating the therapeutic strategy and evaluating the therapeutic effect. This study utilizes the approach of implanting flexible polyvinylidene fluoride (PVDF) vibration sensors inside the tissue-mimicking phantom of various thicknesses to capture waveforms at different depths during the impact process in real time. Parameters including positive and negative pressure changes (P₊, P_-), pulse wave rise time ([Formula: see text]), and energy flux density (EFD) are calculated, and frequency spectrum analysis of the waveforms is conducted. The dynamic response, propagation process, and attenuation law of the shock wave in the phantom under different impact intensities were analyzed. Results showed that flexible PVDF sensors could precisely acquire the characteristics of pulse waveform propagating within the phantom. At the same depth, as the driving pressure increases, P₊ and P_- increase linearly, and [Formula: see text] remains constant. At the same driving pressure, P₊, P_-, and EFD decay exponentially with increasing propagation depth. At the same depth, the spectra of pulse waveforms are similar, and the increasing driving pressure does not cause significant changes in carrier frequency and modulation frequency. The research findings could provide a reference for developing ESWT devices, improving treatment strategies, and enhancing the safety of clinical applications.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053861","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":"Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.","authors":"Divya Sasidharan, V Sowmya, E A Gopalakrishnan","doi":"10.1007/s13246-025-01521-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01521-5","url":null,"abstract":"<p><p>Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification. For this an open EEG dataset consisting of 14 PD and 14 healthy (HC) subjects is utilized. Recurrence plots and cross-recurrence plots (CRPs) were constructed for each frequency band and brain region, extracting complexity measures such as determinism (DET) and entropy (ENT). The interpretability of the ML model decisions is investigated using explainability technique. The scattered distribution of points in RPs of male PD individuals reflects the complex and dynamic nature of abnormal brain function. Also, CRPs confirms the enhanced effect of Beta Gamma synchronization during PD in the Parietal region. Low DET and high ENT corresponds to the complex non-linear characteristics of EEG signals and brain neuronal circuits during PD condition in male subjects. The extracted recurrence features served as inputs to the ML models, which achieved high classification performance, across all the scenarios. This study demonstrates the potential of recurrence plot-based complexity analysis combined with machine learning for the gender-specific, region-specific, and band-specific assessment of EEG signals during resting state in Parkinson's disease.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048411","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":"Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children.","authors":"Nushara Wedasingha, Pradeepa Samarasinghe, Lasantha Senevirathna, Michela Papandrea, Alessandro Puiatti","doi":"10.1007/s13246-024-01507-9","DOIUrl":"https://doi.org/10.1007/s13246-024-01507-9","url":null,"abstract":"<p><p>The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child's well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014385","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":"Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.","authors":"Branimir Rusanov, Martin A Ebert, Mahsheed Sabet, Pejman Rowshanfarzad, Nathaniel Barry, Jake Kendrick, Zaid Alkhatib, Suki Gill, Joshua Dass, Nicholas Bucknell, Jeremy Croker, Colin Tang, Rohen White, Sean Bydder, Mandy Taylor, Luke Slama, Godfrey Mukwada","doi":"10.1007/s13246-024-01513-x","DOIUrl":"https://doi.org/10.1007/s13246-024-01513-x","url":null,"abstract":"<p><p>Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI. In-house experience and key bodies of work on ethics, standards, and best practices for AI in Radiation Oncology were reviewed to inform selection criteria and evaluation strategies. A retrospective analysis using the criteria was performed across six vendors, including a quantitative assessment using five metrics (Dice, Hausdorff Distance, Average Surface Distance, Surface Dice, Added Path Length) across 20 head and neck, 20 thoracic, and 19 male pelvis patients for AI models as of March 2023. A total of 47 selection criteria were identified across seven categories. A retrospective analysis showed that overall no vendor performed exceedingly well, with systematically poor performance in Data Security & Responsibility, Vendor Support Tools, and Transparency & Ethics. In terms of raw performance, vendors varied widely from excellent to poor. As new regulations come into force and the scope of AI auto-segmentation systems adapt to clinical needs, continued interest in ensuring safe, fair, and transparent AI will persist. The selection and evaluation framework provided herein aims to promote user confidence by exploring the breadth of clinically relevant factors to support informed decision-making.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972841","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":"Evaluating the prognostic value of radiomics and clinical features in metastatic prostate cancer using [⁶⁸Ga]Ga-PSMA-11 PET/CT.","authors":"Kaylee Molin, Nathaniel Barry, Suki Gill, Ghulam Mubashar Hassan, Roslyn J Francis, Jeremy S L Ong, Martin A Ebert, Jake Kendrick","doi":"10.1007/s13246-024-01516-8","DOIUrl":"https://doi.org/10.1007/s13246-024-01516-8","url":null,"abstract":"<p><p>Prostate cancer is a significant global health issue due to its high incidence and poor outcomes in metastatic disease. This study aims to develop models predicting overall survival for patients with metastatic biochemically recurrent prostate cancer, potentially helping to identify high-risk patients and enabling more tailored treatment options. A multi-centre cohort of 180 such patients underwent [⁶⁸Ga]Ga-PSMA-11 PET/CT scans, with lesions semi-automatically segmented and radiomic features extracted from lesions. The analysis included two phases: univariable and multivariable. Univariable analysis used Kaplan-Meier curves and Cox proportional hazards models to correlate individual features with overall survival. Multivariable analysis used the LASSO Cox proportional hazards method to create 13 models: radiomics-only, clinical-only, and various combinations of radiomic and clinical features. Each model included six features and was bootstrapped 1000 times to obtain concordance indices with 95% confidence intervals, followed by optimism correction. In the univariable analysis, 6 out of 8 clinical features and 68 out of 89 radiomic features were significantly correlated with overall survival, including age, disease stage, total lesional uptake and total lesional volume. The optimism-corrected concordance indices from the multivariable models were 0.722 (95% CI 0.653-0.784) for the clinical model, 0.681 (95% CI 0.616-0.745) for the radiomics model, and 0.704 (95% CI 0.648-0.768) for the combined model with three clinical and three radiomic features, when extracting radiomic features from the largest lesion only. While univariable analysis showed significant prognostic value for many radiomic features, their integration into multivariable models did not improve predictive accuracy beyond clinical features alone.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956790","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}