Physical and Engineering Sciences in Medicine最新文献_第3页

Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images. 更正：利用单通道脑电图和脑电图图像自动检测精神分裂症的迁移学习和自振。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-02-04 DOI: 10.1007/s13246-024-01501-1

Mohammadreza Mostafavi, Seok-Bum Ko, Shahriar Baradaran Shokouhi, Ahmad Ayatollahi

引用次数: 0

A new HCM heart sound classification method based on weighted bispectrum features. 基于加权双谱特征的HCM心音分类新方法。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-30 DOI: 10.1007/s13246-024-01506-w

Fang Yu, Huang Zhiyuan, Leng Hongxia, Dongbo Liu, Wang Weibo

{"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":"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.","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}

引用次数: 0

Estimation of dose to a bystander from F-18 FDG patients using Monte Carlo simulation in clinical exposure scenarios. 使用蒙特卡罗模拟在临床暴露情景中估计F-18 FDG患者对旁观者的剂量

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-29 DOI: 10.1007/s13246-025-01518-0

K W N S Samaranayake, Erin Mckay, Thomas Hennessy

{"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":"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.","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}

引用次数: 0

Measurement and spectral analysis of medical shock wave parameters based on flexible PVDF sensors. 基于柔性PVDF传感器的医疗冲击波参数测量与光谱分析。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-28 DOI: 10.1007/s13246-025-01519-z

Liansheng Xu, Fei Shen, Fan Fan, Qiong Wu, Li Wang, Fengji Li, Yubo Fan, Haijun Niu

{"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":"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.","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}

引用次数: 0

Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms. 性别、脑区和脑电图频带复杂度分析在帕金森病递归图和机器学习分类中的意义

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-27 DOI: 10.1007/s13246-025-01521-5

Divya Sasidharan, V Sowmya, E A Gopalakrishnan

{"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":"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.","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}

引用次数: 0

Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children. 基于自动编码器的数据聚类，用于识别儿童异常重复的手部运动和行为转变模式。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-21 DOI: 10.1007/s13246-024-01507-9

Nushara Wedasingha, Pradeepa Samarasinghe, Lasantha Senevirathna, Michela Papandrea, Alessandro Puiatti

{"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":"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.","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}

引用次数: 0

Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis. 临床放疗中人工智能自动分割系统的选择和评估指南：来自六家供应商分析的见解。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-13 DOI: 10.1007/s13246-024-01513-x

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

{"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":"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.","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}

引用次数: 0

Evaluating the prognostic value of radiomics and clinical features in metastatic prostate cancer using [⁶⁸Ga]Ga-PSMA-11 PET/CT. 应用[68Ga]Ga-PSMA-11 PET/CT评价转移性前列腺癌放射组学和临床特征的预后价值。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-09 DOI: 10.1007/s13246-024-01516-8

Kaylee Molin, Nathaniel Barry, Suki Gill, Ghulam Mubashar Hassan, Roslyn J Francis, Jeremy S L Ong, Martin A Ebert, Jake Kendrick

{"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":"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.","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}

引用次数: 0

In-silico evaluation of the effect of set-up errors on dose delivery during mouse irradiations with a Cs-137 cell irradiator-based collimator system. 基于Cs-137细胞辐照器的准直系统对小鼠辐照过程中设置误差对剂量传递影响的计算机评价。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-08 DOI: 10.1007/s13246-024-01486-x

Amir Entezam, Andrew Fielding, Gishan Ratnayake, Davide Fontanarosa

{"title":"In-silico evaluation of the effect of set-up errors on dose delivery during mouse irradiations with a Cs-137 cell irradiator-based collimator system.","authors":"Amir Entezam, Andrew Fielding, Gishan Ratnayake, Davide Fontanarosa","doi":"10.1007/s13246-024-01486-x","DOIUrl":"https://doi.org/10.1007/s13246-024-01486-x","url":null,"abstract":"Set-up errors are a problem for pre-clinical irradiators that lack imaging capabilities. The aim of this study was to investigate the impact of the potential set-up errors on the dose distribution for a mouse with a xenographic tumour irradiated with a standard Cs-137 cell irradiator equipped with an in-house lead collimator with 10 mm diameter apertures. The EGSnrc Monte-Carlo (MC) code was used to simulate the potential errors caused by displacements of the mouse in the irradiation setup. The impact of the simulated set-up displacements on the dose delivered to the xenographic tumour and surrounding organs was assessed. MC dose calculations were performed in a Computed Tomography (CT) derived model of the mouse for the reference position of the tumour in the irradiation setup. The errors were added into the CT data and then the mouse doses for the corresponding shifts were recalculated and dose volume histograms (DVHs) were generated. The investigation was performed for 1 cm and 0.5 cm diameter tumours. The DVH resulting from introducing the maximum setup errors for 1 cm diameter tumours showed up to 35% reduced dose to a significant fraction of the tumour volume. The setup errors demonstrated an insignificant effect on doses for 0.5 cm diameter tumour irradiations. Setup errors were observed to have negligible impact on out of field doses to organs at risk. The dosimetric results presented herein verify the robustness of our collimator system for irradiations of xenograft tumours up to 0.5 cm diameter in the presence of the maximum setup errors.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956847","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

Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis. 使用TriVOCTNet增强经皮冠状动脉介入治疗：用于血管内光学相干断层扫描综合分析的多任务深度学习模型。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2025-01-06 DOI: 10.1007/s13246-024-01509-7

Yu Shi Lau, Li Kuo Tan, Kok Han Chee, Chow Khuen Chan, Yih Miin Liew

{"title":"Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis.","authors":"Yu Shi Lau, Li Kuo Tan, Kok Han Chee, Chow Khuen Chan, Yih Miin Liew","doi":"10.1007/s13246-024-01509-7","DOIUrl":"https://doi.org/10.1007/s13246-024-01509-7","url":null,"abstract":"Neointimal coverage and stent apposition, as assessed from intravascular optical coherence tomography (IVOCT) images, are crucial for optimizing percutaneous coronary intervention (PCI). Existing state-of-the-art computer algorithms designed to automate this analysis often treat lumen and stent segmentations as separate target entities, applicable only to a single stent type and overlook automation of preselecting which pullback segments need segmentation, thus limit their practicality. This study aimed for an algorithm capable of intelligently handling the entire IVOCT pullback across different phases of PCI and clinical scenarios, including the presence and coexistence of metal and bioresorbable vascular scaffold (BVS), stent types. We propose a multi-task deep learning model, named TriVOCTNet, that automates image classification/selection, lumen segmentation and stent struts segmentation within a single network by integrating classification, regression and pixel-level segmentation models. This approach allowed a single-network, single-pass implementation with all tasks parallelized for speed and convenience. A joint loss function was specifically designed to optimize each task in situations where each task may or may not be present. Evaluation on 4,746 images achieved classification accuracies of 0.999, 0.997, and 0.998 for lumen, BVS, and metal stent features, respectively. The lumen segmentation performance showed a Euclidean distance error of 21.72 μm and Dice's coefficient of 0.985. For BVS struts segmentation, the Dice's coefficient was 0.896, and for metal stent struts segmentation, the precision was 0.895 and sensitivity was 0.868. TriVOCTNet highlights its clinical potential due to its fast and accurate results, and simplicity in handling all tasks and scenarios through a single system.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933187","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