Healthcare Technology Letters最新文献_第5页

RGB to hyperspectral: Spectral reconstruction for enhanced surgical imaging RGB到高光谱：用于增强外科成像的光谱重建。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-11-25 DOI: 10.1049/htl2.12098

Tobias Czempiel, Alfie Roddan, Maria Leiloglou, Zepeng Hu, Kevin O'Neill, Giulio Anichini, Danail Stoyanov, Daniel Elson

{"title":"RGB to hyperspectral: Spectral reconstruction for enhanced surgical imaging","authors":"Tobias Czempiel, Alfie Roddan, Maria Leiloglou, Zepeng Hu, Kevin O'Neill, Giulio Anichini, Danail Stoyanov, Daniel Elson","doi":"10.1049/htl2.12098","DOIUrl":"10.1049/htl2.12098","url":null,"abstract":"This study investigates the reconstruction of hyperspectral signatures from RGB data to enhance surgical imaging, utilizing the publicly available HeiPorSPECTRAL dataset from porcine surgery and an in-house neurosurgery dataset. Various architectures based on convolutional neural networks (CNNs) and transformer models are evaluated using comprehensive metrics. Transformer models exhibit superior performance in terms of RMSE, SAM, PSNR and SSIM by effectively integrating spatial information to predict accurate spectral profiles, encompassing both visible and extended spectral ranges. Qualitative assessments demonstrate the capability to predict spectral profiles critical for informed surgical decision-making during procedures. Challenges associated with capturing both the visible and extended hyperspectral ranges are highlighted using the MAE, emphasizing the complexities involved. The findings open up the new research direction of hyperspectral reconstruction for surgical applications and clinical use cases in real-time surgical environments.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"307-317"},"PeriodicalIF":2.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.8

Healthcare Technology Letters Pub Date : 2024-11-22 DOI: 10.1049/htl2.12097

Najmeh Sadat Jaddi, Salwani Abdullah, Say Leng Goh, Mohammad Kamrul Hasan

{"title":"A related convolutional neural network for cancer diagnosis using microRNA data classification","authors":"Najmeh Sadat Jaddi, Salwani Abdullah, Say Leng Goh, Mohammad Kamrul Hasan","doi":"10.1049/htl2.12097","DOIUrl":"10.1049/htl2.12097","url":null,"abstract":"This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs. The method's performance is boosted by the relationship between CNNs and exchanging knowledge between them. Besides, communication between small sizes of CNNs reduces the need for large size CNNs and, consequently, the computational time and memory usage while preserving high accuracy. The method proposed is tested on microRNA dataset containing the genomic information of 8129 patients for 29 different types of cancer with 1046 gene expression. The classification accuracy of the selected genes obtained by the proposed approach is compared with the accuracy of 22 well-known classifiers on a real-world dataset. The classification accuracy of each cancer type is also ranked with the results of 77 classifiers reported in previous works. The proposed approach shows accuracy of 100% in 24 out of 29 classes and in seven cases out of 29, the method achieved 100% accuracy that no classifier in other studies has reached. Performance analysis is performed using performance metrics.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"485-495"},"PeriodicalIF":2.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge maps as a complementary tool to learn and teach surgical anatomy in virtual reality: A case study in dental implantology 知识图谱作为一种辅助工具，在虚拟现实中学习和教授外科解剖学：牙科种植学的案例研究。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-11-08 DOI: 10.1049/htl2.12094

Inês M. Lúcio, Bernardo G. de Faria, Renata G. Raidou, Luís Proença, Carlos Zagalo, José João Mendes, Pedro Rodrigues, Daniel Simões Lopes

{"title":"Knowledge maps as a complementary tool to learn and teach surgical anatomy in virtual reality: A case study in dental implantology","authors":"Inês M. Lúcio, Bernardo G. de Faria, Renata G. Raidou, Luís Proença, Carlos Zagalo, José João Mendes, Pedro Rodrigues, Daniel Simões Lopes","doi":"10.1049/htl2.12094","DOIUrl":"10.1049/htl2.12094","url":null,"abstract":"A thorough understanding of surgical anatomy is essential for preparing and training medical students to become competent and skilled surgeons. While Virtual Reality (VR) has shown to be a suitable interaction paradigm for surgical training, traditional anatomical VR models often rely on simple labels and arrows pointing to relevant landmarks. Yet, studies have indicated that such visual settings could benefit from knowledge maps as such representations explicitly illustrate the conceptual connections between anatomical landmarks. In this article, a VR educational tool is presented designed to explore the potential of knowledge maps as a complementary visual encoding for labeled 3D anatomy models. Focusing on surgical anatomy for implantology, it was investigated whether integrating knowledge maps within a VR environment could improve students' understanding and retention of complex anatomical relationships. The study involved 30 master's students in dentistry and 3 anatomy teachers, who used the tool and were subsequently assessed through surgical anatomy quizzes (measuring both completion times and scores) and subjective feedback (assessing user satisfaction, preferences, system usability, and task workload). The results showed that using knowledge maps in an immersive environment facilitates learning and teaching surgical anatomy applied to implantology, serving as a complementary tool to conventional VR educational methods.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"289-300"},"PeriodicalIF":2.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep fusion-based vision transformer for breast cancer classification 一种基于深度融合的乳腺癌分类视觉变换器。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-10-23 DOI: 10.1049/htl2.12093

Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza

{"title":"A deep fusion-based vision transformer for breast cancer classification","authors":"Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza","doi":"10.1049/htl2.12093","DOIUrl":"10.1049/htl2.12093","url":null,"abstract":"Breast cancer is one of the most common causes of death in women in the modern world. Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception-V1, and VGG-16, while useful in many applications, cannot capture the patterns of cell layers and staining properties. Most previous approaches, such as stain normalization and instance-based vision transformers, either miss important features or do not process the whole image effectively. Therefore, a deep fusion-based vision Transformer model (DFViT) that combines CNNs and transformers for better feature extraction is proposed. DFViT captures local and global patterns more effectively by fusing RGB and stain-normalized images. Trained and tested on several datasets, such as BreakHis, breast cancer histology (BACH), and UCSC cancer genomics (UC), the results demonstrate outstanding accuracy, F1 score, precision, and recall, setting a new milestone in histopathological image analysis for diagnosing breast cancer.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"471-484"},"PeriodicalIF":2.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A secure blockchain framework for healthcare records management systems 用于医疗记录管理系统的安全区块链框架。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-10-09 DOI: 10.1049/htl2.12092

Mahmoud Ahmad Al-Khasawneh, Muhammad Faheem, Ala Abdulsalam Alarood, Safa Habibullah, Abdulrahman Alzahrani

{"title":"A secure blockchain framework for healthcare records management systems","authors":"Mahmoud Ahmad Al-Khasawneh, Muhammad Faheem, Ala Abdulsalam Alarood, Safa Habibullah, Abdulrahman Alzahrani","doi":"10.1049/htl2.12092","DOIUrl":"10.1049/htl2.12092","url":null,"abstract":"Electronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance. Several problems in the past few decades regarding medical information management have threatened patient information privacy. In intelligent healthcare applications, the privacy of patients' data is one of the main concerns. As a result, blockchain is a severe necessity as it can enhance transparency and security in medical applications. Accordingly, this paper uses the design science method to propose a secure blockchain framework for healthcare records management systems. The proposed framework comprises five components: a blockchain network, smart contracts, privacy key management, data encryption, and integration with healthcare information technology. In the proposed framework, healthcare organizations can manage healthcare information securely and privately. Additionally, a secure storage system for electronic records is proposed to meet these organizations' needs. It provides security and privacy for healthcare organizations, especially when managing healthcare information, and also proposes a secure storage system for electronic records to meet the needs of the organizations.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"461-470"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification 阿尔茨海默病分类中缺失异构数据的自动编码器输入。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-09-15 DOI: 10.1049/htl2.12091

Namitha Thalekkara Haridas, Jose M. Sanchez-Bornot, Paula L. McClean, KongFatt Wong-Lin, Alzheimer's Disease Neuroimaging Initiative (ADNI)

{"title":"Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification","authors":"Namitha Thalekkara Haridas, Jose M. Sanchez-Bornot, Paula L. McClean, KongFatt Wong-Lin, Alzheimer's Disease Neuroimaging Initiative (ADNI)","doi":"10.1049/htl2.12091","DOIUrl":"10.1049/htl2.12091","url":null,"abstract":"Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history. The authors focused on extreme (≥40%) missing at random of key features which depend on AD progression; identified as the history of a mother having AD, APoE ε4 alleles, and clinical dementia rating. Along with features selected using traditional feature selection methods, latent features extracted from the denoising autoencoder are incorporated for subsequent classification. Using random forest classification with 10-fold cross-validation, robust AD predictive performance of imputed datasets (accuracy: 79%–85%; precision: 71%–85%) across missingness levels, and high recall values with 40% missingness are found. Further, the feature-selected dataset using feature selection methods, including autoencoder, demonstrated higher classification score than that of the original complete dataset. These results highlight the effectiveness and robustness of autoencoder in imputing crucial information for reliable AD prediction in AI-based clinical decision support systems.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"452-460"},"PeriodicalIF":2.8,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpeechMatch—A novel digital approach to supporting communication for neurodiverse groups 一种新颖的数字方法来支持神经多样性群体的交流。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-09-05 DOI: 10.1049/htl2.12090

Sarah Lennard, Samuel J. Tromans, Robert Taub, Sarah Mitchell, Rohit Shankar

{"title":"SpeechMatch—A novel digital approach to supporting communication for neurodiverse groups","authors":"Sarah Lennard, Samuel J. Tromans, Robert Taub, Sarah Mitchell, Rohit Shankar","doi":"10.1049/htl2.12090","DOIUrl":"10.1049/htl2.12090","url":null,"abstract":"Communication can be a challenge for a significant minority of the population. Those with intellectual disability, autism, or Stroke survivors can encounter significant problems and stigma in their communication abilities leading to worse health and social outcomes. SpeechMatch (https://www.speechmatch.com/) is a digital App which is a pragmatic mobile language training platform that teaches individuals to “match” critical components of conversation and looks to provides subjects with immediate visual feedback to shape identification and expression of emotion in speech. While it has been used in autistic people there has been no systematic exploration of its strengths and weaknesses. Further, it's potential to afford improvements in communication to other vulnerable groups such as intellectual disability or Stroke survivors has not been explored. This study looked to understand acceptability from people with intellectual disability and/or autism and those recovering from a stroke on the utility and scope of SpeechMatch using co-production techniques using experts by experience and a mixed methods evaluation. Results across four domains suggest high acceptability levels but highlighting needs for platform capabilities improvement and better user engagement. The study outlines a vital and essential aspect for improving SpeechMatch. It gives a template for evidenced based quality improvement of similar devices.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"447-451"},"PeriodicalIF":2.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT-based remote monitoring system: A new era for patient engagement 基于物联网的远程监控系统：患者参与的新时代。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-08-29 DOI: 10.1049/htl2.12089

Khalad Agali, Maslin Masrom, Fiza Abdul Rahim, Yazriwati Yahya

{"title":"IoT-based remote monitoring system: A new era for patient engagement","authors":"Khalad Agali, Maslin Masrom, Fiza Abdul Rahim, Yazriwati Yahya","doi":"10.1049/htl2.12089","DOIUrl":"10.1049/htl2.12089","url":null,"abstract":"Internet of Things (IoT) is changing patient engagement in healthcare by shifting from traditional care models to a continuous, technology-driven approach using IoT-based Remote Monitoring Systems (IoT-RMS). This research seeks to redefine patient engagement by examining how Internet of Things (IoT) technologies can impact healthcare management and patient–provider interactions at different phases. Additionally, it presents the relationship between patient engagement stages and IoT-RMS, which promotes patients' active participation using technological health management tools. The study emphasizes that IoT-RMS improves patient engagement, organized into three main stages: enabling, engaging, and empowering. This approach shows how technological progress encourages patient involvement and empowerment, leading to improved health results and personalized care. A systematic review and narrative analysis of Web of Science (WOS), Scopus databases, IEEE, and PubMed yielded 1832 studies regarding patient engagement and technology. Despite the optimistic findings, the article highlights the need for more research to evaluate the durability of technology interventions and long-term effectiveness.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"437-446"},"PeriodicalIF":2.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142885984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks 通过线性非混合和深度神经网络集合对组织病理学高光谱图像进行混合脑肿瘤分类。

IF 2.8

Healthcare Technology Letters Pub Date : 2024-06-18 DOI: 10.1049/htl2.12084

Inés A. Cruz-Guerrero, Daniel Ulises Campos-Delgado, Aldo R. Mejía-Rodríguez, Raquel Leon, Samuel Ortega, Himar Fabelo, Rafael Camacho, Maria de la Luz Plaza, Gustavo Callico

{"title":"Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks","authors":"Inés A. Cruz-Guerrero, Daniel Ulises Campos-Delgado, Aldo R. Mejía-Rodríguez, Raquel Leon, Samuel Ortega, Himar Fabelo, Rafael Camacho, Maria de la Luz Plaza, Gustavo Callico","doi":"10.1049/htl2.12084","DOIUrl":"10.1049/htl2.12084","url":null,"abstract":"Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging. The proposed approach is based on the identification of characteristic components in a hyperspectral image by linear unmixing, as a features engineering step, and the subsequent classification by a deep learning approach. For this last step, an ensemble of deep neural networks is evaluated by a cross-validation scheme on an augmented dataset and a transfer learning scheme. The proposed method can classify histological brain samples with an average accuracy of 88%, and reduced variability, computational cost, and inference times, which presents an advantage over methods in the state-of-the-art. Hence, the work demonstrates the potential of hybrid classification methodologies to achieve robust and reliable results by combining linear unmixing for features extraction and deep learning for classification.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 4","pages":"240-251"},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Depression detection with machine learning of structural and non-structural dual languages 通过机器学习检测结构性和非结构性双重语言的抑郁症

IF 2.8

Healthcare Technology Letters Pub Date : 2024-06-10 DOI: 10.1049/htl2.12088

Filza Rehmani, Qaisar Shaheen, Muhammad Anwar, Muhammad Faheem, Shahzad Sarwar Bhatti

{"title":"Depression detection with machine learning of structural and non-structural dual languages","authors":"Filza Rehmani, Qaisar Shaheen, Muhammad Anwar, Muhammad Faheem, Shahzad Sarwar Bhatti","doi":"10.1049/htl2.12088","DOIUrl":"10.1049/htl2.12088","url":null,"abstract":"Depression is a serious mental state that negatively impacts thoughts, feelings, and actions. Social media use is rapidly growing, with people expressing themselves in their regional languages. In Pakistan and India, many people use Roman Urdu on social media. This makes Roman Urdu important for predicting depression in these regions. However, previous studies show no significant contribution in predicting depression through Roman Urdu or in combination with structured languages like English. The study aims to create a Roman Urdu dataset to predict depression risk in dual languages [Roman Urdu (non-structural language) + English (structural language)]. Two datasets were used: Roman Urdu data manually converted from English on Facebook, and English comments from Kaggle. These datasets were merged for the research experiments. Machine learning models, including Support Vector Machine (SVM), Support Vector Machine Radial Basis Function (SVM-RBF), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT), were tested. Depression risk was classified into not depressed, moderate, and severe. Experimental studies show that the SVM achieved the best result with anaccuracy of 0.84% compared to existing models. The presented study refines thearea of depression to predict the depression in Asian countries.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 4","pages":"218-226"},"PeriodicalIF":2.8,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141365366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0