Healthcare Technology Letters最新文献

{"title":"A Secure and Efficient Framework for ECG Signal Digitization and Encryption for Internet of Medical Things Applications.","authors":"Nader Mahmoud, Shymaa S Shaban","doi":"10.1049/htl2.70082","DOIUrl":"https://doi.org/10.1049/htl2.70082","url":null,"abstract":"<p><p>The Internet of Medical Things (IoMT) is transforming healthcare through smart devices, real-time monitoring, and cloud-based data exchange. Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular conditions; however, their sensitivity is still a major challenge for secure transmission and storage. This paper presents a novel content-based encryption framework tailored for ECG data. Unlike conventional approaches that process either raw signals or images separately, the proposed method first extracts accurately ECG waveforms from 2D images while handling noisy or overlapping traces through a dedicated preprocessing and segmentation pipeline. A two-layer chaotic scheme is then employed to encrypt the extracted 1D signals: the Arnold Cat map (ACM) for spatial scrambling and Double Random Phase Encoding for frequency-domain transformation. These cryptographic modules can also operate directly on native digital ECG signals and integrate seamlessly with paper-based ECG datasets, ensuring broad applicability across acquisition formats. Experimental results on a 12-lead ECG dataset demonstrate high digitization accuracy (Structural Similarity Index = 0.94), fast execution (0.0036 s), strong encryption (entropy = 7.3, correlation = 0.0006), and perfect signal recovery (PSNR = ∞). Overall, the framework provides a secure and efficient solution for IoMT-driven healthcare systems, outperforming existing ECG encryption methods across standard evaluation metrics.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70082"},"PeriodicalIF":3.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13135227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821740","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}

{"title":"Systematic Review of Explainable Artificial Intelligence for Epileptic Seizure Onset Early Warning: Towards Responsible Artificial Intelligence.","authors":"Daraje Kaba Gurmessa, Kula Kekeba Tune, Worku Jimma","doi":"10.1049/htl2.70081","DOIUrl":"https://doi.org/10.1049/htl2.70081","url":null,"abstract":"<p><p>A substantial amount of literature has been published on epileptic seizures. However, adequate evidence is still lacking to demonstrate that utilising explainable artificial intelligence for epileptic seizures can ensure an individual's safety. Furthermore, there is a need to define the fundamental challenges and opportunities present in the current state-of-the-art solutions and guide efforts towards responsible artificial intelligence. To identify fundamental challenges and opportunities in the existing state-of-the-art solutions available for explainable artificial intelligence-based epileptic seizure onset early warning: towards responsible artificial intelligence. The PRISMA checklist was utilised to develop this report. Papers were extracted from original articles and prior conference studies published in reputable databases such as PubMed, IEEE Xplore, ScienceDirect, Scopus and Google Scholar from January 2019 to 17 November 2024. Rayyan's online platform was used to identify duplicates, inclusions and exclusions of papers. This systematic review protocol was registered with the PROSPERO database. The included papers were assessed based on Microsoft's Responsible Artificial Intelligence template. The Responsible AI Impact Assessment Template, Principle 3 (transparency and explainability), determined a high-risk rating. A total of 26 studies are included based on the established inclusion and exclusion criteria. This study investigated 14.29% of responsible artificial intelligence principles applied in at least one paper with a high-risk rate. The results indicate that to transform researched solutions into practical applications, epileptic monitoring applications should be tested within the eight principles set by Microsoft. The black box explanation lacks insight into the deep internal features and operational methods, suggesting that further investigation is necessary. Systematic Review Registration ID: CRD42024544.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70081"},"PeriodicalIF":3.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13135226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821783","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}

{"title":"Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy.","authors":"Muhammad Zulqarnain, Syed Jawad Hussain, Muhammad Zeeshan Aslam, Ahsan Fiaz, Muhammad Islam","doi":"10.1049/htl2.70080","DOIUrl":"https://doi.org/10.1049/htl2.70080","url":null,"abstract":"<p><p>Early diagnosis of thoracic diseases using chest x-ray imaging remains a critical challenge, particularly in resource-constrained healthcare environments where data sharing is restricted due to privacy concerns. Federated learning (FL) offers a decentralized solution by enabling collaborative model training without sharing sensitive patient data. However, integrating privacy-preserving mechanisms such as differential privacy (DP) introduces additional challenges related to performance degradation and computational overhead. In this study, we present a unified FL framework for multi-label thoracic disease classification using multiple convolutional neural network (CNN) architectures, including ResNet50, DenseNet169, EfficientNet variants and MobileNetV3. Unlike prior studies focusing on single-model evaluation, this work provides a controlled comparative analysis under identical FL settings and investigates the impact of client scalability (5-10 clients) on model performance. Furthermore, we conduct a comprehensive empirical analysis of the privacy utility trade-off by integrating DP with varying privacy budgets (<i>ε</i> = 1, 15 and 30). Experimental results on the CheXpert and NIH Chest x-ray14 datasets demonstrate that the proposed EfficientNet-B3-based federated model achieves a mean AUC of 0.8027, while maintaining robustness across decentralized settings. The integration of DP leads to a predictable reduction in performance, with mean AUC ranging from 0.60 to 0.64, highlighting the inherent trade-off between privacy and diagnostic accuracy. The findings emphasize the practical viability of FL for privacy-sensitive medical imaging applications and provide insights into model selection, scalability and privacy configuration for real-world deployment. The source code for this study is publicly accessible at https://github.com/Zulqarnain8-8/FEDERATED_LEARNING_FOR_THORACIC_DISEASE_CLASSIFICATION.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70080"},"PeriodicalIF":3.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13135224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821806","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}

{"title":"Trustworthy Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detections.","authors":"Md Rashed, Mohammad Kamrul Hasan, Md Imran Hossain, Md Sarwar Hosain, Abdul Hadi Abd Rahman, Shayla Islam","doi":"10.1049/htl2.70062","DOIUrl":"https://doi.org/10.1049/htl2.70062","url":null,"abstract":"<p><p>Breast cancer (BC) has become a major public health concern and is critically associated with the highest global death rate for cancer detection. The diagnosis process and the techniques remain complex and often influenced by the diagnostician's background, which makes it challenging. Despite advancements in BC detection, existing methods cannot often effectively combine interpretability and high accuracy in complex imaging data, limiting their clinical applicability. This study proposes a reliable strategy for detecting BC by combining deep learning (DL) strengths with ensemble-based machine learning (ML) techniques. ML models offer interpretability and generalisation, while DL enhances the ability to learn and uncover hidden patterns in complicated BC images. The pre-trained model is used in the proposed technique for effective feature extraction, followed by applying eight different ML models to identify BC. The performance of the study is evaluated in terms of precision, recall, F1-score, and confusion matrices for all classifiers. In addition, ROC curves are drawn for each classifier. Our rigorous experimentation yields compelling results that demonstrate exceptional performance compared with those of existing state-of-the-art models. We achieve a higher accuracy rate of 97.50%, a precision of 97.15%, a recall of 97.00%, and an F1-score of 96.98%. Furthermore, we determine that the support vector classifier is the most effective ML model when integrated with the pre-trained VGG-16 architecture. The strategies, exhaustive performance analysis, and reliable assessment presented in this research provide valuable advances in BC detection, helping doctors make better decisions, offering better patient care, and improving BC outcomes.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70062"},"PeriodicalIF":3.3,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13123192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784047","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}

{"title":"APB-FLDPA: Adaptive Personalized Blockchain-Federated Learning With Differential Privacy and Attention for Privacy-Preserving Healthcare Analytics.","authors":"Md Kamran Hussin Chowdhury, Proloy Kumar Mondal, Md Ariful Islam Mozumder, Hee Cheol Kim, Haewon Byeon","doi":"10.1049/htl2.70079","DOIUrl":"https://doi.org/10.1049/htl2.70079","url":null,"abstract":"<p><p>Developing robust medical artificial intelligence (AI) requires collaboration across multiple institutions, but strict data protection regulations such as HIPAA and GDPR prevent centralized patient data sharing. Existing federated learning (FL) methods often exhibit 15%-30% performance degradation in real-world clinical settings due to data heterogeneity, security threats, and privacy constraints. We present APB-FLDPA, a privacy-preserving federated learning framework for secure multi-hospital disease prediction. APB-FLDPA integrates five key innovations: (i) adaptive Byzantine-resilient aggregation using dynamic client trust scoring, (ii) self-attention for automated clinical feature importance, (iii) selective differential privacy applied at the final aggregation stage, (iv) cluster-aware personalization to handle cross-institutional heterogeneity, and (v) a lightweight blockchain module to ensure model integrity. Evaluated across five institutions using large-scale Diabetes (183,000 patients) and Thyroid (6840 patients) datasets, APB-FLDPA achieved 90.8% accuracy for diabetes and 83.8% accuracy for thyroid disease, with minimal performance loss (<0.2%) compared to centralized learning. Statistical tests confirmed significant improvements, and selective differential privacy outperformed conventional methods by 5.6% in accuracy. These results show that APB-FLDPA provides a scalable, high-performance and privacy-compliant solution for real-world federated medical AI.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70079"},"PeriodicalIF":3.3,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783711","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}

{"title":"Early Intensive Care Unit Length of Stay Prediction on MIMIC-IV: A Dual Approach With Clinical Features and Textual Notes.","authors":"Yunyi She, Zach Wood-Doughty","doi":"10.1049/htl2.70077","DOIUrl":"https://doi.org/10.1049/htl2.70077","url":null,"abstract":"<p><p>Predicting intensive care unit (ICU) length of stay (LOS) within the first 24 h of admission can improve bed management, staffing and care planning. While prior studies have used structured electronic health record (EHR) data, including demographics, vitals, labs and ICD codes, recent advances in transformer-based language models enable the use of unstructured clinical notes. In this study, we compare structured and unstructured modelling approaches for early ICU LOS prediction within a unified experimental framework. We developed two parallel pipelines using MIMIC-IV data: (1) a structured pipeline using conventional machine learning models (logistic regression, random forest, XGBoost and SVM) trained on day-one EHR features with ICD-derived embeddings and (2) an unstructured pipeline fine-tuning transformers (ClinicalBERT, Bio+ClinicalBERT and BlueBERT) on discharge notes. We evaluated binary classification (short [ <math><mrow><mo>≤</mo></mrow> </math> 4 days] vs. long [ <math><mrow><mo>></mo></mrow> </math> 4 days] stay) and regression of exact LOS in days. Our results show XGBoost with ICD embeddings achieved the best results (AUROC = 0.805) with minimal training time; XGBoost without ICD embeddings still achieved strong performance (AUROC = 0.732), providing a baseline without delayed codes. Transformer models performed comparably (AUROC = 0.766) but required more computation. Overall, both pipelines offer valuable early signals, but differ in efficiency and integration, highlighting trade-offs.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70077"},"PeriodicalIF":3.3,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783748","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}

{"title":"Epidemic Forecasting via Hybrid Deep Learning With Unified Visibility and Temporal Graphs Under Stochastic Noise Modelling.","authors":"Arman Kavoosi Ghafi, Ali Pirkhedri, Samira Akhbarifar, Mohammad Hossein Shafiabadi","doi":"10.1049/htl2.70073","DOIUrl":"https://doi.org/10.1049/htl2.70073","url":null,"abstract":"<p><p>Epidemic forecasts are unreliable when surveillance data are noisy or incomplete and when underreporting and rapidly changing population behaviour distort observed incidence, degrading the stability of conventional statistical and deep-learning models. We aim to develop an interpretable, uncertainty-aware forecasting pipeline that remains robust under data corruption and is practical for real-time use. We convert COVID-19 incidence into multilayer temporal graphs: global cumulative counts are differenced to daily incidence, normalised, and segmented into overlapping 30-day windows; for each window, we build a visibility graph from the empirical series and a matched-length visibility graph from stochastic simulations (fractional Brownian motion and Lévy-type dynamics) to represent reporting and behavioural randomness. We fuse the graphs (weighted edge averaging), extract compact descriptors (mean degree, clustering coefficient, entropy) and train a lightweight regressor to predict the 7-day-ahead average incidence. On the Johns Hopkins COVID-19 dataset, the method outperforms ARIMA, LSTM and standard GCN baselines (MAE = 0.0558; RMSE = 0.0709). Stress tests with noise and missingness and ablations show that stochastic augmentation and graph fusion materially improve robustness, while a cloud-oriented deployment reduces inference time by >60% and memory usage by 35%, enabling low-latency monitoring for timely public-health decision-making.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70073"},"PeriodicalIF":3.3,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783724","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}

{"title":"Joint Sensor Clustering and Power Allocation in Wireless Body Area Networks Based on NOMA.","authors":"Danhao Deng, Dawei Liu, Jiayue Li","doi":"10.1049/htl2.70075","DOIUrl":"10.1049/htl2.70075","url":null,"abstract":"<p><p>With the transformation of modern medical care towards a prevention-centred model and the growing demand for high-density sensor deployments in wireless body area networks (WBANs), traditional orthogonal multiple access (OMA) schemes face critical limitations in spectral efficiency and energy sustainability. Non-orthogonal multiple access (NOMA) combined with radio frequency energy harvesting (RFEH) emerges as a promising solution, yet existing research often overlooks the strict energy causality constraints of sensors. To address this issue, this paper proposes a novel joint optimization framework for NOMA-assisted WBANs, which strategically pairs predefined sensor groups (classified by body positions) into NOMA clusters and optimizes power allocation while accounting for harvested RF energy. The proposed sorted-greedy joint algorithm enhances successive interference cancellation (SIC) efficiency by pairing sensor groups with the largest channel gain differences, followed by a greedy power allocation strategy that maximizes throughput under energy and power constraints. Simulation results demonstrate that the proposed algorithm outperforms conventional greedy and random clustering approaches in total system throughput across varying numbers of sensor groups, sensors per group, and transmission power levels.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70075"},"PeriodicalIF":3.3,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13091016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724130","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}

{"title":"Mobile-Based Medication Management Applications: A Scoping Review of Functional and Non-Functional Requirements.","authors":"Sanaz Malekzadeh, Haleh Ayatollahi, Esmaeel Toni","doi":"10.1049/htl2.70074","DOIUrl":"https://doi.org/10.1049/htl2.70074","url":null,"abstract":"<p><p>Mobile-based applications are increasingly used to support patient self-management and medication adherence. Despite advantages, there is limited research on their essential functional and non-functional requirements. This study aimed to identify functional and non-functional requirements of mobile-based medication management applications. This scoping review was conducted in 2025 by searching Scopus, PubMed, ProQuest, Web of Science databases, Google Scholar, and Persian-language databases, including Scientific Information Database (SID) and Magiran. Studies published in English and Persian between 2010 and 2024 were screened for eligibility. Relevant studies which met the inclusion criteria were selected, reviewed, and analysed descriptively. A total of 38 articles were included. Functional requirements included medication information, recommendations, alerts, reminders, medication search, data sharing with healthcare providers, drug interaction information, and side effect reporting. Non-functional requirements consisted of ease of use, scalability, synchronisation, accessibility, compatibility, security, and reliability. Defining functional and non-functional requirements is essential for designing effective medication management applications. The findings provided actionable insights for developers and healthcare stakeholders to build user-centred, secure, and reliable mobile-based solutions, ultimately improving patient outcomes in medication adherence and self-management.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 ","pages":"e70074"},"PeriodicalIF":3.3,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13074359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692831","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}

{"title":"In-Vivo Hand Mass Determination and an Anthropometric Investigation on Segment Length and Radius for Prosthetic Segment Design.","authors":"Panagiotis Tsakonas, Neil D Evans, Joseph Hardwicke, Shervanthi Homer-Vanniasinkam, Michael J Chappell","doi":"10.1049/htl2.70078","DOIUrl":"https://doi.org/10.1049/htl2.70078","url":null,"abstract":"<p><p>This paper aims to demonstrate the validity of the use of a cylindrical approximation for human fingers and a parallelepiped approximation of the palm, from a modelling perspective, for the determination of the mass of the hand. The goal is to provide an intuitive way of determining the length and radius of missing segments from partial hand amputees based on palm dimensions and determine the corresponding mass based on the previous two approximations. In-vivo hand mass measurements were taken from 23 able-bodied participants using Archimedes' water displacement method to verify the geometric approximations used. Furthermore, an anthropometric investigation on how segment length and radius change with respect to palm dimensions was undertaken and the estimates found, which can then be used in supporting the design of prosthetic segments in a personalised context.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":"e70078"},"PeriodicalIF":3.3,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13058727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147646909","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}