Healthcare Technology Letters最新文献

{"title":"Automated Diagnosis of Hypertensive Retinopathy Using Res-UNet and Graph Convolutional Networks","authors":"Esra'a Mahmoud Jamil AL Sariera","doi":"10.1049/htl2.70054","DOIUrl":"10.1049/htl2.70054","url":null,"abstract":"<p>Hypertensive retinopathy (HR), a progressive retinal condition, is associated with both hypertension and diabetes mellitus. The development of HR is closely correlated with the severity and duration of hypertension. The results of the HR indicate that pathological eye issues include cotton-like spots, macular oedema, constrained arterioles and retinal haemorrhage. An ophthalmologist would still often undertake a manual physical examination using an ophthalmoscope to detect HR in a patient's body. It is time-consuming for a physician to identify HR in a patient based only on retina fundus imaging when done manually. An automated approach for identifying the retinal fundus image is required to solve this issue. One crucial component in the diagnosis of many eye diseases is the condition of the blood vessels in the retina. Researchers have found great interest in the blood vessel segmentation of fundus images for this reason. The knowledge of blood vessel changes associated with various disorders, such as cardiovascular diseases and retinopathy, depends on the accurate segmentation of arteries and veins (A/V) from fundus images. The arteriovenous ratio displays the proportion of vein to artery diameters. The precision with which vessels are divided into veins and arteries determines the significance of this measure. To increase the accuracy of classifying retinal blood vessels and HR phases, a novel technique combining deep residual UNET (Res-UNet) and a graph convolutional network is suggested in this research. Pre-processing (green channel, contrast-restricted adaptive histogram equalisation) was done before identification. Graphs are used to depict the features of the vessel that are extracted from the spatial domain. The DRIVE-AV image dataset is used to execute the proposed approach and the results show that the system achieves a blood vessel segmentation accuracy of 96.45% and an A/V classification of 96.7%.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158647","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148070345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BTdiagAI: A Web-Deployed Hybrid Framework for Brain Tumor Classification Using Optimized MRI Preprocessing and Deep Learning Fusion","authors":"Faysal Ahmmed,&nbsp;Asef Rahman Antik,&nbsp;Ajmy Alaly,&nbsp;Samanta Mehnaj,&nbsp;Md Sadi Al Huda,&nbsp;Md. Asraf Ali","doi":"10.1049/htl2.70053","DOIUrl":"10.1049/htl2.70053","url":null,"abstract":"<p>Brain tumor diagnosis via MRI remains challenging due to imaging artifacts, tumor heterogeneity, and time-intensive manual evaluations that introduce variability. While preprocessing is critical for accuracy, comparative analyses of techniques are limited, as research often prioritizes algorithmic advancements. This study systematically evaluates five MRI preprocessing methods (CLAHE, Nyul normalization, N4 bias correction, template registration, White Stripe normalization) and proposes a deep learning framework integrating fine-tuned InceptionV3, DenseNet121, and Xception networks. Features from these models were concatenated, refined via ANOVA and L1max selection, and classified using machine learning. To address class imbalance, data augmentation techniques were employed, ensuring a well-distributed dataset for robust model training. All preprocessing methods achieved greater than 98% accuracy, with CLAHE outperforming others (99.8% on Dataset 1; 99.61% on Dataset 2) while requiring minimal computational resources. The framework's efficacy is demonstrated through a publicly accessible web platform BTdiagAI, that enables users to upload brain MRI scans for automated classification into four tumor categories: benign, pituitary, glioma, and meningioma. This deployment underscores the clinical applicability of the solution, offering rapid, scalable diagnosis with state-of-the-art accuracy. The study highlights preprocessing as pivotal for MRI-based tumor diagnosis, advocating CLAHE for its balance of efficiency and performance.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12876050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143962","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Respiratory Rate Measurement Using Mobile Applications in Healthcare Settings: A Scoping Review","authors":"Lachlan Sallabank,&nbsp;James Oswald,&nbsp;Sian Willett,&nbsp;James Kelleher,&nbsp;Brian Haskins","doi":"10.1049/htl2.70035","DOIUrl":"10.1049/htl2.70035","url":null,"abstract":"<p>Respiratory rate (RR) is a strong indicator of clinical trajectory and forms the basis of patient care and assessment. However, clinicians often face barriers to easily obtaining a RR without inefficient methods or costly technology. To remedy this, several phone applications have emerged where clinicians can tap out each breath to calculate a RR. We aimed to map the available evidence for tap-per-breath applications used in healthcare settings. We searched for articles using multiple databases, including primary research articles that evaluated tap-per-breath apps in healthcare settings. 14 articles were selected for this review, mostly cross-sectional and hospital based. Most applications reported high usability and efficiency, although results of accuracy were mixed across the included literature. Median-based apps were more often an accurate measure of RR, however more research is required. Articles were commonly limited in generalisability due to poorly defined reference standards, small sample sizes, or using retrospective video recordings for patient assessment. Studies showed favourable usability and efficiency across the literature, with median-based apps demonstrating greater consistency and accuracy of RR measurements. Though the scope of this review and limited evidence restrict any far-reaching clinical implications until further evidence emerges.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12850432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146087387","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148090953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wavelet-Based Denoising Optimization for Endoscopic Gastric Slow-Wave Recordings","authors":"Peter Tremain,&nbsp;Jarrah M. Dowrick,&nbsp;Leo K. Cheng,&nbsp;James W. McKeage,&nbsp;Carolina Saavedra,&nbsp;Julio Sotelo,&nbsp;Timothy R. Angeli-Gordon","doi":"10.1049/htl2.70052","DOIUrl":"10.1049/htl2.70052","url":null,"abstract":"<p>New, minimally invasive, endoscopic methods for recording gastric bioelectrical slow waves from the mucosal surface are emerging to address the current limitations of invasive recordings. Filtering techniques for these new methods have relied on protocols developed for invasive recordings. Updated signal processing techniques, such as discrete wavelet transformation (DWT), optimised for endoscopic recording conditions, promise more effective noise removal for these signals. Synthetic signals were constructed using averaged slow-wave data and noise segmented from existing endoscopic gastric bioelectrical recordings from 12 patients. DWT was performed on the synthetic signals using 989 different parameter combinations to remove noise. Savitzky-Golay (SG) filtering was also performed on the synthetic signals to provide a comparative baseline for classical filter performance. Combined SG filtering and DWT was then investigated using the top-performing DWT parameters. Filter performance was evaluated using six established metrics, along with the inspection of the power spectral density (PSD) calculated on sample signals. Statistical significance was analysed using a paired two-tailed Student's <i>t</i>-test or Wilcoxon signed-rank test. For signals with moderate signal-to-noise ratio (SNR), DWT-based methods outperformed traditional SG filtering in all metrics considered: signal-distortion ratio (0.84 ± 0.45 vs. 1.34 ± 0.99), root-mean-square error (280 ± 150 µV vs. 450 ± 330 µV), percentage root-mean-square difference (78 ± 42% vs. 113 ± 83%), noise-correction ratio (0.94±0.17 vs. 0.50±0.26), SNR improvement (5.9±3.0 dB vs. 2.1±2.7 dB) and filter performance metric (0.96 ± 0.42 vs. 1.8 ± 1.2). All <i>p</i>-values were &lt;0.05. The combination of SG filtering with DWT provided improved signal denoising when compared to SG filtering alone, whilst offering reduced aggressiveness when compared to DWT alone. Inspection of the calculated PSDs for sample signals reaffirmed these results. The results presented in this study indicate that for endoscopic gastric bioelectrical recordings, with moderate SNR, modern denoising techniques based on DWT can outperform traditional SG filtering. More efficient noise removal using DWT can allow for better automated detection of slow-wave activations and more reliable, efficient data processing.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12831173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054269","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":"Comparative Evaluation of Ultrasound-Guided Peripheral Intravenous Catheter Insertion Techniques in a Virtual Reality Simulator","authors":"Alejandro Olivares,&nbsp;Canelle Schuhler-Husson,&nbsp;Yahia Zine,&nbsp;Simon Drouin","doi":"10.1049/htl2.70040","DOIUrl":"10.1049/htl2.70040","url":null,"abstract":"<p>Peripheral intravenous catheter (PIVC) insertion is a common yet challenging procedure. Although ultrasound guidance improves procedural accuracy and patient outcome, its complexity limits its routine adoption to highly experienced clinicians. This paper introduces a virtual reality (VR) simulator developed specifically for training in ultrasound-guided PIVC insertions. This study aims to validate the simulator's realism and relevance through face, content, and construct assessments, and to demonstrate its utility as a platform for comparing various approaches to PIVC insertion. Thirty participants from diverse medical backgrounds and levels of expertise completed three scenarios, each featuring a different procedural technique, within the simulator's controlled virtual environment. The simulator demonstrated strong face and content validity, with participants rating its realism at 7.1/10 and enjoyment at 8.2/10. Performance data showed that expert participants maintained higher success rates and performance across all procedural scenarios, supporting the simulator's construct validity. In the standard approach scenario, novices required 230.91 <span></span><math>\u0000 <semantics>\u0000 <mo>±</mo>\u0000 <annotation>$pm$</annotation>\u0000 </semantics></math> 158.77 s to complete the task and achieved only a 45% success rate compared to experts' 95.48 <span></span><math>\u0000 <semantics>\u0000 <mo>±</mo>\u0000 <annotation>$pm$</annotation>\u0000 </semantics></math> 65.74 s and 80% success rate. In the procedural scenario involving an alignment assistance device, where needle insertion was aligned with the ultrasound image plane, novice success rates increased to 75% and the number of attempts decreased from 8.95 <span></span><math>\u0000 <semantics>\u0000 <mo>±</mo>\u0000 <annotation>$pm$</annotation>\u0000 </semantics></math> 6.69 to 2.75 <span></span><math>\u0000 <semantics>\u0000 <mo>±</mo>\u0000 <annotation>$pm$</annotation>\u0000 </semantics></math> 2.67, narrowing the performance gap with experts. These findings highlight the simulator's potential not only as an effective training tool but also as a platform for the objective evaluation of different procedural techniques.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904901","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148067240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}