Healthcare Technology Letters最新文献

{"title":"A Comprehensive Review of Artificial Intelligence Methods in Bone Age Assessment","authors":"Mohsen Borjalizadeh,&nbsp;Farshid Babapour Mofrad,&nbsp;Midya Yousefzamani","doi":"10.1049/htl2.70056","DOIUrl":"10.1049/htl2.70056","url":null,"abstract":"<p>Bone age reflects individual skeletal maturity and is an important factor in the follow-up and monitoring of growth and development in children. Determination of bone age by paediatricians has remained one of the most typical indications that require the use of radiology, and the historical method by which radiologists determine bone age is from a bone age atlas. However, there are still some challenges. The limited case diversity related to race, geography, and age distribution; small sample sizes; and the lack of expert validation by multiple radiologists limit the generalisability of current models. Many underlying comorbidities or health histories are often overlooked in developed models. The models of the future, which provide greater accuracy and clinical usefulness, must encompass more diverse datasets, more thorough health histories, expert validation, and fast but reliable artificial intelligence (AI) models. As an educational review, this study analysed a variety of AI-based approaches that have emerged in the past several years for paediatric bone age assessment (most using hand and wrist radiographs and often coupled with radiology reports). Of these, models such as RCNN, which we evaluated with mean absolute error, showed great potential. There are great future clinical applications and advancements that can progressively transform bone age assessment and evaluation from AI. Notably, we did identify the gaps and opportunities for potentially improving the future clinical approach of bone age assessment and evaluation.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12892095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182874","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":"Generating Job Recommendations for People With Schizophrenia Spectrum Disorder Using Gemini 2.0 Flash and Claude Sonnet 4: An Exploratory Analysis","authors":"Maximin Lange,&nbsp;Nikolaos Koutsouleris,&nbsp;Ben Carter,&nbsp;Ricardo Twumasi","doi":"10.1049/htl2.70058","DOIUrl":"10.1049/htl2.70058","url":null,"abstract":"<p>Employment is a crucial part of recovery for individuals with severe mental illness. Individual placement and support (IPS) is the gold standard for vocational rehabilitation, yet IPS reaches only a fraction of who could benefit. Large language models (LLMs) have been proposed as potential tools for vocational guidance, but their utility for vulnerable populations is unknown. We conducted an analysis of LLM-generated job recommendations for individuals with schizophrenia spectrum disorders, and for a matched control cohort without psychiatric diagnoses. We used discharge summaries from 450 patients with a primary diagnosis of schizophrenia spectrum disorder and 50 control cases in the MIMIC-IV database, fitting three independent job recommendations per case with Gemini 2.0 Flash and Claude Sonnet 4. Recommendations were summarised as a frequency and LLM-automated content analysis was used to analyse reasoning patterns, workplace accommodations, and alignment with supported employment principles. Both, Gemini and Claude, showed little diversity and strong bias toward entry-level roles. In the schizophrenia cohort, Gemini mostly recommended data entry and other clerical jobs while Claude produced a similarly narrow pattern with the majority suggesting library-related. The controls revealed comparable clustering, with Gemini defaulting to clerical work and medical secretary roles, and Claude to customer service. There was limited diversity in the role settings, which almost uniformly suggested flexible schedules and minimal social interaction. Nor was there diversity in how roles were tailored to patient strengths, qualifications, or prior experience; instead, demographic stereotypes such as age-based framing, gendered role allocation, and assumptions about language skills often shaped the recommendations. Based on our data and procedures, preliminary evidence does not support immediate deployment of LLMs for job recommendations for the tested population; further evaluation is needed after integrating human oversight and bias-mitigation steps.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12894053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203180","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":"Finite Element Analysis of Von Mises Stress in External Fixators for Open Tibial Fractures: A Comparative Study of ASTM F1541-02 and Tibia-Based Models in Indonesian Patients","authors":"Muhammad Kozin,&nbsp;Muhammad Imam Ammarullah,&nbsp;Abdulfatah Abdu Yusuf,&nbsp;Aghni Ulma Saudi,&nbsp;Siti Amalina Azahra,&nbsp;I. Nyoman Jujur,&nbsp;Muhammad Hirzan Arrifqi,&nbsp;Moch. Agus Choiron","doi":"10.1049/htl2.70068","DOIUrl":"10.1049/htl2.70068","url":null,"abstract":"<p>Traffic accidents are the leading cause of traumatic fractures worldwide, with tibial fractures being the most common lower-extremity injury. Open tibial fractures pose significant clinical challenges due to their high risk of infection and non-union, requiring effective stabilisation. External fixators are widely used for this purpose, but their biomechanical performance must be evaluated under both standardised and patient-specific conditions. This study presents a finite element analysis of an external fixator tailored to Indonesian patients, using two frameworks: the ASTM F1541-02 standard protocol and a tibia-based model derived from patient geometry. Von Mises stress distributions were assessed under axial compression, torsional loading, and four-point bending. Results showed that stresses predicted by ASTM F1541-02 were consistently higher than those from the tibia-based model, particularly under torsion and bending, though all values remained below material yield strength. These findings indicate that the fixator design is safe, while emphasising that tibia-based modelling provides more physiologically realistic predictions. The study shows the importance of patient-specific anatomy in computational biomechanics and points to future directions in design optimisation and localized manufacturing.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12892096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182905","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":"An Add-On Contactless Measurement System for Monitoring Driving Behaviours in Motorised Mobility Scooters","authors":"Yi Liu,&nbsp;Takenobu Inoue,&nbsp;Jun Suzurikawa","doi":"10.1049/htl2.70064","DOIUrl":"10.1049/htl2.70064","url":null,"abstract":"<p>The increasing use of motorised mobility scooters (MMSs) has raised significant safety concerns, particularly related to user behaviour during operation. Although various advanced driving assistance systems have been incorporated into MMSs to identify potential environmental hazards, few studies have investigated the impact of user behaviour in MMS driving. This study was the first to incorporate automated behaviour monitoring into the evaluation of MMS driving using an add-on driving behaviour monitoring system (ADBMS). The ADBMS was a platform for contactless measurement that used a pre-trained convolutional neural network to estimate posture and two inertial measurement units to record steering and throttle operations. Experiments were conducted to demonstrate the usability of the ADBMS and evaluate the coordination of head movements and steering manoeuvres when driving an MMS in the outdoor environment. Cross-correlation analysis revealed that head movement consistently preceded steering operation during the driving tasks, indicating the potential of the proposed system to quantify user behaviour related to attention toward the surrounding environment. The lag time between these two parameters may serve as a novel index of driving safety. These findings could support a comprehensive understanding of users’ driving behaviours and provide valuable insights into developing behavioural interventions to promote safer MMS use.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12894081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203173","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":"Hidden Tumour Visualization in Augmented Monocular Liver Laparoscopy","authors":"Kirana Hanifati,&nbsp;Mohammad Alkhatib,&nbsp;Erol Ozgur,&nbsp;Emmanuel Buc,&nbsp;Bertrand Le Roy,&nbsp;Hestiasari Rante,&nbsp;Youcef Mezouar,&nbsp;Adrien Bartoli","doi":"10.1049/htl2.70043","DOIUrl":"10.1049/htl2.70043","url":null,"abstract":"<p>We address the hidden tumour visualization problem in augmented monocular liver laparoscopy. Conveying a hidden tumour's depth correctly to the surgeon in augmented monocular laparoscopy is extremely difficult and still forms an unsolved problem. The depth conveyance can be split into two subsequent problems. First, designing a visualization that convinces the user to see the tumour inside the organ. Second, enhancing this visualization so that it also provides metric depth perception. The most promising visualization methods rely on a preoperative CT organ model with the tumour to be registered to an intraoperative laparoscopic image. Such a registration allows the organ's intraoperative shape mesh to be overlaid on top of the augmented tumour. The overlaid organ mesh guarantees a partial occlusion on the augmented tumour. This provides a powerful depth cue for the surgeon's perception. However, this type of registration, especially in liver laparoscopy, is usually not real-time and sometimes not possible. This is because of the liver deformation and lack of matchable features between the multimodal images. Subsequently, the tumour augmentation cannot be carried out continuously to guide the surgeon. We propose a novel visualization method to address these limitations. The proposed method replaces the deformable preoperative to intraoperative liver registration with a rigid tumour registration via laparoscopic ultrasound imaging. The proposed method handles surgical tool occlusions, runs faster, and outperforms the state of the art in terms of depth perception, as shown in the user study.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12889570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167035","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":"The Effect of Telehealth on Alzheimer's Disease, Dementia and Mild Cognitive Impairment: A Systematic Review of Clinical Trials","authors":"Kosar Ghaddaripouri,&nbsp;Reza Molavi,&nbsp;Sarah Montazeryan,&nbsp;Mohammadreza Sadegh Kharaghani,&nbsp;Fatemeh Taheri Soudejani,&nbsp;Melika Vafamand,&nbsp;Leila Erfannia","doi":"10.1049/htl2.70065","DOIUrl":"10.1049/htl2.70065","url":null,"abstract":"<p>Alzheimer's disease is a prevalent chronic condition characterised by the gradual deterioration of memory and personal abilities due to nervous system damage, requiring prolonged care and management. In contemporary healthcare, telehealth has gained recognition as an effective approach for managing chronic illnesses by improving equitable access to quality medical services and minimising expenses. The purpose of this systematic review is to evaluate the role of telehealth in enhancing the well-being of patients with Alzheimer's disease and supporting their caregivers, as evidenced by findings from randomised controlled trials (RCTs). This systematic review concentrated on RCTs published in English, with no constraints on publication date. The search process was accomplished on 11 August, 2025, using appropriate keywords across well-established scientific databases, including PubMed, Embase, Scopus, ScienceDirect, Web of Science and ProQuest. The quality of the studies was assessed using the Joanna Briggs Institute checklist and only those scoring above seven were included in the analysis. From an initial collection of 1242 articles, 14 trials were ultimately included in this review. Telehealth interventions demonstrated significant improvements in cognitive function, mobility and quality of life among individuals with mild cognitive impairment and Alzheimer's Disease, while also reducing caregiver burden and psychological distress. These interventions, implemented through synchronous and asynchronous delivery methods, were deemed feasible, well-received and associated with strong adherence rates. Nonetheless, limitations such as small sample sizes and restricted access to technological resources emphasise the need for additional research to address these gaps. The findings from 13 out of 14 articles in this systematic review indicate that telehealth interventions, including virtual reality, video conferencing, computerised cognitive training and group movement programs, have the potential to significantly enhance health outcomes and quality of life for individuals with Alzheimer's disease and their caregivers compared to traditional in-person treatments. These interventions, delivered through diverse and flexible modalities, also demonstrate cost-effectiveness and improved caregiver well-being, reinforcing telehealth as a scalable and effective approach for comprehensive Alzheimer's care.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12890872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182912","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":"Internet of Medical Things Enabled Multimodal Framework: Deep Machine Learning for Chronic Cardiac Disease Prediction in Healthcare 5.0","authors":"Rabia Javed,&nbsp;Tahir Abbas,&nbsp;Ali Sayyed,&nbsp;Sagheer Abbas,&nbsp;Asghar Ali Shah,&nbsp;Khan Muhammad Adnan","doi":"10.1049/htl2.70063","DOIUrl":"10.1049/htl2.70063","url":null,"abstract":"<p>Accurate and early detection of chronic heart disease is vital, as it remains one of the leading global causes of mortality. Despite advancements in Smart Healthcare 5.0 and modern information technologies, reliable diagnosis of cardiovascular conditions remains a significant challenge. The Internet of Medical Things (IoMT) enables seamless data exchange between medical devices, supporting more precise and timely management of cardiac diseases. This study employs convolutional neural networks (CNNs) on electrocardiogram (ECG) image datasets to classify multiple heart conditions. The datasets include ECG scans labelled as Abnormal Heartbeat (ANHB), Myocardial Infarction (MI), History of Myocardial Infarction (HOMI), Atrioventricular Heart Block (AHB), COVID-19, Hypertrophic Cardiomyopathy (HMI), and Normal. A multimodal model integrating images of varying resolutions from two independent datasets was developed to improve classification performance. The proposed CNN model, trained and validated on preprocessed ECG images, achieved 97.18% training accuracy and 94.34% validation accuracy. By combining ECG data from diverse sources, the model enhances the identification of cardiac irregularities and provides a comprehensive diagnostic approach. This method demonstrates potential to support early detection, improve individualised treatment planning, and ultimately strengthen patient outcomes in managing chronic heart disease.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12889572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146166993","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":"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":"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":"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}