European heart journal. Digital health最新文献

{"title":"Explainable electrocardiogram interpretation using deep learning-based semantic segmentation.","authors":"Bauke K O Arends, Bas B S Schots, Parmenion Koutsogeorgos, Timo Nijkamp, Tim M Paquaij, Diantha J M Schipaanboord, Rutger J Hassink, Pim van der Harst, René van Es, Rutger R van de Leur","doi":"10.1093/ehjdh/ztag122","DOIUrl":"10.1093/ehjdh/ztag122","url":null,"abstract":"<p><strong>Aims: </strong>Accurate electrocardiogram (ECG) waveform delineation, rhythm classification, and median beat generation are interdependent steps whose joint modelling improves consistency for downstream computerized diagnostic tasks. This study aimed to develop a lead-agnostic segmentation model that performs these tasks by segmenting individual leads and aggregating predictions in post-processing.</p><p><strong>Methods and results: </strong>A DeepLabV3-based neural network was trained to segment ECG leads into 20 waveform and rhythm classes using 1931 annotated ECGs and 33 093 ECGs with physician-verified diagnostic statements. Post-processing combined lead-wise predictions to delineate intervals, classify rhythm, and construct median beats. Performance was evaluated on internal (<i>n</i> = 988) and external (<i>n</i> = 1303) test sets. Median beats were compared with PTB-XL+ reference medians from Marquette 12SL and University of Glasgow (Uni-G) using similarity metrics and downstream classification. An interactive web tool (http://segmentation.ecgx.ai) was released to support further research. In the external set, delineation for PQ interval, QRS duration, and QT interval had mean errors of -0.9 ± 10.4 ms, 0.2 ± 7.4 ms, and 1.8 ± 16.0 ms. Rhythm classification was performed by assigning class labels to segmented waveform components, with weighted F1 scores of 0.94 for P waves and 0.89 for QRS complexes. Qualitative review showed good alignment with reference beats and differences in beat selection, adjacent beat handling, and QRS identification. Downstream classification performance was equivalent to Marquette 12SL medians but statistically superior to Uni-G medians for 7/10 diagnostic labels.</p><p><strong>Conclusion: </strong>This study demonstrates a robust, clinically applicable, vendor- and lead-agnostic deep learning model for ECG analysis, encompassing waveform delineation, rhythm classification, and median beat construction.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag122"},"PeriodicalIF":4.4,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13528239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868200","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":"Development of an LLM pipeline exceeding physician-documented cardiovascular risk scores under routine clinical conditions.","authors":"Tobias Roeschl, Marie Hoffmann, Axel Unbehaun, Henryk Dreger, Gerhard Hindricks, Volkmar Falk, Ran Balicer, Radu Tanacli, Felix Hohendanner, Alexander Meyer","doi":"10.1093/ehjdh/ztag124","DOIUrl":"https://doi.org/10.1093/ehjdh/ztag124","url":null,"abstract":"<p><strong>Aims: </strong>Risk scores are essential to evidence-based cardiovascular care, but manual calculation is labour intensive and error prone. Large language models (LLMs) could automate this process, yet LLMs are limited by their propensity for calculation errors and factual hallucinations. Pipelines separating LLM-based data extraction from deterministic score computation may improve reliability and transparency.</p><p><strong>Methods and results: </strong>We conducted a retrospective diagnostic study at a quaternary heart centre in Germany (January 2020 to July 2023). Patients with atrial fibrillation (<i>n</i> = 179) from an ablation registry and patients with severe aortic stenosis (<i>n</i> = 76) evaluated by a heart team were included. Six LLMs (GPT-5.2, Gemini 3.1 Pro, DeepSeek-R1, Qwen3, GPT-OSS 120B, and Kimi K2.5) were tested in standalone, retrieval-augmented generation (RAG), and pipeline configurations to compute HAS-BLED, CHA₂DS₂-VASc, and EuroSCORE II scores from routine clinical reports. Accuracy was assessed against expert-adjudicated ground truth using root mean squared error (RMSE) and Krippendorff's α to evaluate numerical deviation and categorical agreement, respectively. Pipeline-generated scores showed substantially higher agreement with expert adjudication than standalone LLMs, LLMs with RAG, and treating physicians (mean Krippendorff's α: 0.78 vs. 0.32 vs. 0.39 vs. 0.31) and lower deviation from ground truth (mean RMSE: 0.89 vs. 5.81 vs. 1.85 vs. 1.34).</p><p><strong>Conclusion: </strong>Pipelines combining expert-curated knowledge injection, LLM-based clinical data extraction, and deterministic score calculation enable accurate and scalable cardiovascular risk score computation from unstructured real-world clinical data, outperforming physician-documented scores. Such pipelines could form the basis for clinical decision-support systems that automate routine risk assessment, reduce clinician workload, and promote more consistent evidence-based care.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag124"},"PeriodicalIF":4.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802422","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 artificial intelligence-assisted literature search tools for identifying clinically meaningful evidence in cardiology.","authors":"Riccardo Di Febo, Maximiliano Jeanneret Medina, Alexandre Renaud, Maryam Dridi, Valentine Pecriaux, Benoit Lequeux, Stephane Lafitte, Baptiste Maille, Aymeric Menet","doi":"10.1093/ehjdh/ztag125","DOIUrl":"10.1093/ehjdh/ztag125","url":null,"abstract":"<p><strong>Aims: </strong>The rapid expansion of biomedical literature challenges clinicians' and researchers' ability to identify clinically meaningful evidence. We systematically compared five literature search tools, four artificial intelligence (AI)-assisted and one conventional, across clinically relevant cardiology research scenarios, using a blinded expert-validated gold standard to assess their ability to retrieve relevant and key references.</p><p><strong>Methods and results: </strong>We evaluated ChatGPT-5, Elicit, Consensus, Scite, and PubMed across four cardiology topics defined by maturity and specificity, with multiple standardized prompts. Three electrophysiology experts independently and blindly rated all retrieved references, defining two gold standards: expert-rated relevance and expert-selected key references. ChatGPT-5 achieved the highest proportion of relevant articles (90% [88-100], <i>P</i> < 0.001) and the highest key-reference overlap (60% [43-68], <i>P</i> < 0.001), whereas Scite performed lowest (20% and 10%, respectively). The tool was the primary determinant of performance (partial <i>R</i> ² = 0.50), whereas prompt formulation had no significant effect. In a pre-specified subanalysis restricted to clinical studies, ChatGPT-5 and human-conducted systematic reviews overlapped by 42% (96% of shared articles highly relevant), with 58% distinct references, indicating complementary AI and human retrieval; ChatGPT-5 produced hallucinated citations when long reference lists were requested for emerging topics, underscoring the need for human verification.</p><p><strong>Conclusion: </strong>AI-assisted tools showed heterogeneous performance, ChatGPT-5 performing best in this cardiology setting. These preliminary, context-specific findings support hybrid human-AI strategies in which AI complements rather than replaces transparent database searches such as PubMed; larger-scale, multi-domain studies are needed to confirm and generalize them.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag125"},"PeriodicalIF":4.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13520940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842134","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":"Disease-focused evaluation of AI-ECG tools: clarifying study design, findings, and the principles of open science.","authors":"Gamze Babur Guler, Arda Guler, Ibrahim Halil Tanboga","doi":"10.1093/ehjdh/ztag120","DOIUrl":"10.1093/ehjdh/ztag120","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag120"},"PeriodicalIF":4.4,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674472","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":"How should patient knowledge trigger clinical action in artificial intelligence-enabled pulmonary hypertension care?","authors":"Ilma Nascimento, Caio Julio Cesar Dos Santos Fernandes","doi":"10.1093/ehjdh/ztag123","DOIUrl":"10.1093/ehjdh/ztag123","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag123"},"PeriodicalIF":4.4,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674491","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":"Correction to: A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study.","authors":"","doi":"10.1093/ehjdh/ztag116","DOIUrl":"https://doi.org/10.1093/ehjdh/ztag116","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/ehjdh/ztag043.].</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag116"},"PeriodicalIF":4.4,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586151","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 impact of digital health on hospitalization and mortality in heart failure patients in low- and middle-income countries: a systematic review and meta-analysis.","authors":"Preston Nicely, Emily Eichstaedt, Charles Heald, Jack Simmons, Shiv Ishpujani, Stephen Clarkson","doi":"10.1093/ehjdh/ztag117","DOIUrl":"10.1093/ehjdh/ztag117","url":null,"abstract":"<p><strong>Aim: </strong>Heart disease is the leading cause of death worldwide, and the growing prevalence of heart failure (HF) is a key contributor. Digital health (dHealth) modalities including mobile applications, telehealth, and wearables have been developed to improve HF care, though little is known about their impact in low- and middle-income countries (LMICs). The aim of this review is to identify studies from LMICs that evaluated dHealth interventions for HF and determine the impact of those interventions on hospitalization and mortality.</p><p><strong>Methods and results: </strong>Six databases were searched from 1 January 1990 to 11 June 2025. A total of 3524 articles were screened (Cohen's κ = 69%). Included studies had a dHealth intervention, were a randomized controlled trial (RCT), occurred in a LMIC, and reported outcomes of interest. Risk of bias for RCTs was assessed using the Cochrane Risk of Bias 2 Tool. Fifteen RCTs (<i>n</i> = 4568) with studies in Brazil (33.3%), China (26.7%), and Thailand (13.3%) were included. Interventions included telemonitoring, mobile apps, and wearables. The population averaged 61.1 years, 31.4% female, and 45.7% New York Heart Association class III-IV. Random-effects meta-analysis showed dHealth reduced all-cause hospitalizations [risk ratio (RR) 0.73, 95% confidence interval (CI) 0.59-0.90], HF hospitalizations (RR 0.70, 95% CI 0.59-0.84), and all-cause mortality (RR 0.86, 95% CI 0.79-1.00); HF mortality was neutral (RR 0.64, 95% CI 0.37-1.11).</p><p><strong>Conclusion: </strong>Digital health in LMICs lowers hospitalizations and may reduce all-cause mortality, though the benefit for HF mortality remains uncertain. These findings support broader dHealth adoption in LMICs to address HF burden.PROSPERO registration number: CRD420251079715.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag117"},"PeriodicalIF":4.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690061","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":"Large language model-based simulated patient training for heart failure palliative care communication: a pilot study.","authors":"Risa Kishikawa, Hiroyuki Morita, Satoshi Kodera","doi":"10.1093/ehjdh/ztag119","DOIUrl":"10.1093/ehjdh/ztag119","url":null,"abstract":"<p><strong>Aims: </strong>Heart failure (HF) palliative care communication is essential but difficult to train at scale because conventional role-play programmes require facilitators and standardized patients. Large language models (LLMs) have emerged as potential tools for scalable communication training. This pilot study aimed to evaluate the feasibility of a web-based LLM-driven communication training application and to explore its early educational signal on physicians' self-efficacy.</p><p><strong>Methods and results: </strong>This single-arm pilot study included physicians who completed one session using a Japanese-language web-based LLM application designed to simulate patients with advanced HF and provide automated framework-based feedback. The primary outcome was change in self-efficacy scores assessed by pre- and post-session questionnaires. Ten sessions were analysed. Physicians engaged in a mean of 7.6 ± 2.0 dialogue turns. Mean response time per model output and feedback generation were approximately 3 and 17 s, respectively. Significant improvements were observed in knowledge of palliative care communication (mean difference +1.7, adjusted <i>P</i> < 0.01) and confidence in HF palliative care communication (+1.2, adjusted <i>P</i> = 0.03). Other domains showed non-significant changes.</p><p><strong>Conclusion: </strong>This pilot study demonstrated the feasibility of a web-based LLM-simulated patient system and suggested an early educational signal in physicians' self-efficacy for HF palliative care communication. Our scalable LLM-driven communication training may complement traditional educational approaches with further evaluation in larger controlled studies.</p><p><strong>Clinical trial registration: </strong>Trial registration number: UMIN000059988.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag119"},"PeriodicalIF":4.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674660","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":"Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.","authors":"Libor Pastika, Konstantinos Patlatzoglou, Ewa Sieliwonczyk, Joseph Barker, Boroumand Zeidaabadi, Kathryn A McGurk, Sandhi M Barreto, Lidyane Camelo, Sadia Khan, William R Scott, Declan P O'Regan, Bruce B Duncan, Maria I Schmidt, James S Ware, Shivani Misra, Daniel B Kramer, Jonathan W Waks, Nicholas S Peters, Antonio Luiz Pinho Ribeiro, Arunashis Sau, Fu Siong Ng","doi":"10.1093/ehjdh/ztag118","DOIUrl":"10.1093/ehjdh/ztag118","url":null,"abstract":"<p><strong>Aims: </strong>A significant proportion of type 2 diabetes cases remain undiagnosed despite screening advances, carrying substantial cardiometabolic risk. Artificial intelligence-enhanced electrocardiography (AI-ECG) detects subtle ECG changes in subclinical disease, potentially enabling opportunistic screening.</p><p><strong>Methods and results: </strong>We developed AI-ECG Risk Estimator for Diabetes Mellitus (AIRE-DM), a convolutional neural network with discrete-time survival loss, for diagnosis of prevalent and prediction of incident type 2 diabetes. It was trained on 1 163 401 ECGs from 189 537 individuals from Beth Israel Deaconess Medical Center (BIDMC) and externally validated in UK Biobank (UKB; <i>n</i> = 65 606) and ELSA-Brasil (<i>n</i> = 13 739). AI-ECG Risk Estimator for Diabetes Mellitus demonstrated moderate discrimination for prevalent type 2 diabetes (area under the receiver operating characteristic curve: BIDMC 0.724, UKB 0.733, ELSA-Brasil 0.706) and incident type 2 diabetes (C-index: BIDMC 0.667, UKB 0.688, ELSA-Brasil 0.625). The highest AIRE-DM risk quartile had elevated incident diabetes risk vs. the lowest (hazard ratio: BIDMC 4.75, UKB 7.52, ELSA-Brasil 3.96). AI-ECG Risk Estimator for Diabetes Mellitus was non-inferior to the American Diabetes Association Diabetes Risk Test in BIDMC, with improved predictive accuracy when combined. In normoglycaemic patients, AIRE-DM was superior to glycated haemoglobin (HbA1c) for predicting incident diabetes in BIDMC and non-inferior in ELSA-Brasil. The highest risk quartile reached 5% cumulative type 2 diabetes mellitus incidence 5.4 years (BIDMC) and 4.8 years (ELSA-Brasil) earlier than the lowest risk quartile, after adjusting for HbA1c, age, and sex. Phenome- and genome-wide association studies revealed biologically plausible associations with glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness, and lipid metabolism.</p><p><strong>Conclusion: </strong>AI-ECG Risk Estimator for Diabetes Mellitus detects prevalent type 2 diabetes and predicts incident disease, uniquely identifying high-risk individuals within the normoglycaemic range. Combined with clinical scores or biomarkers, it enhances risk stratification, enabling earlier intervention.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag118"},"PeriodicalIF":4.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13525370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857918","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":"Rationale and design of the AutoHealth study: prospective clinical validation of continuous multimodal in-vehicle health monitoring in real-world mobility.","authors":"Dominik Naumann, Tatjana Amler, Doreen Schoeppenthau, Sergej Holzmann, Jörg Preißinger, Matthias Franz, Nils Hinrichs, Felix Schoenrath, Alexander Meyer","doi":"10.1093/ehjdh/ztag114","DOIUrl":"10.1093/ehjdh/ztag114","url":null,"abstract":"<p><strong>Aims: </strong>Continuous physiological monitoring outside clinical environments remains limited by usability, reproducibility, and user adherence. Vehicles offer a semi-controlled setting enabling unobtrusive multimodal sensing during everyday mobility. The Automotive Health Proof of Concept Trial (AutoHealth) investigates the real-world feasibility and accuracy of continuous in-vehicle cardiovascular monitoring using integrated optical, electrical, and acoustic sensors benchmarked against medical-grade reference standards.</p><p><strong>Methods and results: </strong>AutoHealth is a prospective, observational cohort study at Charité - Universitätsmedizin Berlin enrolling adults across five predefined cohorts: healthy individuals, patients with an elevated cardiometabolic risk, HFpEF, HFrEF, and persistent atrial fibrillation. Participants undergo comprehensive baseline phenotyping followed by a structured in-vehicle session comprising static and dynamic driving segments and predefined physical and cognitive stress tasks. Synchronized biosignals including rPPG, steering-wheel ECG, phonocardiography, and voice-derived features are compared with clinical reference measurements. The primary endpoint is the median absolute percentage error (MAPE) of in-vehicle vital sign estimates vs. reference measurements, with successful performance defined <i>a priori</i> as MAPE ≤10%. Secondary endpoints include the proportion of measurements meeting predefined clinical accuracy thresholds, arrhythmia detection performance, characterization of autonomic stress response, and correlations with functional mobility metrics. The study adheres to STROBE and SPIRIT-AI guidelines and is registered in the German Clinical Trials Register.</p><p><strong>Discussion: </strong>AutoHealth is designed to provide a prospective clinical validation of continuous multimodal cardiovascular monitoring inside a modified production vehicle under real-world driving conditions and reproducible closed-course testing. Study findings will characterize feasibility, performance, and translational potential of Automotive Health as a scalable prevention and remote physiological monitoring paradigm.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag114"},"PeriodicalIF":4.4,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852060","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}