European heart journal. Digital health最新文献

{"title":"Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.","authors":"Jiajia Si, Yiliang Bao, Fengling Chen, Yue Wang, Meimei Zeng, Nongyue He, Zhu Chen, Yuan Guo","doi":"10.1093/ehjdh/ztae092","DOIUrl":"10.1093/ehjdh/ztae092","url":null,"abstract":"<p><strong>Aims: </strong>The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.</p><p><strong>Methods and results: </strong>We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF). The generalization ability of the model is validated on public datasets CPSC2018, PhysioNet2017, and PTB-XL, and we explored the performance of oversampling, resampling, and hybrid datasets. Finally, additional PhysioNet2021 was added to validate the robustness and applicability in different clinical settings. We employed the SHapley Additive exPlanations (SHAP) method to interpret the model's predictions. The F1-score, Precision, and area under the ROC curve (AUC) of the CLA-AF model on YY2023 are 0.956, 0.970, and 1.00, respectively. Similarly, the AUC on CPSC2018, PhysioNet2017, and PTB-XL reached above 0.95, demonstrating its strong generalization ability. After oversampling PhysioNet2017, F1-score and Recall improved by 0.156 and 0.260. Generalization ability varied with sampling frequency. The model trained from the hybrid dataset has the most robust generalization ability, achieving an AUC of 0.96 or more. The AUC of PhysioNet2021 is 1.00, which proves the applicability of CLA-AF. The SHAP values visualization results demonstrate that the model's interpretation of AF aligns with the diagnostic criteria of AF.</p><p><strong>Conclusion: </strong>The CLA-AF model demonstrates a high accuracy in recognizing AF from ECG, exhibiting remarkable applicability and robustness in diverse clinical settings.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"82-95"},"PeriodicalIF":3.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025878","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":"Wristwatch pulse wave monitoring: assessing daily activity post-catheter ablation for atrial fibrillation.","authors":"Noriko Matsushita Nonoguchi, Kyoko Soejima, Yumi Katsume, Kyoko Hoshida, Ikuko Togashi, Ayumi Goda, Akiko Ueda, Seiichiro Matsuo, Toshiaki Sato, Yuichi Takano, Fumio Koyama, Shin Fujita, Kunihiro Nishimura, Takashi Kohno","doi":"10.1093/ehjdh/ztae091","DOIUrl":"10.1093/ehjdh/ztae091","url":null,"abstract":"<p><strong>Aims: </strong>Atrial fibrillation (AF) leads to impaired exercise capacity, and catheter ablation (CA) for AF improves exercise capacity. However, the precise changes in daily activities after CA for AF remain unclear. The authors aimed to evaluate the changes in daily activities following CA for AF using a wristwatch-type pulse wave monitor (PWM), which tracks steps and exercise time, estimates burnt daily calories, and records sleep duration, in addition to establishing the rhythm diagnosis of AF or non-AF.</p><p><strong>Methods and results: </strong>One hundred and twenty-three patients with AF (97 paroxysmal, 26 persistent) wore a wristwatch-type PWM for 1 week duration at three time points: before, 1 month after, and 3 months after ablation. Daily activity data were compared. Steps did not change in both groups, and the number of burnt daily calories and total exercise time increased after CA in patients with paroxysmal AF (burnt daily calories: before, 1591 kcal/day; 1 month, 1688 kcal/day; and 3 months, 1624 kcal/day; <i>P</i> < 0.001 and exercise time: before, 45.8 min; 1 month, 51.2 min; and 3 months, 56.3 min; <i>P</i> = 0.023). Sleep hours significantly increased (paroxysmal AF: before, 6.8 h; 1 month, 7.1 h; and 3 months, 7.1 h; <i>P</i> = 0.039 and persistent AF: before, 6.0 h; 1 month, 7.0 h; and 3 months, 7.0 h; <i>P</i> = 0.007).</p><p><strong>Conclusion: </strong>Using a wristwatch-type PWM, we demonstrated changes in daily activities after CA in patients with AF.</p><p><strong>Trial registration number: </strong>jRCT1030210022.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"96-103"},"PeriodicalIF":3.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025895","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":"Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics.","authors":"Tim I Johann, Karen Otte, Fabian Prasser, Christoph Dieterich","doi":"10.1093/ehjdh/ztae083","DOIUrl":"10.1093/ehjdh/ztae083","url":null,"abstract":"<p><strong>Aims: </strong>Data availability remains a critical challenge in modern, data-driven medical research. Due to the sensitive nature of patient health records, they are rightfully subject to stringent privacy protection measures. One way to overcome these restrictions is to preserve patient privacy by using anonymization and synthetization strategies. In this work, we investigate the effectiveness of these methods for protecting patient privacy using real-world cardiology health records.</p><p><strong>Methods and results: </strong>We implemented anonymization and synthetization techniques for a structure data set, which was collected during the HiGHmed Use Case Cardiology study. We employed the data anonymization tool ARX and the data synthetization framework ASyH individually and in combination. We evaluated the utility and shortcomings of the different approaches by statistical analyses and privacy risk assessments. Data utility was assessed by computing two heart failure risk scores on the protected data sets. We observed only minimal deviations to scores from the original data set. Additionally, we performed a re-identification risk analysis and found only minor residual risks for common types of privacy threats.</p><p><strong>Conclusion: </strong>We could demonstrate that anonymization and synthetization methods protect privacy while retaining data utility for heart failure risk assessment. Both approaches and a combination thereof introduce only minimal deviations from the original data set over all features. While data synthesis techniques produce any number of new records, data anonymization techniques offer more formal privacy guarantees. Consequently, data synthesis on anonymized data further enhances privacy protection with little impacting data utility. We share all generated data sets with the scientific community through a use and access agreement.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"147-154"},"PeriodicalIF":3.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025791","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 discharge programme after transcatheter aortic valve implantation based on close follow-up supported by telemonitoring using artificial intelligence: the TeleTAVI study.","authors":"Marta Herrero-Brocal, Raquel Samper, Jorge Riquelme, Javier Pineda, Pascual Bordes, Fernando Torres-Mezcua, José Valencia, Francisco Torres-Saura, María González Manso, Raquel Ajo, Juan Arenas, Eloísa Feliu, Juan Gabriel Martínez, Juan Miguel Ruiz-Nodar","doi":"10.1093/ehjdh/ztae089","DOIUrl":"10.1093/ehjdh/ztae089","url":null,"abstract":"<p><strong>Aims: </strong>Evidence regarding the safety of early discharge following transcatheter aortic valve implantation (TAVI) is limited. The aim of this study was to evaluate the safety of very early (<24) and early discharge (24-48 h) as compared to standard discharge (>48 h), supported by the implementation of a voice-based virtual assistant using artificial intelligence (AI) and natural language processing.</p><p><strong>Methods and results: </strong>Single-arm prospective observational study that included consecutive patients who underwent TAVI in a tertiary hospital in 2023 and were discharged under an AI follow-up programme. Primary endpoint was a composite of death, pacemaker implantation, readmission for heart failure, stroke, acute myocardial infarction, major vascular complications, or major bleeding, at 30-day follow-up. A total of 274 patients were included. 110 (40.1%) patients were discharged very early (<24 h), 90 (32.9%) early (24-48 h), and 74 (27.0%) were discharged after 48 h. At 30-day follow-up, no significant differences were found among patients discharged very early, early, and those discharged after 48 h for the primary endpoint (very early 9.1% vs. early 11.1% vs. standard 9.5%; <i>P</i> = 0.88). The AI platform detected complications that could be effectively addressed. The implementation of this follow-up system was simple and satisfactory for TAVI patients.</p><p><strong>Conclusion: </strong>Early and very early discharge in patients undergoing TAVI, supported by close follow-up using AI, were shown to be safe. Patients with early and very early discharge had similar 30-day event rates compared to those with longer hospital stays. The AI system contributed to the early detection and resolution of complications.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"73-81"},"PeriodicalIF":3.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025839","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 eHealth intervention for patients with a low socioeconomic position during their waiting period preceding cardiac rehabilitation: a randomized feasibility study.","authors":"Jasper S Faber, Jos J Kraal, Nienke Ter Hoeve, Isra Al-Dhahir, Linda D Breeman, Niels H Chavannes, Andrea W M Evers, Hans B J Bussmann, Valentijn T Visch, Rita J G van den Berg-Emons","doi":"10.1093/ehjdh/ztae084","DOIUrl":"10.1093/ehjdh/ztae084","url":null,"abstract":"<p><strong>Aims: </strong>Cardiac rehabilitation (CR) shows lower effectiveness and higher dropouts among people with a low socioeconomic position (SEP) compared to those with a high SEP. This study evaluated an eHealth intervention aimed at supporting patients with a low SEP during their waiting period preceding CR.</p><p><strong>Methods and results: </strong>Participants with a low SEP in their waiting period before CR were randomized into an intervention group, receiving guidance videos, patient narratives, and practical tips, or into a control group. We evaluated adherence (usage metrics), acceptance (modified Usefulness, Satisfaction, and Ease of use questionnaire), and changes in feelings of certainty and guidance between the waiting period's start and end. Semi-structured interviews provided complementary insights. The study involved 41 participants [median interquartile range (IQR) age 62 (14) years; 33 males], with 21 participants allocated to the intervention group, using the eHealth intervention for a median (IQR) duration of 16 (10) days, using it on a median (IQR) of 100% (25) of these days, and viewing 88% of the available messages. Key adherence themes were daily routine compatibility and curiosity. Acceptance rates were 86% for usability, 67% for satisfaction, and 43% for usefulness. No significant effects on certainty and guidance were observed, but qualitative data suggested that the intervention helped to inform and set expectations.</p><p><strong>Conclusion: </strong>The study found the eHealth intervention feasible for cardiac patients with a low SEP, with good adherence, usability, and satisfaction. However, it showed no effect on feelings of certainty and guidance. Through further optimization of its content, the intervention holds promise to improve emotional resilience during the waiting period.</p><p><strong>Registration: </strong>This trial is registered as follows: 'Evaluation of a Preparatory eHealth Intervention to Support Cardiac Patients During Their Waiting Period (PReCARE)' at ClinicalTrials.gov (NCT05698121, https://clinicaltrials.gov/study/NCT05698121).</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"115-125"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025691","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":"Performance of computerized self-reported medical history taking and HEAR score for safe early rule-out of cardiac events in acute chest pain patients: the CLEOS-CPDS prospective cohort study.","authors":"Helge Brandberg, Fanny Schierenbeck, Carl Johan Sundberg, Sabine Koch, Jonas Spaak, Thomas Kahan","doi":"10.1093/ehjdh/ztae087","DOIUrl":"10.1093/ehjdh/ztae087","url":null,"abstract":"<p><strong>Aims: </strong>A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.</p><p><strong>Methods and results: </strong>Prospective study including clinically stable adults presenting with chest pain at the emergency department (ED) of Danderyd University Hospital (Stockholm, Sweden), in 2017-19. Participants entered their medical histories on touchscreen tablets using CHT software. The HEAR and HEART scores were calculated from CHT data. Thirty-day MACE and acute coronary syndrome (ACS) outcomes were retrieved, and the diagnostic accuracy was assessed. Logistic regression was used to determine the most predictive components of the HEAR score. Among 1000 patients, HEART and HEAR scores could be calculated from CHT data in 648 and 666 cases, respectively, with negative predictive values [95% confidence interval (CI)] of 0.98 (0.97-0.99) and 0.99 (0.96-1.00). Two patients with HEAR score <2 experienced a 30-day MACE. The age [odds ratio (OR) 2.75, 95% CI 1.62-4.66] and history (OR 2.38, 95% CI 1.52-3.71) components of the HEAR score were most predictive of MACE. Acute coronary syndrome outcomes provided similar results.</p><p><strong>Conclusion: </strong>The HEAR score acquired by CHT identifies very-low-risk patients with chest pain in the ED, safely ruling out ACS and MACE. This highlights the value of computerized history taking by patients, which may reduce unnecessary tests and hospital admissions.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT03439449.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"104-114"},"PeriodicalIF":3.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025865","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":"Use of artificial intelligence to predict outcomes in mild aortic valve stenosis.","authors":"Raghav R Julakanti, Ratnasari Padang, Christopher G Scott, Jordi Dahl, Nader J Al-Shakarchi, Coby Metzger, Alon Lanyado, John I Jackson, Vuyisile T Nkomo, Patricia A Pellikka","doi":"10.1093/ehjdh/ztae085","DOIUrl":"10.1093/ehjdh/ztae085","url":null,"abstract":"<p><strong>Aims: </strong>Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.</p><p><strong>Methods and results: </strong>A comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained to identify progression to severe stenosis and all-cause mortality and tested in their performance for endpoints at 2 and 5 years. In the independent test group, the AS progression model differentiated those with progression to severe AS within 2 and 5 years with an area under the curve (AUC) of 0.86 for both. The feature of greatest importance was aortic valve mean gradient, followed by other valve haemodynamic measurements including valve area and dimensionless index. The mortality model identified those with mortality within 2 and 5 years with an AUC of 0.84 and 0.87, respectively. Smaller reduced-input validation models had similarly robust findings.</p><p><strong>Conclusion: </strong>Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"63-72"},"PeriodicalIF":3.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025883","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":"Multimodal data integration to predict atrial fibrillation.","authors":"Yuchen Yao, Michael J Zhang, Wendy Wang, Zhong Zhuang, Ruoyu He, Yuekai Ji, Katherine A Knutson, Faye L Norby, Alvaro Alonso, Elsayed Z Soliman, Weihong Tang, James S Pankow, Wei Pan, Lin Yee Chen","doi":"10.1093/ehjdh/ztae081","DOIUrl":"10.1093/ehjdh/ztae081","url":null,"abstract":"<p><strong>Aims: </strong>Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.</p><p><strong>Methods and results: </strong>We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF. We constructed a (i) clinical risk score using CHARGE-AF clinical variables, (ii) polygenic risk score using pre-determined weights, (iii) protein risk score using regularized logistic regression, and (iv) ECG risk score from a convolutional neural network. Risk prediction performance was measured using regularized logistic regression. After a median follow-up of 15.1 years, 1910 AF events occurred since Visit 3 and 229 participants had prevalent AF at Visit 5. The area under curve (AUC) improved from 0.660 to 0.752 (95% CI, 0.741-0.763) and from 0.737 to 0.854 (95% CI, 0.828-0.880) after addition of the polygenic risk score to the CHARGE-AF clinical variables for predicting incident and prevalent AF, respectively. Further addition of ECG and protein risk scores improved the AUC to 0.763 (95% CI, 0.753-0.772) and 0.875 (95% CI, 0.851-0.899) for predicting incident and prevalent AF, respectively.</p><p><strong>Conclusion: </strong>A combination of clinical and polygenic risk scores was the most effective and parsimonious approach to predicting AF. Further addition of an ECG risk score or protein risk score provided only modest incremental improvement for predicting AF.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"126-136"},"PeriodicalIF":3.9,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025856","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":"Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.","authors":"Tianyi Liu, Andrew Krentz, Lei Lu, Vasa Curcin","doi":"10.1093/ehjdh/ztae080","DOIUrl":"10.1093/ehjdh/ztae080","url":null,"abstract":"<p><p>Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction. A systematic review and random-effect meta-analysis were conducted according to preferred reporting items for systematic reviews and meta-analyses guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on performance metrics such as area under the curve (AUC) and heterogeneity across models. ML models, particularly random forest and deep learning, demonstrated superior performance, with the highest recorded pooled AUCs of 0.865 (95% CI: 0.812-0.917) and 0.847 (95% CI: 0.766-0.927), respectively. These significantly outperformed the conventional risk score of 0.765 (95% CI: 0.734-0.796). However, significant heterogeneity (I² > 99%) and potential publication bias were noted across the studies. While ML models show enhanced calibration for CVD risk, substantial variability and methodological concerns limit their current clinical applicability. Future research should address these issues by enhancing methodological transparency and standardization to improve the reliability and utility of these models in clinical settings. This study highlights the advanced capabilities of ML models in CVD risk prediction and emphasizes the need for rigorous validation to facilitate their integration into clinical practice.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"7-22"},"PeriodicalIF":3.9,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025853","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":"Fine-tuned large language models can generate expert-level echocardiography reports.","authors":"Achille Sowa, Robert Avram","doi":"10.1093/ehjdh/ztae079","DOIUrl":"10.1093/ehjdh/ztae079","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"5-6"},"PeriodicalIF":3.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025841","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}