Lancet Digital Health最新文献

{"title":"Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.","authors":"Trystan Macdonald, Zhivko Zhelev, Xiaoxuan Liu, Christopher Hyde, Jiri Fajtl, Catherine Egan, Adnan Tufail, Alicja R Rudnicka, Bethany Shinkins, Rosalind Given-Wilson, J Kevin Dunbar, Steve Halligan, Peter Scanlon, Anne Mackie, Sian Taylor-Philips, Alastair K Denniston","doi":"10.1016/j.landig.2024.12.004","DOIUrl":"https://doi.org/10.1016/j.landig.2024.12.004","url":null,"abstract":"<p><p>Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment of sight-threatening disease. There is long-standing interest in the possibility of automating parts of this process through artificial intelligence, commonly known as automated retinal imaging analysis software (ARIAS). A number of such products are now on the market. In the UK, Scotland has used a rules-based autograder since 2011, but the diabetic eye screening programmes in the rest of the UK rely solely on human graders. With more sophisticated machine learning-based ARIAS now available and greater challenges in terms of human grader capacity, in 2019 the UK's National Screening Committee (NSC) was asked to consider the modification of diabetic eye screening in England with ARIAS. Following up on a review of ARIAS research highlighting the strengths and limitations of existing evidence, the NSC here sets out their considerations for evaluating evidence to support the introduction of ARIAS into the diabetic eye screening programme.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential effects of the social media age ban in Australia for children younger than 16 years","authors":"Jasmine Fardouly","doi":"10.1016/j.landig.2025.01.016","DOIUrl":"10.1016/j.landig.2025.01.016","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e235-e236"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Snapshot artificial intelligence—determination of ejection fraction from a single frame still image: a multi-institutional, retrospective model development and validation study","authors":"Jeffrey G Malins PhD ,&nbsp;D M Anisuzzaman PhD ,&nbsp;John I Jackson PhD ,&nbsp;Eunjung Lee PhD ,&nbsp;Jwan A Naser MBBS ,&nbsp;Behrouz Rostami PhD ,&nbsp;Jared G Bird MD ,&nbsp;Dan Spiegelstein MD ,&nbsp;Talia Amar MSc ,&nbsp;Prof Jae K Oh MD ,&nbsp;Prof Patricia A Pellikka MD ,&nbsp;Jeremy J Thaden MD ,&nbsp;Prof Francisco Lopez-Jimenez MD MSc ,&nbsp;Prof Sorin V Pislaru MD PhD ,&nbsp;Prof Paul A Friedman MD ,&nbsp;Prof Garvan C Kane MD PhD ,&nbsp;Zachi I Attia PhD","doi":"10.1016/j.landig.2025.02.003","DOIUrl":"10.1016/j.landig.2025.02.003","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) is poised to transform point-of-care practice by providing rapid snapshots of cardiac functioning. Although previous AI models have been developed to estimate left ventricular ejection fraction (LVEF), they have typically used video clips as input, which can be computationally intensive. In the current study, we aimed to develop an LVEF estimation model that takes in static frames as input.</div></div><div><h3>Methods</h3><div>Using retrospective transthoracic echocardiography (TTE) data from Mayo Clinic Rochester and Mayo Clinic Health System sites (training: n=19 627; interval validation: n=862), we developed a two-dimensional convolutional neural network model that provides an LVEF estimate associated with an input frame from an echocardiogram video. We then evaluated model performance for Mayo Clinic TTE data (Rochester, n=1890; Arizona, n=1695; Florida, n=1862), the EchoNet-Dynamic TTE dataset (n=10 015), a prospective cohort of patients from whom TTE and handheld cardiac ultrasound (HCU) were simultaneously collected (n=625), and a prospective cohort of patients from whom HCU clips were collected by expert sonographers and novice users (n=100, distributed across three external sites).</div></div><div><h3>Findings</h3><div>We observed consistently strong model performance when estimates from single frames were averaged across multiple video clips, even when only one frame was taken per video (for classifying LVEF ≤40% <em>vs</em> LVEF&gt;40%, area under the receiver operating characteristic curve [AUC]&gt;0·90 for all datasets except for HCU clips collected by novice users, for which AUC&gt;0·85). We also observed that LVEF estimates differed slightly depending on the phase of the cardiac cycle when images were captured.</div></div><div><h3>Interpretation</h3><div>When aiming to rapidly deploy such models, single frames from multiple videos might be sufficient for LVEF classification. Furthermore, the observed sensitivity to the cardiac cycle offers some insights on model performance from an explainability perspective.</div></div><div><h3>Funding</h3><div>Internal institutional funds provided by the Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e255-e263"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study","authors":"Joshua Mayourian MD ,&nbsp;Ivor B Asztalos MD ,&nbsp;Amr El-Bokl MD ,&nbsp;Platon Lukyanenko PhD ,&nbsp;Ryan L Kobayashi MD ,&nbsp;William G La Cava MD ,&nbsp;Sunil J Ghelani MD ,&nbsp;Prof Victoria L Vetter MD ,&nbsp;Prof John K Triedman MD","doi":"10.1016/j.landig.2025.01.001","DOIUrl":"10.1016/j.landig.2025.01.001","url":null,"abstract":"<div><h3>Background</h3><div>Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions.</div></div><div><h3>Methods</h3><div>We trained a convolutional neural network on paired ECG–echocardiograms (≤2 days apart) across the lifespan of a wide range of congenital heart disease lesions to detect left ventricular ejection fraction (LVEF) of 40% or less. Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children's Hospital (Boston, MA, USA) and externally at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves.</div></div><div><h3>Findings</h3><div>The training cohort comprised 124 265 ECG–echocardiogram pairs (49 158 patients; median age 10·5 years [IQR 3·5–16·8]; 3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%). Test groups included internal testing (21 068 patients; median age 10·9 years [IQR 3·7–17·0]; 3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%) and external validation (42 984 patients; median age 10·8 years [IQR 4·9–15·0]; 1313 [1·7%] of 76 400 ECG–echocardiogram pairs with LVEF ≤40%) cohorts. High model performance was achieved during internal testing (AUROC 0·95, AUPRC 0·33) and external validation (AUROC 0·96, AUPRC 0·25) for a wide range of congenital heart disease lesions. Patients with LVEF greater than 40% by echocardiogram who were deemed high risk by AI-ECG were more likely to have future dysfunction compared with low-risk patients (hazard ratio 12·1 [95% CI 8·4–17·3]; p&lt;0·0001). High-risk patients by AI-ECG were at increased risk of mortality in the overall cohort and lesion-specific subgroups. Common salient features highlighted across congenital heart disaese lesions include precordial QRS complexes and T waves, with common high-risk ECG features including deep V2 S waves and lateral precordial T wave inversion. A case study on patients with ventricular pacing showed similar findings.</div></div><div><h3>Interpretation</h3><div>Our externally validated algorithm shows promise in prediction of current and future LVSD in patients with congenital heart disease, providing a clinically impactful, inexpensive, and convenient cardiovascular health tool in this population.</div></div><div><h3>Funding</h3><div>Kostin Innovation Fund, Thrasher Research Fund Early Career Award, Boston Children's Hospital Electrophysiology Research Education Fund, National Institutes of Health, National Institute of Childhood Diseases and Human Development, and National Library of Medicine.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e264-e274"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighing the benefits and risks of collecting race and ethnicity data in clinical settings for medical artificial intelligence","authors":"Amelia Fiske PhD ,&nbsp;Sarah Blacker PhD ,&nbsp;Lester Darryl Geneviève PhD ,&nbsp;Theresa Willem MA ,&nbsp;Marie-Christine Fritzsche ,&nbsp;Alena Buyx MD ,&nbsp;Leo Anthony Celi MD ,&nbsp;Stuart McLennan PhD","doi":"10.1016/j.landig.2025.01.003","DOIUrl":"10.1016/j.landig.2025.01.003","url":null,"abstract":"<div><div>Many countries around the world do not collect race and ethnicity data in clinical settings. Without such identified data, it is difficult to identify biases in the training data or output of a given artificial intelligence (AI) algorithm, and to work towards medical AI tools that do not exclude or further harm marginalised groups. However, the collection of these data also poses specific risks to racially minoritised populations and other marginalised groups. This Viewpoint weighs the risks of collecting race and ethnicity data in clinical settings against the risks of not collecting those data. The collection of more comprehensive identified data (ie, data that include personal attributes such as race, ethnicity, and sex) has the possibility to benefit racially minoritised populations that have historically faced worse health outcomes and health-care access, and inadequate representation in research. However, the collection of extensive demographic data raises important concerns that include the construction of intersectional social categories (ie, race and its shifting meaning in different sociopolitical contexts), the risks of biological reductionism, and the potential for misuse, particularly in situations of historical exclusion, violence, conflict, genocide, and colonialism. Careful navigation of identified data collection is key to building better AI algorithms and to work towards medicine that does not exclude or harm marginalised groups.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e286-e294"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to Lancet Digit Health 2025; 7: e161–66","authors":"","doi":"10.1016/j.landig.2025.03.004","DOIUrl":"10.1016/j.landig.2025.03.004","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Page e237"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the social media ban","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.03.003","DOIUrl":"10.1016/j.landig.2025.03.003","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Page e232"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of home-based cardiac rehabilitation interventions delivered via mHealth technologies: a systematic review and meta-analysis","authors":"Leah Li MSN ,&nbsp;Mickaël Ringeval PhD ,&nbsp;Gerit Wagner PhD ,&nbsp;Prof Guy Paré PhD ,&nbsp;Cemal Ozemek PhD ,&nbsp;Spyros Kitsiou PhD","doi":"10.1016/j.landig.2025.01.011","DOIUrl":"10.1016/j.landig.2025.01.011","url":null,"abstract":"<div><h3>Background</h3><div>Centre-based cardiac rehabilitation (CBCR) is underused due to low referral rates, accessibility barriers, and socioeconomic constraints. mHealth technologies have the potential to address some of these challenges through remote delivery of home-based cardiac rehabilitation (HBCR). This study aims to assess the effects of mHealth HBCR interventions compared with usual care and CBCR in patients with heart disease.</div></div><div><h3>Methods</h3><div>We conducted a systematic review and meta-analysis of randomised controlled trials of mHealth HBCR interventions. Four electronic databases (MEDLINE, CENTRAL, CINAHL, and Embase) were searched from inception to March 31, 2023, with no restrictions on language or publication type. Eligible studies were randomised controlled trials of adult patients (age ≥18 years) with heart disease, comparing mHealth interventions with usual care or CBCR. The primary outcome of interest was aerobic exercise capacity, assessed with VO₂ peak or 6-min walk test (6MWT). Quality of evidence was assessed using the GRADE system. This review was registered with PROSPERO, CRD42024544087.</div></div><div><h3>Findings</h3><div>Our search yielded 9164 references, of which 135 were retained for full-text review. 13 randomised controlled trials met eligibility criteria and were included in the systematic review, involving 1508 adults with myocardial infarction, angina pectoris, or heart failure, or who had undergone revascularisation. Intervention duration ranged from 6 weeks to 24 weeks. Random-effects meta-analysis showed that, compared with usual care, mHealth HBCR significantly improved 6MWT (mean difference 24·74, 95% CI 9·88–39·60; 532 patients) and VO₂ peak (1·77, 1·19–2·35; 359 patients). No significant differences were found between mHealth HBCR and CBCR. Quality of evidence ranged from low to very low across outcomes due to risk of bias and imprecision (small sample size).</div></div><div><h3>Interpretation</h3><div>mHealth HBCR could improve access and health outcomes in patients who are unable to attend CBCR. Further research is needed to build a robust evidence base on the clinical effectiveness and cost-effectiveness of mHealth HBCR, particularly in comparison with CBCR, to inform clinical practice and policy.</div></div><div><h3>Funding</h3><div>None.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e238-e254"},"PeriodicalIF":23.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic organ-specific ageing signatures and 20-year risk of age-related diseases: the Whitehall II observational cohort study","authors":"Prof Mika Kivimäki FMedSci ,&nbsp;Philipp Frank PhD ,&nbsp;Jaana Pentti MSc ,&nbsp;Prof Markus Jokela PhD ,&nbsp;Solja T Nyberg PhD ,&nbsp;Acer Blake MSc ,&nbsp;Joni V Lindbohm MD PhD ,&nbsp;Hamilton Se-Hwee Oh PhD ,&nbsp;Prof Archana Singh-Manoux PhD ,&nbsp;Prof Tony Wyss-Coray PhD ,&nbsp;Prof Linda Partridge FMedSci","doi":"10.1016/j.landig.2025.01.006","DOIUrl":"10.1016/j.landig.2025.01.006","url":null,"abstract":"<div><h3>Background</h3><div>Biological ageing is known to vary among different organs within an individual, but the extent to which advanced ageing of specific organs increases the risk of age-related diseases in the same and other organs remains poorly understood.</div></div><div><h3>Methods</h3><div>In this observational cohort study, to assess the biological age of an individual's organs relative to those of same-aged peers, ie, organ age gaps, we collected plasma samples from 6235 middle-aged (age 45–69 years) participants of the Whitehall II prospective cohort study in London, UK, in 1997–99. Age gaps of nine organs were determined from plasma proteins via SomaScan (SomaLogic; Boulder, CO, USA) using the Python package organage. Following this assessment, we tracked participants for 20 years through linkage to national health records. Study outcomes were 45 individual age-related diseases and multimorbidity.</div></div><div><h3>Findings</h3><div>Over 123 712 person-years of observation (mean follow-up 19·8 years [SD 3·6]), after excluding baseline disease cases and adjusting for age, sex, ethnicity, and age gaps in organs other than the one under investigation, individuals with large organ age gaps showed an increased risk of 30 diseases. Six diseases were exclusively associated with accelerated ageing of their respective organ: liver failure (hazard ratio [HR] per SD increment in the organ age gap 2·13 [95% CI 1·41–3·22]), dilated cardiomyopathy (HR 1·65 [1·28–2·12]), chronic heart failure (HR 1·52 [1·40–1·65]), lung cancer (HR 1·29 [1·04–1·59]), agranulocytosis (HR 1·27 [1·07–1·51]), and lymphatic node metastasis (HR 1·23 [1·06–1·43]). 24 diseases were associated with more than one organ age gap or with organ age gaps not directly related to the disease location. Larger age gaps were also associated with elevated HRs of developing two or more diseases affecting different organs within the same individual (ie, multiorgan multimorbidity): 2·03 (1·51–2·74) for the arterial age gap, 1·78 (1·48–2·14) for the kidney age gap, 1·52 (1·38–1·68) for the heart age gap, 1·52 (1·12–2·06) for the brain age gap, 1·43 (1·16–1·78) for the pancreas age gap, 1·37 (1·17–1·61) for the lung age gap, 1·36 (1·26–1·46) for the immune system age gap, and 1·30 (1·18–1·42) for the liver age gap.</div></div><div><h3>Interpretation</h3><div>Advanced proteomic organ ageing is associated with the long-term risk of age-related diseases. In most cases, faster ageing of a specific organ increases susceptibility to morbidity affecting multiple organs.</div></div><div><h3>Funding</h3><div>Wellcome Trust, UK Medical Research Council, National Institute for Aging, Academy of Finland.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e195-e204"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Passive sensing at scale to transform understanding of poor mental health","authors":"Aiden Doherty ,&nbsp;Sandra Bucci ,&nbsp;Alexandra Kenny ,&nbsp;Roman Kotov ,&nbsp;Gosia Lipinska ,&nbsp;Laura Ospina-Pinillos ,&nbsp;Katharina Schultebraucks","doi":"10.1016/j.landig.2025.01.002","DOIUrl":"10.1016/j.landig.2025.01.002","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e172-e174"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}