Lancet Digital Health最新文献

{"title":"Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis","authors":"Zacharias V Fisches MSc ,&nbsp;Michael Ball ScB ,&nbsp;Trasias Mukama PhD ,&nbsp;Vilim Štih PhD ,&nbsp;Nicholas R Payne PhD ,&nbsp;Sarah E Hickman PhD ,&nbsp;Prof Fiona J Gilbert PhD ,&nbsp;Stefan Bunk MSc ,&nbsp;Christian Leibig PhD","doi":"10.1016/S2589-7500(24)00173-0","DOIUrl":"10.1016/S2589-7500(24)00173-0","url":null,"abstract":"<div><h3>Background</h3><div>Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared programme-level performance metrics of seven AI integration strategies.</div></div><div><h3>Methods</h3><div>We performed a retrospective comparative evaluation of seven strategies for integrating AI into mammography screening using datasets generated from screening programmes in Germany (n=1 657 068), the UK (n=223 603) and Sweden (n=22 779). The commercially available AI model used was Vara version 2.10, trained from scratch on German data. We simulated the performance of each strategy in terms of cancer detection rate (CDR), recall rate, and workload reduction, and compared the metrics with those of the screening programmes. We also assessed the distribution of the stages and grades of the cancers detected by each strategy and the AI model's ability to correctly localise those cancers.</div></div><div><h3>Findings</h3><div>Compared with the German screening programme (CDR 6·32 per 1000 examinations, recall rate 4·11 per 100 examinations), replacement of both readers (standalone AI strategy) achieved a non-inferior CDR of 6·37 (95% CI 6·10–6·64) at a recall rate of 3·80 (95% CI 3·67–3·93), whereas single reader replacement achieved a CDR of 6·49 (6·31–6·67), a recall rate of 4·01 (3·92–4·10), and a 49% workload reduction. Programme-level decision referral achieved a CDR of 6·85 (6·61–7·11), a recall rate of 3·55 (3·43–3·68), and an 84% workload reduction. Compared with the UK programme CDR of 8·19, the reader-level, programme-level, and deferral to single reader strategies achieved CDRs of 8·24 (7·82–8·71), 8·59 (8·12–9·06), and 8·28 (7·86–8·71), without increasing recall and while reducing workload by 37%, 81%, and 95%, respectively. On the Swedish dataset, programme-level decision referral increased the CDR by 17·7% without increasing recall and while reducing reading workload by 92%.</div></div><div><h3>Interpretation</h3><div>The decision referral strategies offered the largest improvements in cancer detection rates and reduction in recall rates, and all strategies except normal triaging showed potential to improve screening metrics.</div></div><div><h3>Funding</h3><div>Vara.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e803-e814"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510628","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":"Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study","authors":"Arunashis Sau PhD ,&nbsp;Libor Pastika MBBS ,&nbsp;Ewa Sieliwonczyk PhD ,&nbsp;Konstantinos Patlatzoglou PhD ,&nbsp;Antônio H Ribeiro PhD ,&nbsp;Kathryn A McGurk PhD ,&nbsp;Boroumand Zeidaabadi BSc ,&nbsp;Henry Zhang BSc ,&nbsp;Krzysztof Macierzanka BSc ,&nbsp;Prof Danilo Mandic PhD ,&nbsp;Prof Ester Sabino MD ,&nbsp;Luana Giatti PhD ,&nbsp;Prof Sandhi M Barreto PhD ,&nbsp;Lidyane do Valle Camelo PhD ,&nbsp;Prof Ioanna Tzoulaki PhD ,&nbsp;Prof Declan P O'Regan PhD ,&nbsp;Prof Nicholas S Peters MD ,&nbsp;Prof James S Ware PhD ,&nbsp;Prof Antonio Luiz P Ribeiro PhD ,&nbsp;Daniel B Kramer MD ,&nbsp;Fu Siong Ng PhD","doi":"10.1016/S2589-7500(24)00172-9","DOIUrl":"10.1016/S2589-7500(24)00172-9","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.</div></div><div><h3>Methods</h3><div>The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients.</div></div><div><h3>Findings</h3><div>AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773–0·776; C-indices on external validation datasets 0·638–0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756–0·763; UKB C-index 0·719, 95% CI 0·635–0·803), future atherosclerotic cardiovascular disease (0·696, 0·694–0·698; 0·643, 0·624–0·662), and future heart failure (0·787, 0·785–0·789; 0·768, 0·733–0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome.</div></div><div><h3>Interpretation</h3><div>AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation.</div></div><div><h3>Funding</h3><div>British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e791-e802"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510622","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":"Fairly evaluating the performance of normative models","authors":"Andre Marquand ,&nbsp;Saige Rutherford ,&nbsp;Richard Dinga","doi":"10.1016/S2589-7500(24)00200-0","DOIUrl":"10.1016/S2589-7500(24)00200-0","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Page e775"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510625","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":"Fairly evaluating the performance of normative models – Authors' reply","authors":"Ruiyang Ge ,&nbsp;Yuetong Yu ,&nbsp;Denghuang Zhan ,&nbsp;Sophia Frangou","doi":"10.1016/S2589-7500(24)00199-7","DOIUrl":"10.1016/S2589-7500(24)00199-7","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Page e776"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510624","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":"Lifting the veil on health datasets","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00221-8","DOIUrl":"10.1016/S2589-7500(24)00221-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Page e772"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510626","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":"Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge","authors":"Jun Ma PhD ,&nbsp;Yao Zhang PhD ,&nbsp;Song Gu MSc ,&nbsp;Cheng Ge MSc ,&nbsp;Shihao Mae BSc ,&nbsp;Adamo Young MSc ,&nbsp;Cheng Zhu PhD ,&nbsp;Prof Xin Yang PhD ,&nbsp;Prof Kangkang Meng PhD ,&nbsp;Ziyan Huang BSc ,&nbsp;Fan Zhang MSc ,&nbsp;Yuanke Pan MSc ,&nbsp;Shoujin Huang BSc ,&nbsp;Jiacheng Wang PhD ,&nbsp;Mingze Sun PhD ,&nbsp;Prof Rongguo Zhang PhD ,&nbsp;Dengqiang Jia PhD ,&nbsp;Jae Won Choi MD ,&nbsp;Natália Alves MSc ,&nbsp;Bram de Wilde PhD ,&nbsp;Prof Bo Wang PhD","doi":"10.1016/S2589-7500(24)00154-7","DOIUrl":"10.1016/S2589-7500(24)00154-7","url":null,"abstract":"<div><div>Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups. We then independently validated that deep learning algorithms achieved a median dice similarity coefficient (DSC) of 90·0% (IQR 87·4–91·3%) by use of 50 labelled images and 2000 unlabelled images, which can substantially reduce manual annotation costs. The best-performing algorithms successfully generalised to holdout external validation sets, achieving a median DSC of 89·4% (85·2–91·3%), 90·0% (84·3–93·0%), and 88·5% (80·9–91·9%) on North American, European, and Asian cohorts, respectively. These algorithms show the potential to use unlabelled data to boost performance and alleviate annotation shortages for modern artificial intelligence models.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e815-e826"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510629","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":"Effect of the i2TransHealth e-health intervention on psychological distress among transgender and gender diverse adults from remote areas in Germany: a randomised controlled trial","authors":"Timo O Nieder PhD ,&nbsp;Janis Renner MSc ,&nbsp;Susanne Sehner MSc ,&nbsp;Amra Pepić PhD ,&nbsp;Prof Antonia Zapf PhD ,&nbsp;Martin Lambert MD ,&nbsp;Prof Peer Briken MD ,&nbsp;Arne Dekker PhD","doi":"10.1016/S2589-7500(24)00192-4","DOIUrl":"10.1016/S2589-7500(24)00192-4","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Transgender and gender diverse (TGD) people in remote areas face challenges accessing health-care services, including mental health care and gender-affirming medical treatment, which can be associated with psychological distress. In this study, we aimed to evaluate the effectiveness of a 4-month TGD-informed e-health intervention to improve psychological distress among TGD people from remote areas in northern Germany.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;In a randomised controlled trial done at a single centre in Germany, adults (aged ≥18 years) who met criteria for gender incongruence or gender dysphoria and who lived at least 50 km outside of Hamburg in one of the northern German federal states were recruited and randomly assigned (1:1) to i&lt;sup&gt;2&lt;/sup&gt;TransHealth intervention or a wait list control group. Randomisation was performed with the use of a computer-based code. Due to the nature of the intervention, study participants and clinical staff were aware of treatment allocation, but researchers responsible for data analysis were masked to allocation groups. Study participants in the intervention group (service users) started the i&lt;sup&gt;2&lt;/sup&gt;TransHealth intervention immediately after completing the baseline survey after enrolment. Participants assigned to the control group waited 4 months before they were able to access i&lt;sup&gt;2&lt;/sup&gt;TransHealth services or regular care. The primary outcome was difference in the Brief Symptom Inventory (BSI)-18 summary score between baseline and 4 months, assessed using a linear model analysis. The primary outcome was assessed in the intention-to-treat (ITT) population, which included all randomly assigned participants. The trial was registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT04290286&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between May 12, 2020, and May 2, 2022, 177 TGD people were assessed for eligibility, of whom 174 were included in the ITT population (n=90 in the intervention group, n=84 in the control group). Six participants did not provide data for the primary outcome at 4 months, and thus 168 people were included in the analysis population (88 participants in the intervention group and 80 participants in the control group). At 4 months, in the intervention group, the adjusted mean change in BSI-18 from baseline was –0·65 (95% CI –2·25 to 0·96; p=0·43) compared with 2·34 (0·65 to 4·02; p=0·0069) in the control group. Linear model analysis identified a significant difference at 4 months between the groups with regard to change in BSI-18 summary scores from baseline (between-group difference –2·98 [95% CI –5·31 to –0·65]; p=0·012). Adverse events were rare: there were two suicide attempts and one participant was admitted to hospital in the intervention group, and in the control group, there was one case of self-harm and one case of self-harm followed by hospital admission.&lt;/div&gt;&lt;/d","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e883-e893"},"PeriodicalIF":23.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478075","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 2024; published online Sept 17. https://doi.org/10.1016/S2589-7500(24)00143-2","authors":"","doi":"10.1016/S2589-7500(24)00220-6","DOIUrl":"10.1016/S2589-7500(24)00220-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Page e777"},"PeriodicalIF":23.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394367","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":"Combating medical misinformation and rebuilding trust in the USA","authors":"Clara E Tandar ,&nbsp;John C Lin ,&nbsp;Fatima Cody Stanford","doi":"10.1016/S2589-7500(24)00197-3","DOIUrl":"10.1016/S2589-7500(24)00197-3","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e773-e774"},"PeriodicalIF":23.8,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394366","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":"Emotional competence self-help mobile phone app versus cognitive behavioural self-help app versus self-monitoring app to promote mental wellbeing in healthy young adults (ECoWeB PROMOTE): an international, multicentre, parallel, open-label, randomised controlled trial","authors":"Prof Edward R Watkins PhD ,&nbsp;Fiona C Warren PhD ,&nbsp;Alexandra Newbold PhD ,&nbsp;Prof Claire Hulme PhD ,&nbsp;Timothy Cranston BSc ,&nbsp;Benjamin Aas PhD ,&nbsp;Holly Bear PhD ,&nbsp;Prof Cristina Botella PhD ,&nbsp;Felix Burkhardt PhD ,&nbsp;Prof Thomas Ehring PhD ,&nbsp;Prof Mina Fazel PhD ,&nbsp;Prof Johnny R J Fontaine PhD ,&nbsp;Mads Frost PhD ,&nbsp;Prof Azucena Garcia-Palacios PhD ,&nbsp;Ellen Greimel PhD ,&nbsp;Christiane Hößle PhD ,&nbsp;Arpine Hovasapian PhD ,&nbsp;Veerle E I Huyghe BSc ,&nbsp;Prof Kostas Karpouzis PhD ,&nbsp;Johanna Löchner PhD ,&nbsp;Prof Rod S Taylor PhD","doi":"10.1016/S2589-7500(24)00149-3","DOIUrl":"10.1016/S2589-7500(24)00149-3","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16–22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick–Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT04148508&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, and is closed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS –0·21 [95% CI –1·08 to 0·66]) or the self-monitoring app (0·32 [–0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [–0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related r","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e904-e913"},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378382","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}