Lancet Digital Health最新文献

{"title":"Using digital health to address antimicrobial resistance","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00251-6","DOIUrl":"10.1016/S2589-7500(24)00251-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e879"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639926","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":"Using digital health technologies to optimise antimicrobial use globally","authors":"Timothy M Rawson PhD ,&nbsp;Nina Zhu PhD ,&nbsp;Ronald Galiwango PhD ,&nbsp;Derek Cocker PhD ,&nbsp;Mohammad Shahidul Islam PhD ,&nbsp;Ashleigh Myall PhD ,&nbsp;Vasin Vasikasin MD ,&nbsp;Richard Wilson MPharm ,&nbsp;Prof Nusrat Shafiq PhD ,&nbsp;Prof Shampa Das PhD ,&nbsp;Prof Alison H Holmes MD","doi":"10.1016/S2589-7500(24)00198-5","DOIUrl":"10.1016/S2589-7500(24)00198-5","url":null,"abstract":"<div><div>Digital health technology (DHT) describes tools and devices that generate or process health data. The application of DHTs could improve the diagnosis, treatment, and surveillance of bacterial infection and the prevention of antimicrobial resistance (AMR). DHTs to optimise antimicrobial use are rapidly being developed. To support the global adoption of DHTs and the opportunities offered to optimise antimicrobial use consensus is needed on what data are required to support antimicrobial decision making. This Series paper will explore bacterial AMR in humans and the need to optimise antimicrobial use in response to this global threat. It will also describe state-of-the-art DHTs to optimise antimicrobial prescribing in high-income and low-income and middle-income countries, and consider what fundamental data are ideally required for and from such technologies to support optimised antimicrobial use.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e914-e925"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639910","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":"Innovative diagnostic technologies: navigating regulatory frameworks through advances, challenges, and future prospects","authors":"Jesus Rodriguez-Manzano PhD ,&nbsp;Sumithra Subramaniam PhD ,&nbsp;Chibuzor Uchea PhD ,&nbsp;Katarzyna M Szostak-Lipowicz PhD ,&nbsp;Jane Freeman PhD ,&nbsp;Marcus Rauch PhD ,&nbsp;Prof Halidou Tinto PhD ,&nbsp;Prof Heather J Zar MD ,&nbsp;Prof Umberto D'Alessandro MD ,&nbsp;Prof Alison H Holmes MD ,&nbsp;Prof Gordon A Awandare PhD","doi":"10.1016/S2589-7500(24)00242-5","DOIUrl":"10.1016/S2589-7500(24)00242-5","url":null,"abstract":"<div><div>Diagnostic tools are key to guiding patient management and informing public health policies to control infectious diseases. However, many diseases still do not have effective diagnostics and much of the global population faces restricted access to reliable, affordable testing. This limitation underscores the urgent need for innovation to enhance diagnostic availability and effectiveness. Developing diagnostics presents distinct challenges, especially for innovators and regulators. Unlike medicines, regulatory pathways for diagnostics are often less defined and more complex due to their diverse risk profiles and wide range of products. These challenges are amplified in low-income and middle-income countries, which often do not have regulatory frameworks for this specific purpose. In the UK, initiatives aim to support innovation by providing clearer regulatory pathways and ensuring that diagnostics are safe and effective. Regulators are also collaborating internationally to expedite diagnostics for high-need regions. Harmonised standards, regulatory frameworks, and approval processes are essential to ensure consistent quality and safety across regions and facilitate faster development and global access. This Series paper explores the regulatory challenges in infectious disease and antimicrobial resistance diagnostics, focusing on the UK's response and the broader global efforts to address these issues.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e934-e943"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639902","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":"COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys","authors":"Sumali Bajaj SM ,&nbsp;Siyu Chen DPhil ,&nbsp;Richard Creswell DPhil ,&nbsp;Reshania Naidoo MD ,&nbsp;Joseph L-H Tsui MSc ,&nbsp;Olumide Kolade BSc ,&nbsp;George Nicholson DPhil ,&nbsp;Brieuc Lehmann PhD ,&nbsp;James A Hay PhD ,&nbsp;Prof Moritz U G Kraemer DPhil ,&nbsp;Ricardo Aguas PhD ,&nbsp;Prof Christl A Donnelly ScD ,&nbsp;Tom Fowler FFPH ,&nbsp;Prof Susan Hopkins FMedSci ,&nbsp;Liberty Cantrell MSc ,&nbsp;Prabin Dahal DPhil ,&nbsp;Prof Lisa J White PhD ,&nbsp;Kasia Stepniewska PhD ,&nbsp;Merryn Voysey DPhil ,&nbsp;Ben Lambert DPhil ,&nbsp;Lisa J White","doi":"10.1016/S2589-7500(24)00169-9","DOIUrl":"10.1016/S2589-7500(24)00169-9","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Understanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour during an infectious disease outbreak can help to protect vulnerable populations and guide equity-driven interventions. The COVID-19 pandemic probably exerted different stresses on individuals in different sociodemographic groups and ensuring fair access to and usage of COVID-19 tests was a crucial element of England's testing programme. We aimed to investigate the relationship between sociodemographic factors and COVID-19 testing behaviours in England during the COVID-19 pandemic.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We did a population-based study of COVID-19 testing behaviours with mass COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from Oct 1, 2020, to March 30, 2022. We used mass testing data for lateral flow device (LFD; data for approximately 290 million tests performed and reported) and PCR (data for approximately 107 million tests performed and returned from the laboratory) tests made available for the general public and provided by date and self-reported age and ethnicity at the lower tier local authority (LTLA) level. We also used publicly available data on mean population size estimates for individual LTLAs, and data on ethnic groups, age groups, and deprivation indices for LTLAs. We did not have access to REACT-1 or ONS-CIS prevalence data disaggregated by sex or gender. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by both self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. With confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability subsequent to reporting a positive LFD for PCR tests by sociodemographic groups. We also estimated the daily incidence, allowing us to calculate the fraction of cases captured by the testing programme.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;From March, 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per capita as individuals in the least deprived areas (median ratio 0·50 [IQR 0·44–0·54]). During the period October, 2020, to June, 2021, PCR testing patterns showed the opposite trend, with individuals in the most deprived areas performing almost double the number of PCR tests per capita than those in the least deprived areas (1·8 [1·7–1·9]). Infection prevalences in Asian or Asian British individuals were considerably higher than those of other ethnic groups during the alpha (B.1.1.7) and omicron (B.1.1.529","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e778-e790"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510623","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":"Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development—a systematic review","authors":"Joseph E Alderman MB ChB ,&nbsp;Maria Charalambides MB ChB ,&nbsp;Gagandeep Sachdeva MB ChB ,&nbsp;Elinor Laws MB BCh ,&nbsp;Joanne Palmer PhD ,&nbsp;Elsa Lee MSc ,&nbsp;Vaishnavi Menon MB ChB ,&nbsp;Qasim Malik MB ChB ,&nbsp;Sonam Vadera MB BS ,&nbsp;Prof Melanie Calvert PhD ,&nbsp;Marzyeh Ghassemi PhD ,&nbsp;Melissa D McCradden PhD ,&nbsp;Johan Ordish MA ,&nbsp;Bilal Mateen MBBS ,&nbsp;Prof Charlotte Summers PhD ,&nbsp;Jacqui Gath ,&nbsp;Rubeta N Matin PhD ,&nbsp;Prof Alastair K Denniston PhD ,&nbsp;Xiaoxuan Liu PhD","doi":"10.1016/S2589-7500(24)00146-8","DOIUrl":"10.1016/S2589-7500(24)00146-8","url":null,"abstract":"<div><div>During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals’ country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e827-e847"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510627","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":"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}