Agathe Zecevic, Laurence Jackson, Xinyue Zhang, Polychronis Pavlidis, Jason Dunn, Nigel Trudgill, Shahd Ahmed, Pierfrancesco Visaggi, Zanil YoonusNizar, Angus Roberts, Sebastian S. Zeki
{"title":"Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool","authors":"Agathe Zecevic, Laurence Jackson, Xinyue Zhang, Polychronis Pavlidis, Jason Dunn, Nigel Trudgill, Shahd Ahmed, Pierfrancesco Visaggi, Zanil YoonusNizar, Angus Roberts, Sebastian S. Zeki","doi":"10.1038/s41746-024-01302-6","DOIUrl":"10.1038/s41746-024-01302-6","url":null,"abstract":"Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett’s oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy’s and St Thomas’ Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King’s College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists’ decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-9"},"PeriodicalIF":12.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01302-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594767","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}
Gloria Y. K. Kim, Rea Rostosky, Franziska K. Bishop, Kelly Watson, Priya Prahalad, Aishwari Vaidya, Sharon Lee, Alexander Diana, Clint Beacock, Brian Chu, Ginny Yadav, Kaylin Rochford, Carissa Carter, Johannes O. Ferstad, Erica Pang, Jamie Kurtzig, Brandon Arbiter, Howard Look, Ramesh Johari, David M. Maahs, David Scheinker
{"title":"The adaptation of a single institution diabetes care platform into a nationally available turnkey solution","authors":"Gloria Y. K. Kim, Rea Rostosky, Franziska K. Bishop, Kelly Watson, Priya Prahalad, Aishwari Vaidya, Sharon Lee, Alexander Diana, Clint Beacock, Brian Chu, Ginny Yadav, Kaylin Rochford, Carissa Carter, Johannes O. Ferstad, Erica Pang, Jamie Kurtzig, Brandon Arbiter, Howard Look, Ramesh Johari, David M. Maahs, David Scheinker","doi":"10.1038/s41746-024-01319-x","DOIUrl":"10.1038/s41746-024-01319-x","url":null,"abstract":"Digital decision support and remote patient monitoring may improve outcomes and efficiency, but rarely scale beyond a single institution. Over the last 5 years, the platform Timely Interventions for Diabetes Excellence (TIDE) has been associated with reduced care provider screen time and improved, equitable type 1 diabetes care and outcomes for 268 patients in a heterogeneous population as part of the Teamwork, Targets, Technology, and Tight Control (4T) Study (NCT03968055, NCT04336969). Previous efforts to deploy TIDE at other institutions continue to face delays. In partnership with the diabetes technology non-profit, Tidepool, we developed Tidepool-TIDE, a clinic-agnostic, turnkey solution available to any clinic in the United States. We present how we overcame common technical and operational barriers specific to scaling digital health technology from one site to many. The concepts described are broadly applicable for institutions interested in facilitating broader adoption of digital technology for population-level management of chronic health conditions.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01319-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588541","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}
Morteza Naghavi, Anthony P. Reeves, Kyle Atlas, Chenyu Zhang, Thomas Atlas, Claudia I. Henschke, David F. Yankelevitz, Matthew J. Budoff, Dong Li, Sion K. Roy, Khurram Nasir, Sabee Molloi, Zahi Fayad, Michael V. McConnell, Ioannis Kakadiaris, David J. Maron, Jagat Narula, Kim Williams, Prediman K. Shah, Daniel Levy, Nathan D. Wong
{"title":"Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction","authors":"Morteza Naghavi, Anthony P. Reeves, Kyle Atlas, Chenyu Zhang, Thomas Atlas, Claudia I. Henschke, David F. Yankelevitz, Matthew J. Budoff, Dong Li, Sion K. Roy, Khurram Nasir, Sabee Molloi, Zahi Fayad, Michael V. McConnell, Ioannis Kakadiaris, David J. Maron, Jagat Narula, Kim Williams, Prediman K. Shah, Daniel Levy, Nathan D. Wong","doi":"10.1038/s41746-024-01308-0","DOIUrl":"10.1038/s41746-024-01308-0","url":null,"abstract":"Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1–100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-9"},"PeriodicalIF":12.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583449","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":"Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach","authors":"Ting Li, Xi Chen, Weida Tong","doi":"10.1038/s41746-024-01317-z","DOIUrl":"10.1038/s41746-024-01317-z","url":null,"abstract":"Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI model using a generative adversarial network (GAN) method to facilitate the bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment. TransTox demonstrates robust performance, validated across independent datasets and laboratories. First, the concordance between real experimental data and synthetic data generated by TransTox was demonstrated in characterizing toxicity mechanisms compared to real experimental settings. Second, TransTox proved valuable in gene expression predictive models, where synthetic data could be used to develop gene expression predictive models or serve as “digital twins” for diagnostic applications. The TransTox approach holds the potential for multi-organ toxicity assessment with AI and advancing the field of precision toxicology.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-14"},"PeriodicalIF":12.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583462","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}
Daewoon Seong, Euimin Lee, Yoonseok Kim, Che Gyem Yae, JeongMun Choi, Hong Kyun Kim, Mansik Jeon, Jeehyun Kim
{"title":"Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography","authors":"Daewoon Seong, Euimin Lee, Yoonseok Kim, Che Gyem Yae, JeongMun Choi, Hong Kyun Kim, Mansik Jeon, Jeehyun Kim","doi":"10.1038/s41746-024-01305-3","DOIUrl":"10.1038/s41746-024-01305-3","url":null,"abstract":"Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01305-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579796","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":"The R.O.A.D. to precision medicine","authors":"Dimitris Bertsimas, Angelos Georgios Koulouras, Georgios Antonios Margonis","doi":"10.1038/s41746-024-01291-6","DOIUrl":"10.1038/s41746-024-01291-6","url":null,"abstract":"We propose a novel framework that addresses the deficiencies of Randomized clinical trial data subgroup analysis while it transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data through a two-step process that adjusts predicted outcomes under treatment. These adjusted predictions train decision trees, optimizing treatment assignments for patient subgroups based on their characteristics, enabling intuitive treatment recommendations. Implementing this framework on gastrointestinal stromal tumors (GIST) data, including genetic sub-cohorts, showed that our tree recommendations outperformed current guidelines in an external cohort. Furthermore, we extended the application of this framework to RCT data from patients with extremity sarcomas. Despite initial trial indications of universal treatment necessity, our framework identified a subset of patients who may not require treatment. Once again, we successfully validated our recommendations in an external cohort.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-15"},"PeriodicalIF":12.4,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01291-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566102","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}
Damien Keng Ming, John Daniels, Ho Quang Chanh, Stefan Karolcik, Bernard Hernandez, Vasileios Manginas, Van Hao Nguyen, Quang Huy Nguyen, Tu Qui Phan, Thi Hue Tai Luong, Huynh Trung Trieu, Alison Helen Holmes, Vinh Tho Phan, Pantelis Georgiou, Sophie Yacoub, On behalf of the VITAL consortium
{"title":"Predicting deterioration in dengue using a low cost wearable for continuous clinical monitoring","authors":"Damien Keng Ming, John Daniels, Ho Quang Chanh, Stefan Karolcik, Bernard Hernandez, Vasileios Manginas, Van Hao Nguyen, Quang Huy Nguyen, Tu Qui Phan, Thi Hue Tai Luong, Huynh Trung Trieu, Alison Helen Holmes, Vinh Tho Phan, Pantelis Georgiou, Sophie Yacoub, On behalf of the VITAL consortium","doi":"10.1038/s41746-024-01304-4","DOIUrl":"10.1038/s41746-024-01304-4","url":null,"abstract":"Close vital signs monitoring is crucial for the clinical management of patients with dengue. We investigated performance of a non-invasive wearable utilising photoplethysmography (PPG), to provide real-time risk prediction in hospitalised individuals. We performed a prospective observational clinical study in Vietnam between January 2020 and October 2022: 153 patients were included in analyses, providing 1353 h of PPG data. Using a multi-modal transformer approach, 10-min PPG waveform segments and basic clinical data (age, sex, clinical features on admission) were used as features to continuously forecast clinical state 2 h ahead. Prediction of low-risk states (17,939/80,843; 22.1%), defined by NEWS2 and mSOFA < 6, was associated with an area under the precision-recall curve of 0.67 and an area under the receiver operator curve of 0.83. Implementation of such interventions could provide cost-effective triage and clinical care in dengue, offering opportunities for safe ambulatory patient management.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-8"},"PeriodicalIF":12.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01304-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563245","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}
Whitney P. Underwood, Meghan G. Michalski, Catherine P. Lee, Gina A. Fickera, Su S. Chun, Stefan E. Eng, Lydia Y. Liu, Brandon L. Tsai, Chaya S. Moskowitz, Jessica A. Lavery, Kimberly J. Van Zee, Ginger J. Gardner, Jennifer J. Mueller, Chau T. Dang, Behfar Ehdaie, Vincent P. Laudone, James A. Eastham, Jessica M. Scott, Paul C. Boutros, Lee W. Jones
{"title":"A digital, decentralized trial of exercise therapy in patients with cancer","authors":"Whitney P. Underwood, Meghan G. Michalski, Catherine P. Lee, Gina A. Fickera, Su S. Chun, Stefan E. Eng, Lydia Y. Liu, Brandon L. Tsai, Chaya S. Moskowitz, Jessica A. Lavery, Kimberly J. Van Zee, Ginger J. Gardner, Jennifer J. Mueller, Chau T. Dang, Behfar Ehdaie, Vincent P. Laudone, James A. Eastham, Jessica M. Scott, Paul C. Boutros, Lee W. Jones","doi":"10.1038/s41746-024-01288-1","DOIUrl":"10.1038/s41746-024-01288-1","url":null,"abstract":"We developed and evaluated the Digital Platform for Exercise (DPEx): a decentralized, patient-centric approach designed to enhance all aspects of clinical investigation of exercise therapy. DPEx integrated provision of a treadmill with telemedicine and remote biospecimen collection permitting all study procedures to be conducted in patient’s homes. Linked health biodevices enabled high-resolution monitoring of lifestyle and physiological response. Here we describe the rationale and development of DPEx as well as feasibility evaluation in three different cohorts of patients with cancer: a phase 0a development study among three women with post-treatment primary breast cancer; a phase 0b proof-of-concept trial of neoadjuvant exercise therapy in 13 patients with untreated solid tumors; and a phase 1a level-finding trial of neoadjuvant exercise therapy in 53 men with localized prostate cancer. Collectively, our study demonstrates the utility of a fully digital, decentralized approach to conduct clinical trials of exercise therapy in a clinical population.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01288-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519437","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}
Shashi Gupta, Aditya Basu, Mauro Nievas, Jerrin Thomas, Nathan Wolfrath, Adhitya Ramamurthi, Bradley Taylor, Anai N. Kothari, Regina Schwind, Therica M. Miller, Sorena Nadaf-Rahrov, Yanshan Wang, Hrituraj Singh
{"title":"PRISM: Patient Records Interpretation for Semantic clinical trial Matching system using large language models","authors":"Shashi Gupta, Aditya Basu, Mauro Nievas, Jerrin Thomas, Nathan Wolfrath, Adhitya Ramamurthi, Bradley Taylor, Anai N. Kothari, Regina Schwind, Therica M. Miller, Sorena Nadaf-Rahrov, Yanshan Wang, Hrituraj Singh","doi":"10.1038/s41746-024-01274-7","DOIUrl":"10.1038/s41746-024-01274-7","url":null,"abstract":"Clinical trial matching is the task of identifying trials for which patients may be eligible. Typically, this task is labor-intensive and requires detailed verification of patient electronic health records (EHRs) against the stringent inclusion and exclusion criteria of clinical trials. This process also results in many patients missing out on potential therapeutic options. Recent advancements in Large Language Models (LLMs) have made automating patient-trial matching possible, as shown in multiple concurrent research studies. However, the current approaches are confined to constrained, often synthetic, datasets that do not adequately mirror the complexities encountered in real-world medical data. In this study, we present an end-to-end large-scale empirical evaluation of a clinical trial matching system and validate it using real-world EHRs. We perform comprehensive experiments with proprietary LLMs and our custom fine-tuned model called OncoLLM and show that OncoLLM outperforms GPT-3.5 and matches the performance of qualified medical doctors for clinical trial matching.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142522550","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}
James Liley, Gergo Bohner, Samuel R. Emerson, Bilal A. Mateen, Katie Borland, David Carr, Scott Heald, Samuel D. Oduro, Jill Ireland, Keith Moffat, Rachel Porteous, Stephen Riddell, Simon Rogers, Ioanna Thoma, Nathan Cunningham, Chris Holmes, Katrina Payne, Sebastian J. Vollmer, Catalina A. Vallejos, Louis J. M. Aslett
{"title":"Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland","authors":"James Liley, Gergo Bohner, Samuel R. Emerson, Bilal A. Mateen, Katie Borland, David Carr, Scott Heald, Samuel D. Oduro, Jill Ireland, Keith Moffat, Rachel Porteous, Stephen Riddell, Simon Rogers, Ioanna Thoma, Nathan Cunningham, Chris Holmes, Katrina Payne, Sebastian J. Vollmer, Catalina A. Vallejos, Louis J. M. Aslett","doi":"10.1038/s41746-024-01310-6","DOIUrl":"10.1038/s41746-024-01310-6","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-1"},"PeriodicalIF":12.4,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11513153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142504872","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}
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