Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial
Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunc...
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creator | Adedinsewo, Demilade A. Morales-Lara, Andrea Carolina Afolabi, Bosede B. Kushimo, Oyewole A. Mbakwem, Amam C. Ibiyemi, Kehinde F. Ogunmodede, James Ayodele Raji, Hadijat Olaide Ringim, Sadiq H. Habib, Abdullahi A. Hamza, Sabiu M. Ogah, Okechukwu S. Obajimi, Gbolahan Saanu, Olugbenga Oluseun Jagun, Olusoji E. Inofomoh, Francisca O. Adeolu, Temitope Karaye, Kamilu M. Gaya, Sule A. Alfa, Isiaka Yohanna, Cynthia Venkatachalam, K. L. Dugan, Jennifer Yao, Xiaoxi Sledge, Hanna J. Johnson, Patrick W. Wieczorek, Mikolaj A. Attia, Zachi I. Phillips, Sabrina D. Yamani, Mohamad H. Tobah, Yvonne Butler Rose, Carl H. Sharpe, Emily E. Lopez-Jimenez, Francisco Friedman, Paul A. Noseworthy, Peter A. Carter, Rickey E. |
description | Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05–4.27;
P
= 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85–3.62;
P
= 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration:
NCT05438576
In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions. |
doi_str_mv | 10.1038/s41591-024-03243-9 |
format | Article |
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P
= 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85–3.62;
P
= 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration:
NCT05438576
In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.</description><identifier>ISSN: 1078-8956</identifier><identifier>ISSN: 1546-170X</identifier><identifier>EISSN: 1546-170X</identifier><identifier>DOI: 10.1038/s41591-024-03243-9</identifier><identifier>PMID: 39223284</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>692/308/174 ; 692/308/409 ; 692/699/75/74 ; 692/700/478/2772 ; Adult ; Artificial Intelligence ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Cardiomyopathies - diagnosis ; Cardiomyopathies - diagnostic imaging ; Cardiomyopathy ; Clinical trials ; Diagnosis ; Echocardiography ; EKG ; Electrocardiography ; Female ; Heart ; Hospitals ; Humans ; Infectious Diseases ; Intervention ; Mass Screening - methods ; Medical instruments ; Metabolic Diseases ; Molecular Medicine ; Neurosciences ; Nigeria - epidemiology ; Patients ; Postpartum ; Postpartum period ; Pregnancy ; Pregnancy Complications, Cardiovascular - diagnosis ; Subgroups ; Ventricular Dysfunction, Left - diagnosis ; Ventricular Dysfunction, Left - diagnostic imaging</subject><ispartof>Nature medicine, 2024-10, Vol.30 (10), p.2897-2906</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). 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L.</creatorcontrib><creatorcontrib>Dugan, Jennifer</creatorcontrib><creatorcontrib>Yao, Xiaoxi</creatorcontrib><creatorcontrib>Sledge, Hanna J.</creatorcontrib><creatorcontrib>Johnson, Patrick W.</creatorcontrib><creatorcontrib>Wieczorek, Mikolaj A.</creatorcontrib><creatorcontrib>Attia, Zachi I.</creatorcontrib><creatorcontrib>Phillips, Sabrina D.</creatorcontrib><creatorcontrib>Yamani, Mohamad H.</creatorcontrib><creatorcontrib>Tobah, Yvonne Butler</creatorcontrib><creatorcontrib>Rose, Carl H.</creatorcontrib><creatorcontrib>Sharpe, Emily E.</creatorcontrib><creatorcontrib>Lopez-Jimenez, Francisco</creatorcontrib><creatorcontrib>Friedman, Paul A.</creatorcontrib><creatorcontrib>Noseworthy, Peter A.</creatorcontrib><creatorcontrib>Carter, Rickey E.</creatorcontrib><creatorcontrib>SPEC-AI Nigeria Investigators</creatorcontrib><creatorcontrib>on behalf of the SPEC-AI Nigeria Investigators</creatorcontrib><title>Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial</title><title>Nature medicine</title><addtitle>Nat Med</addtitle><addtitle>Nat Med</addtitle><description>Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05–4.27;
P
= 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85–3.62;
P
= 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration:
NCT05438576
In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.</description><subject>692/308/174</subject><subject>692/308/409</subject><subject>692/699/75/74</subject><subject>692/700/478/2772</subject><subject>Adult</subject><subject>Artificial Intelligence</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Cardiomyopathies - diagnosis</subject><subject>Cardiomyopathies - diagnostic imaging</subject><subject>Cardiomyopathy</subject><subject>Clinical trials</subject><subject>Diagnosis</subject><subject>Echocardiography</subject><subject>EKG</subject><subject>Electrocardiography</subject><subject>Female</subject><subject>Heart</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infectious Diseases</subject><subject>Intervention</subject><subject>Mass Screening - methods</subject><subject>Medical instruments</subject><subject>Metabolic Diseases</subject><subject>Molecular Medicine</subject><subject>Neurosciences</subject><subject>Nigeria - epidemiology</subject><subject>Patients</subject><subject>Postpartum</subject><subject>Postpartum period</subject><subject>Pregnancy</subject><subject>Pregnancy Complications, Cardiovascular - diagnosis</subject><subject>Subgroups</subject><subject>Ventricular Dysfunction, Left - diagnosis</subject><subject>Ventricular Dysfunction, Left - diagnostic imaging</subject><issn>1078-8956</issn><issn>1546-170X</issn><issn>1546-170X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNp9kctu1jAQhS0EoqXwAiyQJTZsAr7FidmgquImVWIDEjtr4jjpVIkdbAepbHh1XP5SLgtWtjXfOTPjQ8hjzp5zJvsXWfHW8IYJ1TAplGzMHXLMW6Ub3rHPd-uddX3Tm1YfkQc5XzLGJGvNfXIkjRBS9OqYfD9NBSd0CAvFUPyy4OyD83TecfQjzS55HzDMdIqJOkgjxvUqblAu0OcqoRBoHHLxJaGjW9z2BQrG8JIC3RLMa305miCMccVv1dEtGNDVdlUAy0Nyb4Il-0c35wn59Ob1x7N3zfmHt-_PTs8bJ1tdGsF5Z7reCM-MEqOAnulBy27Uo_OuH6TkxgtmJq04KDBKDdJxKabJm0FCL0_Iq4Pvtg-rr6JQEix2S7hCurIR0P5dCXhh5_jVcq76VrSiOjy7cUjxy-5zsStmVz8Mgo97tpIzJjrZ8utmT_9BL-OeQt2vUryrc2qmKyUOlEsx5-Sn22k4s9cB20PAtgZsfwZsTRU9-XOPW8mvRCsgD0CupTD79Lv3f2x_APocs-0</recordid><startdate>202410</startdate><enddate>202410</enddate><creator>Adedinsewo, Demilade A.</creator><creator>Morales-Lara, Andrea Carolina</creator><creator>Afolabi, Bosede B.</creator><creator>Kushimo, Oyewole A.</creator><creator>Mbakwem, Amam C.</creator><creator>Ibiyemi, Kehinde F.</creator><creator>Ogunmodede, James Ayodele</creator><creator>Raji, Hadijat Olaide</creator><creator>Ringim, Sadiq H.</creator><creator>Habib, Abdullahi A.</creator><creator>Hamza, Sabiu M.</creator><creator>Ogah, Okechukwu S.</creator><creator>Obajimi, Gbolahan</creator><creator>Saanu, Olugbenga Oluseun</creator><creator>Jagun, Olusoji E.</creator><creator>Inofomoh, Francisca O.</creator><creator>Adeolu, Temitope</creator><creator>Karaye, Kamilu M.</creator><creator>Gaya, Sule A.</creator><creator>Alfa, Isiaka</creator><creator>Yohanna, Cynthia</creator><creator>Venkatachalam, K. 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L. ; Dugan, Jennifer ; Yao, Xiaoxi ; Sledge, Hanna J. ; Johnson, Patrick W. ; Wieczorek, Mikolaj A. ; Attia, Zachi I. ; Phillips, Sabrina D. ; Yamani, Mohamad H. ; Tobah, Yvonne Butler ; Rose, Carl H. ; Sharpe, Emily E. ; Lopez-Jimenez, Francisco ; Friedman, Paul A. ; Noseworthy, Peter A. ; Carter, Rickey E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-211797892e0942d2a806b637d6dcec8b3319e209f641a4a944b3c132ffe9b3a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>692/308/174</topic><topic>692/308/409</topic><topic>692/699/75/74</topic><topic>692/700/478/2772</topic><topic>Adult</topic><topic>Artificial Intelligence</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer Research</topic><topic>Cardiomyopathies - diagnosis</topic><topic>Cardiomyopathies - diagnostic imaging</topic><topic>Cardiomyopathy</topic><topic>Clinical trials</topic><topic>Diagnosis</topic><topic>Echocardiography</topic><topic>EKG</topic><topic>Electrocardiography</topic><topic>Female</topic><topic>Heart</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Infectious Diseases</topic><topic>Intervention</topic><topic>Mass Screening - methods</topic><topic>Medical instruments</topic><topic>Metabolic Diseases</topic><topic>Molecular Medicine</topic><topic>Neurosciences</topic><topic>Nigeria - epidemiology</topic><topic>Patients</topic><topic>Postpartum</topic><topic>Postpartum period</topic><topic>Pregnancy</topic><topic>Pregnancy Complications, Cardiovascular - diagnosis</topic><topic>Subgroups</topic><topic>Ventricular Dysfunction, Left - diagnosis</topic><topic>Ventricular Dysfunction, Left - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adedinsewo, Demilade A.</creatorcontrib><creatorcontrib>Morales-Lara, Andrea Carolina</creatorcontrib><creatorcontrib>Afolabi, Bosede B.</creatorcontrib><creatorcontrib>Kushimo, Oyewole A.</creatorcontrib><creatorcontrib>Mbakwem, Amam C.</creatorcontrib><creatorcontrib>Ibiyemi, Kehinde F.</creatorcontrib><creatorcontrib>Ogunmodede, James Ayodele</creatorcontrib><creatorcontrib>Raji, Hadijat Olaide</creatorcontrib><creatorcontrib>Ringim, Sadiq H.</creatorcontrib><creatorcontrib>Habib, Abdullahi A.</creatorcontrib><creatorcontrib>Hamza, Sabiu M.</creatorcontrib><creatorcontrib>Ogah, Okechukwu S.</creatorcontrib><creatorcontrib>Obajimi, Gbolahan</creatorcontrib><creatorcontrib>Saanu, Olugbenga Oluseun</creatorcontrib><creatorcontrib>Jagun, Olusoji E.</creatorcontrib><creatorcontrib>Inofomoh, Francisca O.</creatorcontrib><creatorcontrib>Adeolu, Temitope</creatorcontrib><creatorcontrib>Karaye, Kamilu M.</creatorcontrib><creatorcontrib>Gaya, Sule A.</creatorcontrib><creatorcontrib>Alfa, Isiaka</creatorcontrib><creatorcontrib>Yohanna, Cynthia</creatorcontrib><creatorcontrib>Venkatachalam, K. L.</creatorcontrib><creatorcontrib>Dugan, Jennifer</creatorcontrib><creatorcontrib>Yao, Xiaoxi</creatorcontrib><creatorcontrib>Sledge, Hanna J.</creatorcontrib><creatorcontrib>Johnson, Patrick W.</creatorcontrib><creatorcontrib>Wieczorek, Mikolaj A.</creatorcontrib><creatorcontrib>Attia, Zachi I.</creatorcontrib><creatorcontrib>Phillips, Sabrina D.</creatorcontrib><creatorcontrib>Yamani, Mohamad H.</creatorcontrib><creatorcontrib>Tobah, Yvonne Butler</creatorcontrib><creatorcontrib>Rose, Carl H.</creatorcontrib><creatorcontrib>Sharpe, Emily E.</creatorcontrib><creatorcontrib>Lopez-Jimenez, Francisco</creatorcontrib><creatorcontrib>Friedman, Paul A.</creatorcontrib><creatorcontrib>Noseworthy, Peter A.</creatorcontrib><creatorcontrib>Carter, Rickey E.</creatorcontrib><creatorcontrib>SPEC-AI Nigeria Investigators</creatorcontrib><creatorcontrib>on behalf of the SPEC-AI Nigeria Investigators</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nature medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adedinsewo, Demilade A.</au><au>Morales-Lara, Andrea Carolina</au><au>Afolabi, Bosede B.</au><au>Kushimo, Oyewole A.</au><au>Mbakwem, Amam C.</au><au>Ibiyemi, Kehinde F.</au><au>Ogunmodede, James Ayodele</au><au>Raji, Hadijat Olaide</au><au>Ringim, Sadiq H.</au><au>Habib, Abdullahi A.</au><au>Hamza, Sabiu M.</au><au>Ogah, Okechukwu S.</au><au>Obajimi, Gbolahan</au><au>Saanu, Olugbenga Oluseun</au><au>Jagun, Olusoji E.</au><au>Inofomoh, Francisca O.</au><au>Adeolu, Temitope</au><au>Karaye, Kamilu M.</au><au>Gaya, Sule A.</au><au>Alfa, Isiaka</au><au>Yohanna, Cynthia</au><au>Venkatachalam, K. L.</au><au>Dugan, Jennifer</au><au>Yao, Xiaoxi</au><au>Sledge, Hanna J.</au><au>Johnson, Patrick W.</au><au>Wieczorek, Mikolaj A.</au><au>Attia, Zachi I.</au><au>Phillips, Sabrina D.</au><au>Yamani, Mohamad H.</au><au>Tobah, Yvonne Butler</au><au>Rose, Carl H.</au><au>Sharpe, Emily E.</au><au>Lopez-Jimenez, Francisco</au><au>Friedman, Paul A.</au><au>Noseworthy, Peter A.</au><au>Carter, Rickey E.</au><aucorp>SPEC-AI Nigeria Investigators</aucorp><aucorp>on behalf of the SPEC-AI Nigeria Investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial</atitle><jtitle>Nature medicine</jtitle><stitle>Nat Med</stitle><addtitle>Nat Med</addtitle><date>2024-10</date><risdate>2024</risdate><volume>30</volume><issue>10</issue><spage>2897</spage><epage>2906</epage><pages>2897-2906</pages><issn>1078-8956</issn><issn>1546-170X</issn><eissn>1546-170X</eissn><abstract>Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05–4.27;
P
= 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85–3.62;
P
= 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration:
NCT05438576
In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>39223284</pmid><doi>10.1038/s41591-024-03243-9</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-9906-7106</orcidid><orcidid>https://orcid.org/0000-0001-5780-6070</orcidid><orcidid>https://orcid.org/0000-0003-4859-5405</orcidid><orcidid>https://orcid.org/0000-0001-7992-2398</orcidid><orcidid>https://orcid.org/0000-0003-1851-8724</orcidid><orcidid>https://orcid.org/0000-0002-8629-2029</orcidid><orcidid>https://orcid.org/0000-0002-9706-7900</orcidid><orcidid>https://orcid.org/0000-0002-0760-8014</orcidid><orcidid>https://orcid.org/0000-0002-0125-7276</orcidid><orcidid>https://orcid.org/0000-0002-4308-0456</orcidid><orcidid>https://orcid.org/0000-0002-0665-1045</orcidid><orcidid>https://orcid.org/0000-0002-5595-195X</orcidid><orcidid>https://orcid.org/0000-0003-4232-0718</orcidid><orcidid>https://orcid.org/0000-0001-5052-2948</orcidid><orcidid>https://orcid.org/0000-0002-7511-7567</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1078-8956 |
ispartof | Nature medicine, 2024-10, Vol.30 (10), p.2897-2906 |
issn | 1078-8956 1546-170X 1546-170X |
language | eng |
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source | MEDLINE; Nature Journals Online; Alma/SFX Local Collection |
subjects | 692/308/174 692/308/409 692/699/75/74 692/700/478/2772 Adult Artificial Intelligence Biomedical and Life Sciences Biomedicine Cancer Research Cardiomyopathies - diagnosis Cardiomyopathies - diagnostic imaging Cardiomyopathy Clinical trials Diagnosis Echocardiography EKG Electrocardiography Female Heart Hospitals Humans Infectious Diseases Intervention Mass Screening - methods Medical instruments Metabolic Diseases Molecular Medicine Neurosciences Nigeria - epidemiology Patients Postpartum Postpartum period Pregnancy Pregnancy Complications, Cardiovascular - diagnosis Subgroups Ventricular Dysfunction, Left - diagnosis Ventricular Dysfunction, Left - diagnostic imaging |
title | Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial |
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