Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context
Summary: Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both...
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Elsevier
2025-02-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396425000040 |
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| author | Jiesuck Park Jiyeon Kim Jaeik Jeon Yeonyee E. Yoon Yeonggul Jang Hyunseok Jeong Youngtaek Hong Seung-Ah Lee Hong-Mi Choi In-Chang Hwang Goo-Yeong Cho Hyuk-Jae Chang |
| author_facet | Jiesuck Park Jiyeon Kim Jaeik Jeon Yeonyee E. Yoon Yeonggul Jang Hyunseok Jeong Youngtaek Hong Seung-Ah Lee Hong-Mi Choi In-Chang Hwang Goo-Yeong Cho Hyuk-Jae Chang |
| author_sort | Jiesuck Park |
| collection | DOAJ |
| description | Summary: Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings. Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. We performed internal (internal test dataset [ITDS], n = 841) and external validation (distinct hospital dataset [DHDS], n = 1696; temporally distinct dataset [TDDS], n = 772) for diagnostic value across various stages of AS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement). Findings: The DL index for the AS continuum (DLi-ASc, range 0–100) increased with worsening AS severity and demonstrated excellent discrimination for any AS (AUC 0.91–0.99), significant AS (0.95–0.98), and severe AS (0.97–0.99). DLi-ASc was independent predictor for composite endpoint (adjusted hazard ratios 2.19, 1.64, and 1.61 per 10-point increase in ITDS, DHDS, and TDDS, respectively). Automatic measurement of conventional AS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AS staging (98.2% for ITDS, 82.1% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters. Interpretation: The AI-based system provides accurate and prognostically valuable AS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments. Funding: This work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (Ministry of Science and ICT; MSIT, Republic of Korea) (No. 2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC); and the Medical AI Clinic Program through the National IT Industry Promotion Agency (NIPA) funded by the MSIT, Republic of Korea (Grant No.: H0904-24-1002). |
| format | Article |
| id | doaj-art-11e77dd0668e4a3bb3debbfc2667bedd |
| institution | Kabale University |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj-art-11e77dd0668e4a3bb3debbfc2667bedd2025-01-23T05:27:10ZengElsevierEBioMedicine2352-39642025-02-01112105560Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in contextJiesuck Park0Jiyeon Kim1Jaeik Jeon2Yeonyee E. Yoon3Yeonggul Jang4Hyunseok Jeong5Youngtaek Hong6Seung-Ah Lee7Hong-Mi Choi8In-Chang Hwang9Goo-Yeong Cho10Hyuk-Jae Chang11Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of KoreaCONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of KoreaOntact Health Inc., Seoul, Republic of Korea; Corresponding author. Ontact Health Inc., 50-5, Ewhayeodae-gil, Seodaemun-gu, Seoul, Republic of Korea.Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea; Corresponding author. Division of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 82, 173 Beon-gil, Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea.Ontact Health Inc., Seoul, Republic of KoreaCONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of KoreaCONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of KoreaCONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of KoreaCardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of KoreaCardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of KoreaCardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of KoreaCONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of KoreaSummary: Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings. Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. We performed internal (internal test dataset [ITDS], n = 841) and external validation (distinct hospital dataset [DHDS], n = 1696; temporally distinct dataset [TDDS], n = 772) for diagnostic value across various stages of AS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement). Findings: The DL index for the AS continuum (DLi-ASc, range 0–100) increased with worsening AS severity and demonstrated excellent discrimination for any AS (AUC 0.91–0.99), significant AS (0.95–0.98), and severe AS (0.97–0.99). DLi-ASc was independent predictor for composite endpoint (adjusted hazard ratios 2.19, 1.64, and 1.61 per 10-point increase in ITDS, DHDS, and TDDS, respectively). Automatic measurement of conventional AS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AS staging (98.2% for ITDS, 82.1% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters. Interpretation: The AI-based system provides accurate and prognostically valuable AS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments. Funding: This work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (Ministry of Science and ICT; MSIT, Republic of Korea) (No. 2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC); and the Medical AI Clinic Program through the National IT Industry Promotion Agency (NIPA) funded by the MSIT, Republic of Korea (Grant No.: H0904-24-1002).http://www.sciencedirect.com/science/article/pii/S2352396425000040Aortic stenosisArtificial intelligenceEchocardiographyDiagnostic accuracyPrognostic value |
| spellingShingle | Jiesuck Park Jiyeon Kim Jaeik Jeon Yeonyee E. Yoon Yeonggul Jang Hyunseok Jeong Youngtaek Hong Seung-Ah Lee Hong-Mi Choi In-Chang Hwang Goo-Yeong Cho Hyuk-Jae Chang Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context EBioMedicine Aortic stenosis Artificial intelligence Echocardiography Diagnostic accuracy Prognostic value |
| title | Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context |
| title_full | Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context |
| title_fullStr | Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context |
| title_full_unstemmed | Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context |
| title_short | Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context |
| title_sort | artificial intelligence enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyresearch in context |
| topic | Aortic stenosis Artificial intelligence Echocardiography Diagnostic accuracy Prognostic value |
| url | http://www.sciencedirect.com/science/article/pii/S2352396425000040 |
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