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Chen X, Yang F, Zhang P, Lin X, Wang W, Pu H, Chen X, Chen Y, Yu L, Deng Y, Liu B, Bai Y, Burkhoff D, He K. Artificial Intelligence-Assisted Left Ventricular Diastolic Function Assessment and Grading: Multiview Versus Single View. J Am Soc Echocardiogr 2023; 36:1064-1078. [PMID: 37437669 DOI: 10.1016/j.echo.2023.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 06/28/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUND Clinical assessment and grading of left ventricular diastolic function (LVDF) requires quantification of multiple echocardiographic parameters interpreted according to established guidelines, which depends on experienced clinicians and is time consuming. The aim of this study was to develop an artificial intelligence (AI)-assisted system to facilitate the clinical assessment of LVDF. METHODS In total, 1,304 studies (33,404 images) were used to develop a view classification model to select six specific views required for LVDF assessment. A total of 2,238 studies (16,794 two-dimensional [2D] images and 2,198 Doppler images) to develop 2D and Doppler segmentation models, respectively, to quantify key metrics of diastolic function. We used 2,150 studies with definite LVDF labels determined by two experts to train single-view classification models by AI interpretation of strain metrics or video. The accuracy and efficiency of these models were tested in an external data set of 388 prospective studies. RESULTS The view classification model identified views required for LVDF assessment with good sensitivity (>0.9), and view segmentation models successfully outlined key regions of these views with intersection over union > 0.8 in the internal validation data set. In the external test data set of 388 cases, AI quantification of 2D and Doppler images showed narrow limits of agreement compared with the two experts (e.g., left ventricular ejection fraction, -12.02% to 9.17%; E/e' ratio, -3.04 to 2.67). These metrics were used to detect LV diastolic dysfunction (DD) and grade DD with accuracy of 0.9 and 0.92, respectively. Concerning the single-view method, the overall accuracy of DD detection was 0.83 and 0.75 by strain-based and video-based models, and the accuracy of DD grading was 0.85 and 0.8, respectively. These models could achieve diagnosis and grading of LVDD in a few seconds, greatly saving time and labor. CONCLUSION AI models successfully achieved LVDF assessment and grading that compared favorably with human experts reading according to guideline-based algorithms. Moreover, when Doppler variables were missing, AI models could provide assessment by interpreting 2D strain metrics or videos from a single view. These models have the potential to save labor and cost and to facilitate work flow of clinical LVDF assessment.
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Affiliation(s)
- Xu Chen
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China; Department of Cardiology, The Second Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Feifei Yang
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China; Department of Cardiology, The Fourth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Peifang Zhang
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Xixiang Lin
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Wenjun Wang
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Haitao Pu
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Xiaotian Chen
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Yixin Chen
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Liheng Yu
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Yujiao Deng
- Department of Ultrasound Diagnosis, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Bohan Liu
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Yongyi Bai
- Department of Cardiology, The Second Medical Center of Chinese PLA General Hospital, Beijing, China
| | | | - Kunlun He
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
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Strachinaru M, Bosch JG. Automated algorithms in diastology: how to move forward? THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:975-977. [PMID: 35132500 DOI: 10.1007/s10554-021-02505-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 11/24/2022]
Affiliation(s)
- Mihai Strachinaru
- Department of Cardiology, Erasmus University Medical Center, Postbus 2040, 3000 CA, Rotterdam, The Netherlands.
- Biomedical Engineering, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - Johan G Bosch
- Biomedical Engineering, Erasmus University Medical Center, Rotterdam, The Netherlands
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