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Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities

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Abstract

The diagnostic accuracy of exercise stress echocardiography (ESE) for myocardial ischemia requires improvement, given that it currently depends on the physicians’ experience and image quality. To address this issue, we aimed to develop artificial intelligence (AI)-based slow-motion echocardiography using inter-image interpolation. The clinical usefulness of this method was evaluated for detecting regional wall-motion abnormalities (RWMAs). In this study, an AI-based echocardiographic image-interpolation pipeline was developed using optical flow calculation and prediction for in-between images. The accuracy for detecting RWMAs and image readability among 25 patients with RWMA and 25 healthy volunteers was compared between four cardiologists using slow-motion and conventional ESE. Slow-motion echocardiography was successfully developed for arbitrary time-steps (e.g., 0.125×, and 0.5×) using 1,334 videos. The RWMA detection accuracy showed a numerical improvement, but it was not statistically significant (87.5% in slow-motion echocardiography vs. 81.0% in conventional ESE; odds ratio: 1.43 [95% CI: 0.78–2.62], p = 0.25). Interreader agreement analysis (Fleiss’s Kappa) for detecting RWMAs among the four cardiologists were 0.66 (95%CI: 0.55–0.77) for slow-motion ESE and 0.53 (95%CI: 0.42–0.65) for conventional ESE. Additionally, subjective evaluations of image readability using a four-point scale showed a significant improvement for slow-motion echocardiography (2.11 ± 0.73 vs. 1.70 ± 0.78, p < 0.001).In conclusion, we successfully developed slow-motion echocardiography using in-between echocardiographic image interpolation. Although the accuracy for detecting RWMAs did not show a significant improvement with this method, we observed enhanced image readability and interreader agreement. This AI-based approach holds promise in supporting physicians’ evaluations.

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Data availability

Private patient data access is limited for privacy reasons. Codes are publicly available in https://github.com/Gifu-University-Cardiology/Slow-motion-Echocardiography These codes were modified from https://github.com/avinashpaliwal/Super-SloMo.

Abbreviations

A2C:

Apical-2-chamber

A3C:

Apical-3-chamber

A4C:

Apical-4-chamber

AI:

Artificial Intelligence

CI:

Confidence interval

DICOM:

Digital Imaging and Communications in Medicine

ESE:

Exercise stress echocardiography

LVEF:

Left ventricular ejection fraction

OR:

Odds ratio

PLAX:

Parasternal long axis

PSAX:

Parasternal short axis

ROC:

Receiver operating characteristic

RWMA:

Regional wall motion abnormality

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Acknowledgements

The authors thank all the participants (patients and in-hospital volunteers) in the study and the Division of Clinical Laboratory members in Gifu University.

Funding

This study was supported by the Physician Clinical Research grant from the Japanese Circulation Society (2021) and the 34th research grant from Fukuda Foundation for Medical Technology. (T.H) This work was partly supported by JST, CREST (JPMJCR21D4), Japan.

Author information

Author notes

  1. Yuki Sahashi and Ryo Takeshita served as co-first authors and contributed equally to this work. Takeshi Hara and Hiroyuki Okura contributed equally to this work as co-senior authors.

Authors and Affiliations

  1. Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan

    Yuki Sahashi, Takatomo Watanabe, Takeshi Ishihara, Hajime Ichiryu, Susumu Endo & Hiroyuki Okura

  2. Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University Graduate School of Medicine, Gifu, Japan

    Ryo Takeshita, Daisuke Fukuoka & Takeshi Hara

  3. Division of Clinical Laboratory, Gifu University Hospital, Gifu, Japan

    Takatomo Watanabe, Ayako Sekine, Daichi Watanabe & Hiroyuki Okura

  4. Innovative and Clinical Research Promotion Center, Gifu University Hospital, Gifu, Japan

    Takuma Ishihara

  5. Department of Pharmacy, Gifu University Hospital, Gifu, Japan

    Daichi Watanabe

  6. Faculty of Education, Gifu University, Gifu, Japan

    Daisuke Fukuoka

  7. Center for Research, Education, and Development for Healthcare Life Design (C-REX), Tokai National Higher Education and Research System, Gifu, Japan

    Takeshi Hara

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Contributions

Y.S, R.T, T.H, Takuma Ishihara and H.O had full access to all of the data of the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Y.S, T.W, T.H, and H.O. Acquisition, analysis, or interpretation of data: Y.S, A.S, Takeshi Ishihara, E.S, T.W, H.I, M.H and, H.O. Model development: R.T, D.F, and H.T. Drafting of the manuscript: Y.S. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Takuma Ishihara, D.W. Obtained funding: Y.S. Supervision: T.H, H.O.

Corresponding author

Correspondence to Yuki Sahashi.

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Competing interests

The authors declare no competing interests.

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We used the services of Editage® to perform English proofreading on the final manuscript. The proofreading was conducted to ensure the accuracy and clarity of the language used in the manuscript. We did not use any Generative AI services in the writing process. All content in this manuscript was written by the authors and was manually.

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Supplemental Fig. 1

. Histogram showing the number of ischemic segments identified during exercise stress echocardiography among patients with regional wall motion abnormalities (n = 25).

Supplemental Fig. 2

. (A) The association between loss function and the number of epochs (training and validation dataset), and (B) the association between peak signal to noise ratio (PSNR) and the number of epochs (test dataset).

Supplemental table 1

. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME) checklist.

Supplemental table 2

. The Standards for Reporting of Diagnostic Accuracy (STARD) checklist.

Supplemental Fig. 3

. Representative comparison between the control and slow-motion video images (0.25×, 0.125× speed) in a healthy volunteer (non-stress conventional echocardiography). Slow-motion echocardiography (for example, at 0.25×) is developed by quadrupling the number of images while maintaining the same fps.

Supplemental Fig. 4

. Representative comparison between the control and artificial intelligence-based slow-motion video images (0.25×, used in the diagnostic accuracy test) in both healthy volunteer and patients with regional wall motion abnormalities (post-myocardial infarction of diagonal artery).

Supplemental Fig. 5

. Randomly selected video images from four patients in test dataset showing that the trained model successfully created slow-motion echocardiography images among a wide range of cardiovascular patients.

Supplemental Fig. 6

. Representative echocardiograms showing that both videos with high fps and low fps were successfully converted to slow-motion echocardiography videos.

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Sahashi, Y., Takeshita, R., Watanabe, T. et al. Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities. Int J Cardiovasc Imaging 40, 385–395 (2024). https://doi.org/10.1007/s10554-023-02997-6

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