The Use of Artificial Intelligence Guidance for Rheumatic Heart Disease Screening by Novices

Daniel Peck, Joselyn Rwebembera, Doreen Nakagaayi, Neema W. Minja, Nicholas J. Ollberding, Jafesi Pulle, Jennifer Klein, David Adams, Randolph Martin, Kilian Koepsell, Amy Sanyahumbi, Andrea Beaton, Emmy Okello, Craig Sable

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Introduction: A novel technology utilizing artificial intelligence (AI) to provide real-time image-acquisition guidance, enabling novices to obtain diagnostic echocardiographic images, holds promise to expand the reach of echo screening for rheumatic heart disease (RHD). We evaluated the ability of nonexperts to obtain diagnostic-quality images in patients with RHD using AI guidance with color Doppler. Methods: Novice providers without prior ultrasound experience underwent a 1-day training curriculum to complete a 7-view screening protocol using AI guidance in Kampala, Uganda. All trainees then scanned 8 to 10 volunteer patients using AI guidance, half RHD and half normal. The same patients were scanned by 2 expert sonographers without the use of AI guidance. Images were evaluated by expert blinded cardiologists to assess (1) diagnostic quality to determine presence/absence of RHD and (2) valvular function and (3) to assign an American College of Emergency Physicians score of 1 to 5 for each view. Results: Thirty-six novice participants scanned a total of 50 patients, resulting in a total of 462 echocardiogram studies, 362 obtained by nonexperts using AI guidance and 100 obtained by expert sonographers without AI guidance. Novice images enabled diagnostic interpretation in >90% of studies for presence/absence of RHD, abnormal MV morphology, and mitral regurgitation (vs 99% by experts, P ≤ .001). Images were less diagnostic for aortic valve disease (79% for aortic regurgitation, 50% for aortic stenosis, vs 99% and 91% by experts, P < .001). The American College of Emergency Physicians scores of nonexpert images were highest in the parasternal long-axis images (mean, 3.45; 81% ≥ 3) compared with lower scores for apical 4-chamber (mean, 3.20; 74% ≥ 3) and apical 5-chamber images (mean, 2.43; 38% ≥ 3). Conclusions: Artificial intelligence guidance with color Doppler is feasible to enable RHD screening by nonexperts, performing significantly better for assessment of the mitral than aortic valve. Further refinement is needed to optimize acquisition of color Doppler apical views.

Original languageEnglish (US)
Pages (from-to)724-732
Number of pages9
JournalJournal of the American Society of Echocardiography
Volume36
Issue number7
DOIs
StatePublished - Jul 2023

Bibliographical note

Publisher Copyright:
© 2023 American Society of Echocardiography

Keywords

  • Artificial intelligence
  • Echocardiography
  • Rheumatic heart disease
  • Screening

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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