Artificial Intelligence

New Echo AI Model Could Streamline Mitral Regurgitation Diagnosis

Columbia and Cornell researchers developed an echo AI model that could improve the difficult task of mitral regurgitation diagnosis, and might even represent an AI-driven step towards enhancing all valvular regurgitation assessments.

  • Transthoracic echocardiography is the go-to modality for mitral regurgitation diagnosis, but these exams are difficult to perform and prone to high variability.

The researchers set out to solve this problem using AI, and indeed showed that AI could perform MR exams at the same level as academic echo labs.

Using 52,702 Columbia-sourced TTEs for model training and validation, the researchers developed their end-to-end AI system to intake complete TTE studies, identify color MR Doppler videos, then accurately determine MR severity on a 4-step scale (none/trace, mild, moderate, and severe). 

They then tested their AI against 8,987 TTEs from an internal Columbia dataset and 8,208 TTEs from an external Cornell dataset, finding that the model achieved high…

  • Agreement with cardiologist interpretations – 82% & 79% accuracy (k = 0.84 & 0.80)
  • Performance for detecting moderate/severe MR – 0.98 & 0.98 AUROCs

Most misclassification disagreements between the AI and cardiologists involved exams with none/trace or mild MR, while the AI maintained robust performance across different MR types (slightly lower with eccentric MR cases).

  • When the AI and cardiologist disagreed, an adjudication panel sided with the AI and cardiologists about a quarter of the time each, while half the time they thought the answer was in the middle.
  • The researchers also found that AI trained to use multiple TTE views outperformed models using the apical 4-chamber view (82% vs. 80%).

Next up, the researchers plan to continue their echo AI efforts, including expanding to other valvular and cardiac assessments, and bringing their research into clinical settings.

The Takeaway

Echocardiography has long been an AI hotspot due to its importance, prevalence, and its challenges with efficiency and variability, but most clinical-level echo AI solutions have focused on heart failure and aortic stenosis so far. Although Columbia and Cornell’s new AI model is still in its early stages, this study makes a solid case for how much more echo AI can help, including across a full range of valvular regurgitation assessments.

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