A team of Australian researchers developed an echo AI solution that accurately assesses patients’ aortic stenosis (AS) severity levels, including many patients with severe AS who might go undetected using current methods.
The researchers trained their AI-Decision Support Algorithm (AI-DSA) using the Australian Echo Database, which features more than 1M echo exams from over 630k patients, and includes the patients’ 5-year mortality outcomes.
Using 179k echo exams from the same Australian Echo Database, the researchers found that AI-DSA detected…
- Moderate-to-severe AS in 2,606 patients, who had a 56.2% five-year mortality rate
- Severe AS in 4,622 patients, who had a 67.9% five-year mortality rate
Those mortality rates are far higher than the study’s remaining 171,826 patients (22.9% 5yr rate), giving the individuals that AI-DSA classified with moderate-to-severe or severe AS significantly higher odds of dying within five years (Adjusted odds ratios: 1.82 & 2.80).
AI-DSA also served as a valuable complement to current methods, as 33% of the patients that AI-DSA identified with severe AS would not have been detected using the current echo assessment guidelines. However, severe AS patients who were only flagged by the AI-DSA algorithm had similar 5-year mortality rates as patients who were flagged by both AI-DSA and the current guidelines (64.4% vs. 69.1%).
There’s been a lot of promising echo AI research lately, but most studies have highlighted the technology’s performance in comparison to sonographers. This new study suggests that echo AI might also help identify high-risk AS patients who wouldn’t be detected by sonographers (at least if they are using current methods), potentially steering more patients towards life-saving aortic valve replacement procedures.
A new JASE study showed that AI-based echocardiography measurements can be used to predict COVID patient mortality, but manual measurements performed by echo experts can’t. This could be seen as yet another “AI beats humans” study (or yet another COVID AI study), but it also gives important evidence of AI’s potential to reduce echo measurement variability.
Starting with transthoracic echocardiograms from 870 hospitalized COVID patients (13 hospitals, 9 countries, 27.4% who later died), the researchers utilized Ultromics’ EchoGo Core AI solution and a team of expert readers to measure left ventricular ejection fraction (LVEF) and LV longitudinal strain (LVLS). They then analyzed the measurements and applied them to mortality prediction models, finding that the AI-based measurements:
- Were “significant predictors” of patient mortality (LVEF: OR=0.974, p=0.003; LVLS: OR=1.060, p=0.004), while the manual measurements couldn’t be used to predict mortality
- Had significantly less variability than the experts’ manual measurements
- Were similarly “feasible” as manual measurements when applied to the various echo exams
- Showed stronger correlations with other COVID biomarkers (e.g. diastolic blood pressure)
- Combined with other biomarkers to produce even more accurate mortality predictions
The authors didn’t seem too surprised that AI measurements had less variability, or by their conclusion that reducing measurement variability “consequently increased the statistical power to predict mortality.”
They also found that sonographers’ original scanning inconsistency was responsible for nearly half of the experts’ measurement variability, suggesting that a combination of echo guidance AI software (e.g. Caption or UltraSight) with echo reporting AI tools (e.g. Us2.ai or Ultromics) could “further reduce variability.”
Echo AI measurements aren’t about to become a go-to COVID mortality biomarker (clinical factors and comorbidities are much stronger predictors), but this study makes a strong case for echo AI’s measurement consistency advantage. It’s also a reminder that reducing variability improves overall accuracy, which would be valuable for sophisticated prediction models or everyday echocardiography operations.
A new JACC study showed that Ultromics’ EchoGo Pro AI solution can accurately classify stress echocardiograms, while improving clinician performance with a particularly challenging and operator-dependent exam.
The researchers used EchoGo Pro to independently analyze 154 stress echo studies, leveraging the solution’s 31 image features to identify patients with severe coronary artery disease with a 0.927 AUC (84.4% sensitivity; 92.7% specificity).
EchoGo Pro maintained similar performance with a version of the test dataset that excluded the 38 patients with known coronary artery disease or resting wall motion abnormalities (90.5% sensitivity; 88.4% specificity).
The researchers then had four physicians with different levels of stress echo experience analyze the same 154 studies with and without AI support, finding that the EchoGo Pro reports:
- Improved the readers’ average AUC – 0.877 vs. 0.931
- Increased their mean sensitivity – 85% vs. 95%
- Didn’t hurt their specificity – 83.6% vs. 85%
- Increased their number of confident reads – 440 vs. 483
- Reduced their number of non-confident reads – 152 vs. 109
- Improved their diagnostic agreement rates – 0.68-0.79 vs. 0.83-0.97
Ultromics’ stress echo reports improved the physicians’ interpretation accuracy, confidence, and reproducibility, without increasing false positives. That list of improvements satisfies most of the requirements clinicians have for AI (in addition to speed/efficiency), and it represents another solid example of echo AI’s real-world potential.