July 1, 2023Open Access
Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study
Structured PICO
Does an artificial intelligence decision support algorithm using routine echocardiographic measurements identify severe aortic stenosis phenotypes associated with high mortality?
P
Population
184,301 individuals undergoing routine echocardiography evaluated for severe aortic stenosis
I
Intervention
Artificial intelligence decision support algorithm (AI-DSA) using routine echocardiographic measurements (excluding left ventricular outflow tract velocity or dimension measurements)
C
Comparator
AI-derived low-probability AS group and guideline-defined severe AS
O
Outcome
Detection of severe aortic stenosis (Area under receiver operating characteristic curve) and 5-year mortality
An AI decision support algorithm using routine echocardiographic data can accurately identify severe aortic stenosis phenotypes associated with high 5-year mortality, including cases not meeting standard guideline definitions.
Abstract
OBJECTIVE: We developed an artificial intelligence decision support algorithm (AI-DSA) that uses routine echocardiographic measurements to identify severe aortic stenosis (AS) phenotypes associated with high mortality. METHODS: , excluding all left ventricular outflow tract velocity or dimension measurements and then using the remainder of echocardiographic measurement data. The optimal probability threshold for severe AS detection was identified at the f1 score probability of 0.235. An automated feature also ensured detection of guideline-defined severe AS. The AI-DSA's performance was independently evaluated in 184 301 (30%) individuals. RESULTS: The area under receiver operating characteristic curve for the AI-DSA to detect severe AS was 0.986 (95% CI 0.985 to 0.987) with 4622/88 199 (5.2%) individuals (79.0±11.9 years, 52.4% women) categorised as 'high-probability' severe AS. Of these, 3566 (77.2%) met guideline-defined severe AS. Compared with the AI-derived low-probability AS group (19.2% mortality), the age-adjusted and sex-adjusted OR for actual 5-year mortality was 2.41 (95% CI 2.13 to 2.73) in the high probability AS group (67.9% mortality)-5-year mortality being slightly higher in those with guideline-defined severe AS (69.1% vs 64.4%; age-adjusted and sex-adjusted OR 1.26 (95% CI 1.04 to 1.53), p=0.021). CONCLUSIONS: An AI-DSA can identify the echocardiographic measurement characteristics of AS associated with poor survival (with not all cases guideline defined). Deployment of this tool in routine clinical practice could improve expedited identification of severe AS cases and more timely referral for therapy.