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Early diagnosis and risk stratification of aortic stenosis using artificial intelligence applied to echocardiography:
Gabriel Eduardo Malagón Tarqui1, Juanita Valencia García1, Erwin Hernando Hernández Rincón2
1Primary Care Physician, Universidad de La Sabana, Chía, Colombia.
Insights
Artificial intelligence (AI) shows promise for early aortic stenosis (AS) diagnosis using echocardiography, achieving expert-level accuracy in studies. Further validation is needed for widespread clinical use.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Aortic stenosis (AS) is the most common acquired valvular heart disease.
- Severe symptomatic AS has a high mortality rate if untreated.
- Echocardiography is crucial for AS diagnosis but is operator-dependent.
Purpose of the Study:
- To assess the utility of AI in echocardiography for early AS diagnosis.
- To identify AI performance, clinical applicability, and limitations.
Main Methods:
- A scoping review of 25 studies published between January 2020 and December 2025.
- Searches conducted in PubMed, Scopus, Web of Science, and BIREME.
- Included studies used AI for AS diagnosis and risk stratification via echocardiography.
Main Results:
- AI algorithms, particularly convolutional neural networks, demonstrated varied performance (AUC 0.82-0.99).
- High sensitivity (82.2-90%) and specificity (88-99%) were reported.
- Multivision AI models outperformed single-vision models.
Conclusions:
- AI algorithms show strong potential for detecting and grading AS severity.
- AI performance in retrospective studies rivals expert accuracy.
- Barriers include lack of external validation, interpretability, and clinical integration.
Introduction:
Aortic stenosis (AS) is the most common acquired valvular heart disease worldwide, accounting for 43% of valvular diseases. It is estimated that 40-50% of patients with severe symptomatic AS do not receive intervention, resulting in a mortality rate of over 90%. Transthoracic echocardiography remains the gold standard for diagnosis, making it critical for early detection of the disease. However, it is operator-dependent and varies according to the patient's clinical presentation. In this context, artificial intelligence algorithms, especially deep learning algorithms applied to echocardiography, are emerging as tools with the potential to automate and improve the detection of aortic stenosis.
Objective:
To evaluate the available evidence on the usefulness of artificial intelligence tools applied to echocardiography for the early diagnosis of aortic stenosis, identifying their performance, clinical applicability, and methodological limitations.
Methods:
A scoping review was conducted in four databases (PubMed, Scopus, Web of Science, and BIREME) in accordance with the PRISMA-ScR guideline, which included 25 studies between January 2020 and December 2025 that used AI systems applied to echocardiography for the early diagnosis and risk stratification of aortic stenosis.
Results:
Twenty-five studies met the inclusion criteria for this review. Artificial intelligence (AI) algorithms, especially convolutional neural networks, achieved heterogeneous performance. The AUC ranged from 0.82 to 0.99; sensitivity was 82.2-90% and specificity was 88-99%. Multivision models performed better than single-vision models.
Conclusions:
Artificial intelligence algorithms perform well in detecting and classifying the severity of AS. Their performance shows high diagnostic potential in retrospective datasets, reaching metrics that emulate expert accuracy. Critical barriers remain, such as lack of external validation, interpretability, and clinical integration. Prospective multicenter studies with harmonized regulatory frameworks are needed for global validation.
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