Why the study?
Neural network classifiers detect aortic stenosis well on cart-based ultrasound, but had never been tested or fine-tuned for focused cardiac ultrasound acquisitions from handheld devices.
Does a fine-tuned machine learning classifier improve the automated detection of aortic stenosis using handheld focused cardiac ultrasound in adults ≥65 years?
Does a fine-tuned machine learning classifier improve the automated detection of aortic stenosis using handheld focused cardiac ultrasound in adults ≥65 years?
Fine-tuning a machine learning model specific to handheld ultrasound devices significantly recovers diagnostic performance for detecting aortic stenosis compared to standard cart-based models, facilitating potential point-of-care screening.
May enable AS screening on handheld FoCUS in older adults; leaves open prospective validation before clinical adoption.
Aims: Neural network classifiers can detect aortic stenosis (AS) using limited cardiac ultrasound images. While networks perform very well using cart-based imaging, they have never been tested or fine-tuned for use with focused cardiac ultrasound (FoCUS) acquisitions obtained on handheld ultrasound devices. Methods and results: Prospective study performed at Tufts Medical Center. All patients ≥65 years of age referred for clinically indicated transthoracic echocardigraphy (TTE) were eligible for inclusion. Parasternal long axis and parasternal short axis imaging was acquired using a commercially available handheld ultrasound device. Our cart-based AS classifier (trained on ∼10 000 images) was tested on FoCUS imaging from 160 patients. The median age was 74 (inter-quartile range 69-80) years, 50% of patients were women. Thirty patients (18.8%) had some degree of AS. The area under the received operator curve (AUROC) of the cart-based model for detecting AS was 0.87 (95% CI 0.75-0.99) on the FoCUS test set. Last-layer fine-tuning on handheld data established a classifier with AUROC of 0.94 (0.91-0.97). AUROC during temporal external validation was 0.97 (95% CI 0.89-1.0). When performance of the fine-tuned AS classifier was modelled on potential screening environments (2 and 10% AS prevalence), the positive predictive value ranged from 0.72 (0.69-0.76) to 0.88 (0.81-0.97) and negative predictive value ranged from 0.94 (0.94-0.94) to 0.99 (0.99-0.99) respectively. Conclusion: Our cart-based machine-learning model for AS showed a drop in performance when tested on handheld ultrasound imaging collected by sonographers. Fine-tuning the AS classifier improved performance and demonstrates potential as a novel approach to detecting AS through automated interpretation of handheld imaging.
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Karmiy et al. (2025) studied this question.
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