Echocardiography is a fundamental tool in cardiovascular diagnostics, providing radiation-free real-time assessments of cardiac function. However, its accuracy strongly depends on operator expertise, resulting in inter-operator variability that affects diagnostic consistency. Recent advances in artificial intelligence have enabled new applications for real-time image classification and probe guidance, but these typically rely on large datasets and specialized hardware such as GPU-based or embedded accelerators, limiting their clinical adoption. Here, we address this challenge by developing a cognitive electronic unit that integrates convolutional neural network (CNN) models and an inertial sensor for assisted echocardiography. We show that our system—powered by an NVIDIA Jetson Orin Nano—can effectively classify standard cardiac views and differentiate good-quality from poor-quality ultrasound images in real time even when trained on relatively small datasets. Preliminary results indicate that the combined use of CNN-based classification and inertial sensor-based feedback can reduce inter-operator variability and may also enhance diagnostic precision.
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