# Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging
## 2 Materials and Methods  
### Hardware Setup  
The selection of appropriate hardware for executing deep learning algorithms was guided by a thorough literature review focused on evaluating the performance of edge devices in artificial intelligence applications. Given the parallel development of software and the absence of specific algorithms to test, it was crucial to base hardware choices on established benchmarks and comparative analyses available in the literature. The initial comparative analysis included devices commonly tested in image processing applications, such as the ASUS Tinker Edge R, Raspberry Pi 4, Google Coral Dev Board,NVIDIA Jetson Nano, and Arduino Nano 33 BLE. The evaluation considered inference speed and accuracy across various network models. Findings indicated that the Google Coral Dev Board demonstrated superior performance in continuous computational applications for models compatible with the TensorFlow Lite framework. The NVIDIA Jetson Nano ranked closely behind, offering greater versatility and the ability to train models using the onboard GPU [22]. Further analysis narrowed the focus to GPU-equipped devices. The literature indicated that the NVIDIA Jetson Nano exhibited better image processing performance compared to the Jetson TX2, GTX 1060, and Tesla V100 in convolutional neural network applications [23]. Within the NVIDIA Jetson series, the Jetson Orin Nano was identified as significantly outperforming both the Jetson Nano and Jetson AGX Xavier for video processing tasks using convolutional neural network models developed in PyTorch and optimized with NVIDIA's Torch-TensorRT SDK [24]. Based on these insights, the NVIDIA Jetson Orin Nano was selected for its robust performance. Table 1 summarizes the main features of the NVIDIA Jetson Orin Nano. Figure 1 shows the experimental setup used for both ultrasound data collection and the inference phase. A small inertial measurement unit (IMU) was selected for attachment to the ultrasound probe in order to utilize the probe's spatial position during inference. After conducting tests on both online and offline ultrasound video processing through deep learning applications, we began to evaluate the performance of the hardware setup by developing a simple CNN model trained on the Camus dataset [25], which includes echocardiographic sequences from 500 patients with varying image quality. Given the high variability in image acquisition and quality, the only preprocessing step we applied was resizing all images to 256 \(\times\) 256 pixels to ensure input uniformity. This minimal preprocessing allows the model to remain robust across different input qualities, which is crucial for generalization in real-world clinical settings. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline CPU cores & 6 \\ CUDA cores (GPU) & 1024 \\ Tensor cores (GPU) & 32 \\ TOP/s & 40 \\ Power (W) & 7–15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NVIDIA Jetson Orin Nano technical specifications. Figure 1: Hardware setup: the ultrasound scanner feed is acquired by a dedicated video capture board and processed by the Jetson Orin Nano, while an IMU captures the probe’s motion. Both streams are synchronized by the data acquisition system, enabling real-time classification (view type, image quality) and immediate operator feedback. The neural network then classified echocardiographic images into \"Apical projection with 2-chamber view (2 CH)\", \"Apical projection with 4-chamber view (4 CH)\", and \"Unknown\", with the latter including all non-classifiable images. This network was designed to perform classification of the observed cardiac projection automatically and in real time. A second CNN was trained on the same dataset to perform binary classification of image quality, distinguishing between \"good quality\" and \"bad/poor quality\" for each frame.