# Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging
Some studies have explored the use of recurrent neural networks (RNNs) and generative adversarial networks (GANs); the former can enable real-time image classification and provide guidance for correct probe alignment [13; 14], while the latter have been used to adapt the quality of the ultrasound image to a user-defined level without the need for matched pairs of low- and high-quality images [15]. Using a GAN, [16] achieved accuracy greater than 90% in both classification and segmentation of cardiac views for the diagnosis of left ventricular hypertrophy. To improve performance, architectures that simultaneously use spatial and temporal information have recently been explored, including time-distributed and two-stream networks [14]. Despite significant advances in algorithms, there are numerous critical issues that need to be addressed in the development of assisted ultrasound applications that can be easily used by operators. The most relevant aspect is the need to acquire, label, and archive large datasets [9]. This activity should be carried out through multi-center studies and with different equipment, which is contrary to usual practice [11]. In addition, the use of specific datasets can improve the accuracy of automated diagnosis and promote greater integration of artificial intelligence into clinical practice for patients with congenital and structural heart disease [17]. Another significant limitation is poor image quality due to background noise and limited acoustic windows [18] as well as the movement of anatomical structures, which requires considering the temporal evolution of the image [12]. This aspect is linked to the poor interpretability of deep learning algorithms [7; 8] and may leads to more cautious adoption of these technologies by healthcare professionals [10]. The computing power required to train such algorithms is another aspect that must be properly assessed, as it is necessary to find a balance between image resolution and computational load [16]. For assisted probe positioning, one of the most important issues is the tracking method [13], which can be implemented with cameras or IMUs. The use of cameras can lead to privacy issues and inflexible setups, while tracking with a single IMU is complex due to signal noise that prevents the correct reconstruction of the trajectory. One method that has been explored to obtain a generic trajectory of an object using a single IMU is to break down the acquired trajectory into basic trajectories, then pair these with predefined geometric models [19]. Another promising technique appears to be exploiting the continuous wavelet transform to correct drift and obtain a more accurate position estimate [20]. A further approach involves error compensation to correct drift accumulation through a reset mechanism. However, these approaches cannot compensate for poor sensor quality or avoid the need for accurate initial calibration [21]. In summary, existing solutions for assisted echocardiography often rely on large labeled datasets [9; 11] or multiple sensors [13; 19], and may still encounter issues with noise, drift, and interpretability, particularly when using single-IMU setups and AI-based image analysis [7; 8]. While deep learning has achieved notable results in classifying echocardiographic views and monitoring image quality [13; 14], practical barriers such as complex calibration steps [20; 21], data scarcity [9; 11], and computational overhead [16] continue to persist. The objective of the ongoing research activity reported in this paper is to develop a cognitive unit that enhances ultrasound imaging by assisting operators with real-time guidance and assessment. In this framework, 'cognitive' highlights the system's planned ability to learn optimal probe manipulation by fusing real-time inertial sensor data with CNN-based analysis, ultimately providing adaptive and context-specific guidance to the operator. This unit aims to improve image quality and provide valuable feedback during the scanning process.