# Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging  
[PERSON]  
1 Micro Nano Sensor Group, Politecnico di Bari, 70125 Bari, Italy; [EMAIL_ADDRESS]  
[PERSON]  
2  
[PERSON]  
2 Dipartimento Internatreneto di Fisica, Universita degli Studi di Bari Aldo Moro, 70125 Bari, Italy; [PERSON]DDRESS].it (T.A. ); [EMAIL_ADDRESS] (M.L.R. ); [EMAIL_ADDRESS] (T.M. ); [EMAIL_ADDRESS] (R.B.) 23 Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125 Bari, Italy 23 Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Baridiagnostic consistency of echocardiographic images. Recent advancements in artificial intelligence techniques have enabled the exploration of both robotic and assisted ultrasound applications to address these critical issues [2]. In the robotic scenario, a robotic arm performs ultrasound examinations under the guidance of artificial intelligence algorithms. These algorithms manage various functions, including planning the scanning path, adjusting the arm's movement in space and the probe's pressure on the scanned body area, and completing the scan. Robotic ultrasound systems can be classified into three categories based on their level of autonomy: teleoperated [3], semi-autonomous [4], and fully autonomous [5]. Teleoperated systems involve robotic arms piloted by an operator, aiming to reduce or prevent musculoskeletal disorders in physicians and facilitate remote diagnosis. Semi-autonomous systems autonomously perform some tasks, such as positioning the probe in the body region of interest, but still require operator intervention to conduct the examination. Fully autonomous systems can independently plan and execute the ultrasound scan, acquiring the necessary images without sonographer intervention and allowing the physician to review the examination subsequently. By contrast, in the assisted ultrasound scenario a sonographer performs the examination while being guided by an algorithm in their movement of the probe. Although robotic ultrasound remains primarily within the research domain, some applications of assisted ultrasound have already reached the market, including [6], which validated an AI-based real-time guidance system enabling novice operators to obtain diagnostic-quality echocardiographic images with minimal training, illustrating the potential of assisted ultrasound solutions to broaden access and streamline workflow in clinical settings. In particular, echocardiography requires precise probe placement and immediate feedback to capture complex cardiac dynamics, making it especially poised to benefit from these innovations. Notable advancements in the echocardiographic domain have employed deep learning algorithms to guide non-expert operators. To reduce inter-operator variability, the use of deep learning algorithms in echocardiographic examinations is proving promising for the selection of echocardiographic views, image quality control, and improved real-time probe guidance [7]. Among deep learning algorithms, CNNs and their variants have shown excellent performance in recognizing and segmenting cardiac structures [8]. The use of CNNs allows for the quantification of cardiac parameters and identification of pathological patterns with accuracy comparable to that of clinical experts [9]. Models developed for the classification of cardiac views differ in the number of classifiable views, but have shown accuracy levels of up to 98% [10] for five cardiac views [11] and fifteen cardiac views [12]. An important distinction must be made between classification performed on videos and on static images; because the heart is a moving organ, it is necessary to identify transitions between different views and consider changes in the image due to the opening and closing of the heart valves.