# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 1 Introduction  
Within the field of diagnostic imaging, lung ultrasound (LUS) has become significantly more prominent in recent years, offering a non-invasive, radiation-free approach for dynamic assessment of lung condition in various respiratory diseases [1]. Yet, despite its growing importance, the interpretation challenges inherent to pulmonary ultrasound images persist, which, combined with a shortage of skilled professionals trained in this technique, limits its application in clinical practice [2]. LUS images do not provide an anatomical view of the lung; this is because ultrasound waves hardly penetrate into the lung parenchyma due to the air present in the alveoli. Instead, most of the acoustic energy is reflected by the pleura, which is the most easily identifiable structure, appearing like a bright horizontal line. Furthermore, replicas of the pleura appear at regular intervals below this line, generated by the reverberation of the incident wave between the transducer and the pleura. This artifact, named A-Line, is indicative of good lung condition. In the presence of pneumonia and other interstitial syndrome diseases, the air inside the alveoli is progressively substituted by liquid, which modifies the acoustic impedance of the lung parenchyma, making it more similar to the impedance of muscle and fat above the pleura. As the acoustic impedance difference between both tissues reduces, more energy of the incident wave passes through the pleura to the lung parenchyma. But if air remains in some alveoli, a local reverberation phenomenon occurs (acoustic trap), and a bright vertical line appears in the image because of the multiple echoes backscattered inside thelesion [3]. This vertical artifact is named B-Line, and it is indicative of the presence of pneumonia. Figure 1 shows some examples to better understand the morphology of the artifacts explained. As the disease advances, more air is substituted by liquid and then consolidates into solid material. These consolidations appear in the image as hypoechoic regions and constitute the third artifact usually looked for when imaging the lung. In sum, artifacts like A-lines, B-lines, and consolidations are indicative of lung condition and can be used to diagnose a pulmonary pathology [4]. Detecting and interpreting these artifacts is the key for a correct evaluation of lung images, which has been the principal bottleneck in the dissemination of this technique. Moreover, the shock caused by the COVID-19 pandemic amplified the relevance of pulmonary ultrasound as a vital tool for evaluating pulmonary conditions. As the pandemic disseminated worldwide, LUS emerged as a frontline modality for assessing lung involvement and monitoring pulmonary condition, particularly in patients affected with COVID-19 pneumonia. Its real-time, bedside capabilities offered rapid insights into lung pathology, especially in emergency, critical care units, and domiciliary attention, where other imaging modalities like TAC and magnetic resonance encountered limitations or were less accessible [1, 5, 6, 7, 8]. In this context, the integration of diagnostic aid algorithms into ultrasound scanners could help reduce the learning curve of the technique, as well as reducing the evaluation time and possibly increasing the diagnosis success. Artificial intelligence (AI) has brought about significant advancements across diverse scientific and medical fields, showcasing its potential in reshaping the landscape of diagnosis and patient welfare. Within the realm of healthcare, AI is revolutionizing the field, showcasing promising outcomes through the provision of sophisticated tools that aid healthcare providers in making clinical decisions. This integration has led to improvements in precision and effectiveness, enhancing the process of diagnosing and treating a wide range of medical conditions [9]. The machine learning approach for detecting and classifying lesions is not exclusive to ultrasound imaging.