A Prospective Study for the Conversion of Ultrasound Images to CT Format Imaging Using Artificial Intelligence-based Learning

Background and rationale of the medical experiment:

Ultrasound imaging is an imaging method that uses sound waves to characterize the structure and function of various organs in health and disease conditions. This technique is widely used in clinical daily life and has many benefits. The exam does not involve the use of ionizing radiation, so its use is safer than other techniques such as X-rays or CT scans (Computed Tomography). The images acquired and viewed on the ultrasound device are real-time, so changes can be identified, which in many cases affects the final diagnosis. The test is non-invasive, nor does the test require the use of a contrast agent that contains substances that may cause an allergic reaction or impair kidney function. In addition, the equipment is widely available, and can also be used bedside. Aside from the mentioned advantages, the ultrasound test also has notable drawbacks. The ultrasound waves do not penetrate well through bones or air, thus impairing test quality. In addition, the method is highly dependent on the skill of the operator, so substantial experience is required in order to produce sufficient quality information and make an accurate diagnosis. The quality of the test also depends on the cooperation of the subject during the test, such as changes in posture and deep breathing.

Compared to the ultrasound examination, the CT scan allows for a broader anatomical view, and is not limited by physiological factors such as bones and air. The most notable shortcomings of the CT scan include the fact that the test requires ionizing radiation that inevitably carries a direct and indirect danger to the patient's health. In addition, the test requires more resources financially and in terms of manpower, which is a limitation in its use outside the hospital or in countries with poor socio-economic status.

In this study, the investigators aim to bridge the gap between these two types of techniques and create a uniform system that takes advantage of both. This is done by creating CT images from ultrasound images. This process will involve the use of artificial intelligence methods, namely machine learning algorithms.

Machine learning in general, and deep learning in particular, have gained momentum in recent years in the field of computer vision and more recently also in the field of medical imaging. In addition, one can already see significant successes in the classification of retinal diseases, in which the sensor is a fundus camera or Optical Coherence Tomography (OCT), in the classification of classifications In MRI imaging of the breast 3, and more recently, in the classification of the severity of the disease caused by the Corona virus, using chest X-rays and ultrasound scans.