Prospects of using computer vision technology to detect urinary stones and liver and kidney neoplasms on computed tomography images of the abdomen and retroperitoneal space

Abstract

Radiology has undergone several significant changes in recent years. Technologies based on computer vision are being actively introduced and allow improving and accelerating the diagnosis of many diseases, as well as reducing the burden on medical personnel. At the same time, these technologies have already proven their effectiveness in routine practice in the analysis of X-ray studies of the mammary glands and chest organs. Also recently, solutions have appeared for the search, qualitative and quantitative evaluation of such common pathologies as urolithiasis and volumetric formations in the parenchyma of the liver and kidneys with a sufficiently high accuracy.
 Currently, there are many different architectures of deep learning networks and computer vision algorithms that allow identifying and classify the pathology of the abdominal organs. At the same time, all models can be divided into algorithms that distinguish (segment) pathology and algorithms that allow classification of the pathology of the abdominal organs.
 This review evaluates the existing computer vision algorithms used in computed tomography of the abdominal organs, determines the main directions of their development, and provides prospects for application in medical organizations.