ABSTRACT
Ultrasound examination has the advantages of non-radiation, non-invasive, low cost and high efficiency, and is the most commonly used method of liver imaging examination. In recent years, the application of computer vision technology to the intelligent analysis of ultrasound images has become a research hotspot in the field of intelligent healthcare. Through large-scale data training, the intelligent analysis model of ultrasound omics based on machine learning algorithm can assist clinical diagnosis and therapy, and improve the efficiency and accuracy of diagnosis. Based on the literature, the authors summarize the application proprect of computer vision technology assisted ultrasonography in the evaluation of diffuse liver lesions, focal liver lesions, microvascular invasion of liver cancer, postoperative recurrence of liver cancer, and postoperative therapy response to trans-catheter arterial chemoembolization.
ABSTRACT
In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.
ABSTRACT
Deep learning is a process in which machine learning obtains new knowledge and skills by simulating the learning behavior of human brain through massive data training and analysis. With the development of medical technology, a large amount of data has been accumulated in the medical field, and the research on data may help to understand the relationships and rules within data and predict the onset and prognosis of human diseases. Deep learning can find the hidden information in data and has been increasingly used in the medical field. Primary liver cancer is a malignant tumor with high incidence and mortality rates, poor prognosis, and a high recurrence rate, and early diagnosis, timely treatment, and prediction of recurrence have always been the research hotspots in recent years. This article reviews the advances in the application of deep learning in the diagnosis and recurrence of liver cancer from the aspects of risk prediction, postoperative recurrence, and survival risk prediction.
ABSTRACT
Bioinformatics is an interdisciplinary science that combines the tools of mathematics, computer science, and biology to clarify and explore the biological implications of large amounts of biological data. With the continuous development of genome sequencing technology, a large number of biological data has been generated, and mining of the biological significance contained in big data has become one of the main tasks that need to be solved urgently. This article summarizes the risk prediction models for hepatocellular carcinoma (HCC) based on feature genes, so as to provide new perspectives for early identification, prognosis, and treatment optimization of HCC.