Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

• Published in: World journal of gastroenterology : WJG Vol. 29; no. 9; pp. 1427 - 1445
• Main Authors: Berbís, M Alvaro, Paulano Godino, Felix, Royuela Del Val, Javier, Alcalá Mata, Lidia, Luna, Antonio
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 07.03.2023
• Subjects: Artificial Intelligence; Humans; Liver Diseases; Magnetic Resonance Imaging; Pancreas - diagnostic imaging; Review; Deep learning; Pancreas; Artificial intelligence; Imaging; Liver; Machine learning
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v29.i9.1427

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians' workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.