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

Abstract

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.

Keywords: Artificial intelligence, Machine learning, Deep learning, Imaging, Liver, Pancreas

Core Tip: The gastroenterology field is changing with the application of artificial intelligence (AI) solutions capable of assisting and even automating the interpretation of radiological images (ultrasound, endoscopic ultrasound, computerized tomography, magnetic resonance imaging, and positron emission tomography), generating accurate and reproducible diagnoses. AI can further be applied to other steps of the radiological workflow beyond image interpretation, including test selection, image quality improvement, acceleration of image acquisition, and prediction of patient prognosis and outcome. We herein discuss the current evidence, challenges, and future directions on the application of AI to hepatic and pancreatic radiology.