Artificial Intelligence in Pancreatic Imaging: A Systematic Review

Abstract

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

Keywords: artificial intelligence; deep learning; endoscopic ultrasound; machine learning; pancreatic ductal adenocarcinoma.

FIGURE 1

The process of study inclusion based on PRISMA methodology.

FIGURE 2

A schematic drawing showing how a CNN model is analyzing pancreatic imaging data.

FIGURE 3

Situations representing different DICE scores: (a) No Overlap (DICE = 0) > the two areas do not overlap at all; (b) Partial Overlap (DICE < 1) > the circles overlap to some extent, leading to a DICE score between 0 and 1; (c) Complete Overlap (DICE = 1) > The circles perfectly overlap, resulting in a maximum DICE score of 1. The Hausdorff distance that indicates the maximum distance between the two sets as measured from their boundaries is also represented (red arrow).

FIGURE 4

AI clinical applications for pancreatic diseases.