Abstract
Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine‐learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
Highlights
THE ARTIFICIAL INTELLIGENCE (AI) health market is growing explosively to a market size of $6.6 billion, with a compound annual growth rate of 40%.1 AI techniques are emerging, especially in imaging-based specialties like radiology and gastroenterology
The results showed that the artificial neural network (ANN) significantly outperformed the logistic regression (LR) modeling in predicting the occurrence of several complications during the course of the disease in all three studies.[17,18,19]
IN THIS REVIEW, we showed that AI applications for pancreatic diseases are rapidly evolving
Summary
THE ARTIFICIAL INTELLIGENCE (AI) health market is growing explosively to a market size of $6.6 billion, with a compound annual growth rate of 40%.1 AI techniques are emerging, especially in imaging-based specialties like radiology and gastroenterology. The model had a sensitivity and specificity of 75% and 78% for recognizing high grade dysplasia or cancer These results were comparable to an experienced radiologist following current guidelines, but the DL model performed the task in only 1.82 seconds.[36] Chakraborthy et al.[37] developed a ML model incorporating clinical and imaging features to predict high- or low-risk branch-duct (BD)-IPMNs and reported a sensitivity of 80% with a specificity of 59%. Comparable results were found in a ML model that was trained to identify and classify PDAC on PET–CT images of 80 cases and healthy controls, reaching a detection accuracy of 96.5%.44 These studies only included images of normal pancreases and PDAC, while, in particular, the differentiation between diverse pancreatic lesions can be challenging. In the test-set, the model reached an accuracy of 81.1% with an AUC of 0.89.70
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