Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.

Journal: PLOS digital health
Published Date: Jul 23, 2025

Abstract

Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n = 11,44%) and included less than 250 patients (n = 18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n = 9,36%) or risk stratification (n = 10,40%) rather than IPMN detection (n = 5,20%) or IPMN segmentation (n = 2,8%). This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.

Authors

  • Muhammad Ibtsaam Qadir
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States of America.
  • Jackson A Baril
    Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
  • Michele T Yip-Schneider
    Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
  • Duane Schonlau
    Department of Radiology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
  • Thi Thanh Thoa Tran
    Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
  • C Max Schmidt
  • Fiona R Kolbinger
    Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals