Application of deep learning models for accurate classification of fluid collections in acute necrotizing pancreatitis on computed tomography: a multicenter study.

Journal: Abdominal radiology (New York)
Published Date: Sep 30, 2024

Abstract

PURPOSE: To apply CT-based deep learning (DL) models for accurate solid debris-based classification of pancreatic fluid collections (PFC) in acute pancreatitis (AP).

Authors

  • Pankaj Gupta
    Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
  • Ruby Siddiqui
    Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Shravya Singh
    Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Nikita Pradhan
    Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Jimil Shah
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Jayanta Samanta
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
  • Vaneet Jearth
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Anupam Singh
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Harshal Mandavdhare
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
  • Vishal Sharma
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
  • Amar Mukund
    Department of Interventional Radiology, Institute of Liver and Biliary Science, New Delhi, India.
  • Chhagan Lal Birda
    Department of Gastroenterology, All India Institute of Medical Sciences, Jodhpur, India.
  • Ishan Kumar
    Department of Radiodiagnosis and Imaging, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India.
  • Niraj Kumar
    Department of Interventional Radiology, Institute of Liver and Biliary Science, New Delhi, India.
  • Yashwant Patidar
    Department of Interventional Radiology, Institute of Liver and Biliary Science, New Delhi, India.
  • Ashish Agarwal
    Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, 30013, Taiwan.
  • Taruna Yadav
    Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India.
  • Binit Sureka
    Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India.
  • Anurag Tiwari
    Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India.
  • Ashish Verma
    Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Ashish Kumar
    Department of pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER-Raebareli), Lucknow, India.
  • Saroj K Sinha
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
  • Usha Dutta
    Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.

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