The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics.

Journal: The Urologic clinics of North America
Published Date:

Abstract

The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.

Authors

  • Masatomo Kaneko
    Department of Urology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
  • Vasileios Magoulianitis
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Lorenzo Storino Ramacciotti
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer.
  • Alex Raman
    Western University of Health Sciences. Pomona, CA, USA.
  • Divyangi Paralkar
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer.
  • Andrew Chen
    University of Illinois at Urbana-Champaign, Department of Computer Science.
  • Timothy N Chu
    Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA.
  • Yijing Yang
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Jintang Xue
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Jiaxin Yang
    Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Jinyuan Liu
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Donya S Jadvar
    Dornsife School of Letters and Science, University of Southern California, Los Angeles, CA, USA.
  • Karanvir Gill
    Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Giovanni E Cacciamani
    USC Institute of Urology, Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA - giovanni.cacciamani@med.usc.edu.
  • Chrysostomos L Nikias
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Vinay Duddalwar
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • C-C Jay Kuo
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Inderbir S Gill
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California.
  • Andre Luis Abreu
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: andre.abreu@med.usc.edu.