No code machine learning: validating the approach on use-case for classifying clavicle fractures.

Journal: Clinical imaging
PMID: 38838448

Abstract

PURPOSE: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures.

Authors

  • Giridhar Dasegowda
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Data Science Office, Mass General Brigham, Boston, Massachusetts.
  • James Yuichi Sato
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA.
  • Daniel C Elton
    Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bldg. 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182.
  • Emiliano Garza-Frias
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
  • Thomas Schultz
    B-IT and Department of Computer Science, University of Bonn, 53115, Bonn, Germany. schultz@cs.uni-bonn.de.
  • Christopher P Bridge
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
  • Bernardo C Bizzo
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Mass General Brigham Data Science Office (DSO), Boston, MA, United States.
  • Mannudeep K Kalra
  • Keith J Dreyer
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Mass General Brigham Data Science Office (DSO), Boston, MA, United States.

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