A comparative evaluation of three consecutive artificial intelligence algorithms released by Techcyte for identification of blasts and white blood cells in abnormal peripheral blood films.

Journal: International journal of laboratory hematology
PMID: 37786915

Abstract

INTRODUCTION: Digital pathology artificial intelligence (AI) platforms have the capacity to improve over time through "deep machine learning." We have previously reported on the accuracy of peripheral white blood cell (WBC) differential and blast identification by Techcyte (Techcyte, Inc., Orem, UT, USA), a digital scanner-agnostic web-based system for blood film reporting. The aim of the current study was to compare AI protocols released over time to assess improvement in cell identification.

Authors

  • Lisa F Lincz
    Haematology Department, Calvary Mater Newcastle, Waratah, New South Wales, Australia.
  • Karan Makhija
    Haematology Department, Calvary Mater Newcastle, Waratah, New South Wales, Australia.
  • Khaled Attalla
    Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia.
  • Fiona E Scorgie
    Haematology Department, Calvary Mater Newcastle, Waratah, New South Wales, Australia.
  • Anoop K Enjeti
    Haematology Department, Calvary Mater Newcastle, Waratah, New South Wales, Australia.
  • Ritam Prasad
    Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia.

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