Artificial Intelligence and Whole Slide Imaging Assist in Thyroid Indeterminate Cytology: A Systematic Review.

Journal: Acta cytologica
PMID: 39746329

Abstract

INTRODUCTION: Thyroid cytopathology, particularly in cases of atypia of undetermined significance/follicular lesions of undetermined significance (AUS/FLUS), suffers from suboptimal sensitivity and specificity challenges. Recent advancements in digital pathology and artificial intelligence (AI) hold promise for enhancing diagnostic accuracy. This systematic review included studies that focused on diagnostic accuracy in AUS/FLUS cases using AI, whole slide imaging (WSI), or both.

Authors

  • Olia Poursina
    Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA.
  • Azadeh Khayyat
    Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Sara Maleki
    Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA.
  • Ali Amin
    Department of Pathology and Laboratory Medicine, The Miriam Hospital, Providence, RI, USA; Warren Alpert Medical School of Brown University, Providence, RI, USA.

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