Attention-based image segmentation and classification model for the preoperative risk stratification of thyroid nodules.

Journal: World journal of surgery
Published Date: Dec 29, 2024

Abstract

BACKGROUND: Despite widespread use of standardized classification systems, risk stratification of thyroid nodules is nuanced and often requires diagnostic surgery. Genomic sequencing is available for this dilemma however, costs and access restricts global applicability. Artificial intelligence (AI) has the potential to overcome this issue nevertheless, the need for black-box interpretability is pertinent. We aimed to create an ultrasonographic segmentation and classification model that offers explainability and risk accountability.

Authors

  • Karishma Jassal
    Monash University Endocrine Surgery Unit, Department of General Surgery, Alfred Hospital, Melbourne, Victoria, Australia.
  • Bruno Di Muzio
    Department of Radiology, Alfred Hospital, Melbourne, Victoria, Australia.
  • Melissa Edwards
    Department of Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
  • Wendy Brown
    Department of Surgery, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
  • Jonathan Serpell
    Monash University Endocrine Surgery Unit, Department of General Surgery, Alfred Hospital, Melbourne, Victoria, Australia.
  • Afsaneh Koohestani
    Monash University Endocrine Surgery Unit, Department of General Surgery, Alfred Hospital, Melbourne, Victoria, Australia.
  • James C Lee
    Monash University Endocrine Surgery Unit, Department of General Surgery, Alfred Hospital, Melbourne, Victoria, Australia.

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