Predicting the Recurrence of Sporadic Odontogenic Keratocyst Using Whole-Slide Histopathology Images With the Hybrid Encoder Iterative Attention Convolution Model.

Journal: Clinical and experimental dental research
Published Date: Aug 1, 2025

Abstract

OBJECTIVES: Odontogenic keratocysts (OKCs) are challenging due to their aggressiveness and high recurrence rates, complicating decision-making for clinicians and pathologists. Despite efforts to identify predictive characteristics, management remains challenging. The study aims to design a reliable artificial intelligence model to enhance predictive models and distinguish between recurrent and nonrecurrent whole-slide images of OKCs.

Authors

  • Samahit Mohanty
    Department of Computer Science and Engineering, Faculty of Engineering and Technology, M.S. Ramaiah University of Applied Sciences, Bangalore, India.
  • Divya Biligere Shivanna
    Department of Computer Science and Engineering, Faculty of Engineering and Technology, M.S. Ramaiah University of Applied Sciences, Bangalore, India.
  • Roopa S Rao
    Professor and Head Department of Oral Pathology and Microbiology Faculty of Dental Sciences MS Ramaiah University of Applied Sciences Bengaluru, Karnataka, India.
  • Madhusudan Astekar
    Department of Oral and Maxillofacial Pathology, Institute of Dental Sciences, Bareilly International University, Bareilly, Uttar Pradesh, India.

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