Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign.

Journal: Reproduction & fertility
PMID: 35118401

Abstract

OBJECTIVES: Pouch of Douglas (POD) obliteration is a severe consequence of inflammation in the pelvis, often seen in patients with endometriosis. The sliding sign is a dynamic transvaginal ultrasound (TVS) test that can diagnose POD obliteration. We aimed to develop a deep learning (DL) model to automatically classify the state of the POD using recorded videos depicting the sliding sign test.

Authors

  • Gabriel Maicas
    Australian Institute for Machine Learning, The University of Adelaide, Australia. Electronic address: gabriel.maicas@adelaide.edu.au.
  • Mathew Leonardi
    Robinson Research Institute University of Adelaide Adelaide South Australia Australia.
  • Jodie Avery
    Robinson Research Institute University of Adelaide Adelaide South Australia Australia.
  • Catrina Panuccio
    Specialist Imaging Partners, North Adelaide, Australia.
  • Gustavo Carneiro
    Australian Centre for Visual Technologies, The University of Adelaide, Australia. Electronic address: gustavo.carneiro@adelaide.edu.au.
  • M Louise Hull
    Robinson Research Institute University of Adelaide Adelaide South Australia Australia.
  • George Condous
    Robinson Research Institute University of Adelaide Adelaide South Australia Australia.

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