Background: Ultrasound has been demonstrated to accurately diagnose rectal deep endometriosis (DE) and pouch of Douglas (POD) obliteration. The role of ultrasound in the assessment of patients who have undergone surgery for rectal DE and POD obliteration has not been evaluated.

Aim: To describe the transvaginal ultrasound (TVS) findings of patients who have undergone rectal surgery for DE.

Materials And Methods: An observational cross-sectional study at a tertiary care centre in Sydney, Australia between January and April 2017. Patients previously treated for rectal DE (low anterior resection vs rectal shaving/disc excision) were recruited and asked to complete a questionnaire on their current symptoms. On TVS, POD state and rectal DE were assessed. Correlating recurrence of POD obliteration and/or rectal DE to surgery type and symptoms was done.

Results: Fifty-six patients were contacted; 22/56 (39.3%) attended for the study visit. Average interval of surgery to study visit was 52.8 ± 24.6 months. Surgery type breakdown was as follows: low anterior resection (56%) and rectal shaving/disc excision (44%). The prevalence of POD obliteration was 16/22 (72.7%) intraoperatively and 8/22 (36.4%) at study visit, as per the sliding sign. Nine patients (39.1%) had evidence on TVS of recurrent rectal DE. Recurrence of POD obliteration and rectal DE was not associated with surgery type or symptomatology.

Conclusion: Despite surgery for rectal DE, many patients have a negative sliding sign on TVS, representing POD obliteration, and rectal DE. Our numbers are too small to correlate with the surgery type or their current symptoms.

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http://dx.doi.org/10.1111/ajo.13112DOI Listing

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Article Synopsis
  • - Endometriosis, affecting about 10% of individuals assigned female at birth, is difficult to diagnose and often requires surgical or MRI imaging methods, with MRI being quicker but less precise.
  • - A significant diagnostic indicator is the obliteration of the Pouch of Douglas, yet even experienced clinicians find it hard to accurately classify this from MRI images, which makes training reliable AI models a challenge.
  • - The paper introduces the HAICOMM methodology, which combines multi-rater and multi-modal learning with human-AI collaboration, leading to more accurate diagnoses of endometriosis compared to traditional methods and improving classification accuracy.
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