Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.

Introduction: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities.

Methods: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively.

Colonic stenting in acute malignant large bowel obstruction: audit of efficacy and safety in a Singapore tertiary referral centre.

Introduction: Acute malignant large bowel obstruction (MBO) occurs in 8%-15% of colorectal cancer patients. Self-expandable metal stents (SEMS) have progressed from a palliative modality to use as bridge to surgery (BTS). We aimed to assess the safety and efficacy of SEMS for MBO in our institution.