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The presence of perianal fistulae constitutes a more severe phenotype of Crohn's disease (CD) that often requires intensive medical therapy, wound care, and surgical intervention. Despite therapeutic advances in inflammatory bowel disease, the treatment of perianal fistulae remains challenging. Hyperbaric oxygen therapy (HBOT) has been proposed as an adjunctive treatment modality for induction of fistula healing. We illustrate a case in which HBOT achieved fistula healing in a young patient with severe refractory perianal Crohn's disease (pCD). We also review the current literature and discuss the role of HBOT in the treatment armamentarium of pCD.
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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. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29). RESULTS: Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding. CONCLUSION: Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.
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Endoscopía Capsular , Aprendizaje Profundo , Humanos , Endoscopía Capsular/métodos , Proyectos Piloto , Singapur , Redes Neurales de la ComputaciónRESUMEN
Cronkhite-Canada syndrome (CCS) is a rare nonhereditary gastrointestinal polyposis syndrome. We illustrate a case with clinical presentation of dysgeusia, chronic diarrhea and weight loss, and endoscopic features of diffuse gastric mucosa nodularity with circumferential nodular pancolitis and a solitary colonic polyp initially mimicking inflammatory bowel disease. After multidisciplinary discussion, the diagnosis of CCS was made. The patient received steroids with resultant clinical, endoscopic, and histological improvement. We discuss the treatment and risk of neoplasia in CCS.