RÉSUMÉ
With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.
RÉSUMÉ
Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans' errors and limited capabilities by bringing more accuracy, consistency, and higher speed, making endoscopic procedures more efficient and of higher quality. AI showed great results in diagnostic and therapeutic endoscopy in all parts of the GI tract. More studies are still needed before the introduction of this new technology in our daily practice and clinical guidelines. Furthermore, ethical clearance and new legislations might be needed. In conclusion, the introduction of AI will be a big breakthrough in the field of GI endoscopy in the upcoming years. It has the potential to bring major improvements to GI endoscopy at all levels.
RÉSUMÉ
BACKGROUND/AIMS: A flexible spectral imaging color enhancement system was installed in new capsule software for video capsule endoscopy. Contrast image capsule endoscopy (CICE) is a novel technology using light-emitting diodes selected for the main absorption range of hemoglobin. We assessed the feasibility and diagnostic effi cacy for small bowel surveillance in patients with polyposis syndromes. METHODS: Six patients with polyposis syndromes, four with familial adenomatous polyposis and one each with Cowden syndrome (CS) and Cronkhite-Canada syndrome (CCS) were examined using CICE. We conducted three evaluations to assess the effect on the numbers of the detected polyps; compare polyp diagnostic rates between adenoma and hamartoma; and assess polyp visibility. RESULTS: The numbers of detected polyps and diagnostic accuracy did not differ signifi cantly between pre-contrast and contrast images. However, 50% of the adenomatous polyps displayed enhanced visibility on contrast images. CICE contrast images exhibited clearly demarcated lesions and improved the visibility of minute structures of adenomatous polyps. Hamartomatous polyp micro-structures in patients with CS and CCS were more clearly visualized on contrast than pre-contrast images. CONCLUSIONS: CICE is an effective tool for enhancing the visibility of polyps in patients with polyposis syndrome.