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A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps
Consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate. Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses. Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.
The primary endoscopist conducted the colonoscopic examination in the usual manner. All colonoscopy procedures were performed with high-definition colonoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). The colonoscopy was first advanced to the cecum in all patients as confirmed by identification of the appendiceal orifice and ileocecal valve or by intubation of the ileum. After cecal intubation, the colonoscopy was slowly withdrawn to the rectum by the primary endoscopist. The AI real time detection was then activated with the output displayed in a different monitor and was only viewed by an independent investigator, who was an experienced endoscopist. The primary endoscopist was blinded to the AI real time detection result al.
The colon was divided into three segments during the examination: right side, transverse and left side colon, using hepatic flexure and splenic flexure as dividing landmark. All polyps were marked for size (measured with biopsy forceps), location and morphology according to the Paris classification, and then removed or biopsied for histological examination. After examination of each segment, segmental unblinding of the AI results were provided by the independent viewer. If additional polyps were detected by AI but not by the endoscopist, that segment were reexamined to look for the missed polyp. If no additional polyp was detected by the AI, the next colonic segment was examined. Missed lesions were defined as lesions identified by AI and then confirmed on reexamination by the endoscopist.
The first withdrawal time (minus the polypectomy site) was measured. The Boston Bowel Preparation Scale score (BPPS) was used for evaluation of bowel cleanliness.
Artificial intelligence-Assisted real time colonoscopy
Queen Mary Hospital
The University of Hong Kong
Published on BioPortfolio: 2020-01-21T11:32:40-0500
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The study and implementation of techniques and methods for designing computer systems to perform functions normally associated with human intelligence, such as understanding language, learning, reasoning, problem solving, etc.
Ultrasonography applying the Doppler effect combined with real-time imaging. The real-time image is created by rapid movement of the ultrasound beam. A powerful advantage of this technique is the ability to estimate the velocity of flow from the Doppler shift frequency.
Surgical procedures conducted with the aid of computers. This is most frequently used in orthopedic and laparoscopic surgery for implant placement and instrument guidance. Image-guided surgery interactively combines prior CT scans or MRI images with real-time video.
Endoscopic examination, therapy or surgery of the luminal surface of the colon.