Clinical evaluation of a real-time artificial intelligence-based polyp detection system: a US multi-center pilot study.

Artificial intelligence (AI) has increasingly been employed in multiple fields, and there has been significant interest in its use within gastrointestinal endoscopy. Computer-aided detection (CAD) can potentially improve polyp detection rates and decrease miss rates in colonoscopy. However, few clinical studies have evaluated real-time CAD during colonoscopy. In this study, we analyze the efficacy of a novel real-time CAD system during colonoscopy. This was a single-arm prospective study of patients undergoing colonoscopy with a real-time CAD system. This AI-based system had previously been trained using manually labeled colonoscopy videos to help detect neoplastic polyps (adenomas and serrated polyps). In this pilot study, 300 patients at two centers underwent elective colonoscopy with the CAD system. These results were compared to 300 historical controls consisting of consecutive colonoscopies performed by the participating endoscopists within 12 months prior to onset of the study without the aid of CAD. The primary outcome was the mean number of adenomas per colonoscopy. Use of real-time CAD trended towards increased adenoma detection (1.35 vs 1.07, p = 0.099) per colonoscopy though this did not achieve statistical significance. Compared to historical controls, use of CAD demonstrated a trend towards increased identification of serrated polyps (0.15 vs 0.07) and all neoplastic (adenomatous and serrated) polyps (1.50 vs 1.14) per procedure. There were significantly more non-neoplastic polyps detected with CAD (1.08 vs 0.57, p < 0.0001). There was no difference in ≥ 10 mm polyps identified between the two groups. A real-time CAD system can increase detection of adenomas and serrated polyps during colonoscopy in comparison to historical controls without CAD, though this was not statistically significant. As this pilot study is underpowered, given the findings we recommend pursuing a larger randomized controlled trial to further evaluate the benefits of CAD.

A prospective study of the diagnostic accuracy of PillCam ESO esophageal capsule endoscopy versus conventional upper endoscopy in patients with chronic gastroesophageal reflux diseases.

INTRODUCTION: Endoscopy is commonly performed to evaluate for suspected or established esophageal diseases including gastroesophageal reflux disease (GERD) and its complications. The newly developed PillCam ESO Esophageal Capsule offers an alternative approach to visualize the esophagus and to evaluate patients with suspected esophageal disease. AIM: Compare the accuracy (specificity, sensitivity, positive predictive value [PPV], and negative predictive value [NPV]) of esophageal capsule endoscopy (ECE) compared with esophagogastroduodenoscopy (EGD) in evaluating patients with GERD. METHODS: A multicenter pivotal trial was conducted at seven sites. The PillCam ESO esophageal capsule is similar to the standard capsule endoscope used for the small bowel but acquires video images from both ends of the device at 2 frames/second/end. A total of 106 patients (93 GERD; 13 Barrett) underwent ECE followed by EGD. ECE videos were evaluated by an investigator blinded to EGD findings. A blinded adjudication committee reviewed all discrepant findings between ECE and EGD. RESULTS: Sixty-six of 106 patients had positive esophageal findings, ECE identified esophageal abnormalities in 61 (sensitivity, 92%; specificity, 95%). The per-protocol sensitivity, specificity, PPV, and NPV of ECE for Barrett esophagus were 97%, 99%, 97%, and 99%, respectively, and for esophagitis 89%, 99%, 97%, and 94%, respectively. ECE was preferred over EGD by all patients. There were no adverse events related to ECE. CONCLUSIONS: ECE is a convenient and sensitive method for visualization of esophageal mucosal pathology and may provide an effective method to evaluate patients for esophageal disease.