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Objectives: Ulcerative colitis‐associated neoplasias (UCAN) are often flat with an indistinct boundary from surrounding tissues, which makes differentiating UCAN from non‐neoplasias difficult. Pit pattern (PIT) has been reported as one of the most effective indicators to identify UCAN. However, regenerated mucosa is also often diagnosed as a neoplastic PIT. Endocytoscopy (EC) allows visualization of cell nuclei. The aim of this retrospective study was to demonstrate the diagnostic ability of combined EC irregularly‐formed nuclei with PIT (EC‐IN‐PIT) diagnosis to identify UCAN. Methods: This study involved patients with ulcerative colitis whose lesions were observed by EC. Each lesion was diagnosed by two independent expert endoscopists, using two types of diagnostic strategies: PIT alone and EC‐IN‐PIT. We evaluated and compared the diagnostic abilities of PIT alone and EC‐IN‐PIT. We also examined the difference in the diagnostic abilities of an EC‐IN‐PIT diagnosis according to endoscopic inflammation severity. Results: We analyzed 103 lesions from 62 patients; 23 lesions were UCAN and 80 were non‐neoplastic. EC‐IN‐PIT diagnosis had a significantly higher specificity and accuracy compared with PIT alone: 84% versus 58% (P < 0.001), and 88% versus 67% (P < 0.01), respectively. The specificity and accuracy were significantly higher for Mayo endoscopic score (MES) 0–1 than MES 2–3: 93% versus 68% (P < 0.001) and 95% versus 74% (P < 0.001), respectively. Conclusions: Our novel EC‐IN‐PIT strategy had a better diagnostic ability than PIT alone to predict UCAN from suspected and initially detected lesions using conventional colonoscopy. UMIN clinical trial (UMIN000040698). [ABSTRACT FROM AUTHOR]

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one‐fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non‐experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence‐assisted computer‐aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep‐learning, data‐driven machine learning techniques have made the development of computer‐aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer‐aided diagnosis by colonoscopy. To date, computer‐aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer‐aided characterization systems may have the potential to improve diagnostic accuracy in real‐time clinical practice. Furthermore, some artificial intelligence‐assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer‐aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real‐time clinical decision‐making. In this review, we have focused on computer‐aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects. [ABSTRACT FROM AUTHOR]

Background: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost.Objective: To evaluate the performance of real-time CAD with endocytoscopes (×520 ultramagnifying colonoscopes providing microvascular and cellular visualization of colorectal polyps after application of the narrow-band imaging [NBI] and methylene blue staining modes, respectively).Design: Single-group, open-label, prospective study. (UMIN [University hospital Medical Information Network] Clinical Trial Registry: UMIN000027360).Setting: University hospital.Participants: 791 consecutive patients undergoing colonoscopy and 23 endoscopists.Intervention: Real-time use of CAD during colonoscopy.Measurements: CAD-predicted pathology (neoplastic or nonneoplastic) of detected diminutive polyps (≤5 mm) on the basis of real-time outputs compared with pathologic diagnosis of the resected specimen (gold standard). The primary end point was whether CAD with the stained mode produced a negative predictive value (NPV) of 90% or greater for identifying diminutive rectosigmoid adenomas, the threshold required to "diagnose-and-leave" nonneoplastic polyps. Best- and worst-case scenarios assumed that polyps lacking either CAD diagnosis or pathology were true- or false-positive or true- or false-negative, respectively.Results: Overall, 466 diminutive (including 250 rectosigmoid) polyps from 325 patients were assessed by CAD, with a pathologic prediction rate of 98.1% (457 of 466). The NPVs of CAD for diminutive rectosigmoid adenomas were 96.4% (95% CI, 91.8% to 98.8%) (best-case scenario) and 93.7% (CI, 88.3% to 97.1%) (worst-case scenario) with stained mode and 96.5% (CI, 92.1% to 98.9%) (best-case scenario) and 95.2% (CI, 90.3% to 98.0%) (worst-case scenario) with NBI.Limitation: Two thirds of the colonoscopies were conducted by experts who had each experienced more than 200 endocytoscopies; 186 polyps not assessed by CAD were excluded.Conclusion: Real-time CAD can achieve the performance level required for a diagnose-and-leave strategy for diminutive, nonneoplastic rectosigmoid polyps.Primary Funding Source: Japan Society for the Promotion of Science. [ABSTRACT FROM AUTHOR]