Kenichi Takeda

Double staining with crystal violet and methylene blue is appropriate for colonic endocytoscopy: An in vivo prospective pilot study

Endocytoscopy (EC) at ultra‐high magnification enables in vivo visualization of cellular atypia of gastrointestinal mucosae. Clear images are essential for precise diagnosis by EC. The aim of the present study was to evaluate the optimal staining method for EC in the colon.

Computer-aided diagnosis for colonoscopy

Several studies have shown that colonoscopy is associated with a reduction in colorectal cancer mortality. This benefit is based on the detection and resection of any neoplastic polyps; however, polyps can be missed during screening colonoscopy and endoscopists may not be able to differentiate between neoplastic and non-neoplastic polyps. Polyp miss rates as high as 20 % have been reported for high definition resolution colonoscopy 1 , while a large prospective trial of optical biopsy of small colon polyps using narrow-band imaging (NBI) showed that the accuracy of physicians was only 80 % in diagnosing detected polyps as adenomas, even after a physician training program 2 . To overcome these limitations, computer-aided diagnosis (CAD) is attracting more attention because it may help endoscopists to avoid missing and mischaracterizing polyps. CAD for colonoscopy is generally designed to extract various features from a colonoscopic image/movie and output the predicted polyp location or pathology based on machine learning. The term “machine learning” refers to a fundamental function of artificial intelligence, whereby a computer can be trained to learn (in this case, recognize or characterize polyps) through repetition and experience (exposure to a large number of annotated polyp images). Ideally, the output of CAD is expressed in real time on the monitor, immediately assisting the endoscopist’s decision-making.

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts

PurposeReal-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists.MethodsWe developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated.ResultsECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%;

Evaluation of microvascular findings of deeply invasive colorectal cancer by endocytoscopy with narrow-band imaging

P=0.01

Endocytoscopic findings of colorectal neuroendocrine tumors (with video)

P=0.01), but similar to experts (87.8 vs 84.2%;

Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience

P=0.76