Background and Aim: Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. Methods: A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. Results: The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. Conclusion: A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy.

Background Implementation of personalized medicine requires the accessibility of tumor molecular profiling in order to allow prioritization of appropriate targeted therapies for individual patients. Our aim was to study the role of comprehensive genomic profiling assays that may inform treatment recommendations for patients with solid tumors. Materials and Methods We performed a prospective study to evaluate the feasibility of application of the FoundationOne CDx panel—which detects substitutions, insertions and deletions, and copy number alterations in 324 genes, select gene rearrangements, and genomic signatures including microsatellite instability and tumor mutation burden (TMB)—to patients with advanced or recurrent solid tumors before its approval in Japan. Results A total of 181 samples were processed for genomic testing between September 2018 and June 2019, with data being successfully obtained for 175 of these samples, yielding a success rate of 96.7%. The median turnaround time was 41 days (range, 21–126 days). The most common known or likely pathogenic variants were TP53 mutations (n = 113), PIK3CA mutations (n = 33), APC mutations (n = 32), and KRAS mutations (n = 29). Among the 153 patients assessed for TMB, the median TMB was 4 mutations/Mb, and tumors with a high TMB (≥10 mutations/Mb) were more prevalent for lung cancer (11/32) than for other solid tumor types (9/121, Fisher's exact test p < .01). No clear trend toward increased efficacy for immune checkpoint inhibitor (ICI) monotherapy or ICI combination chemotherapy in patients with a high programmed cell death–ligand 1 tumor proportion score or a high TMB was apparent. Among the 174 patients found to harbor known or likely pathogenic actionable alterations, 24 individuals (14%) received matched targeted therapy. Conclusion The FoundationOne CDx assay was performed with formalin‐fixed, paraffin‐embedded tumor specimens with a success rate of >95%. Such testing may inform the matching of patients with cancer with investigational or approved targeted drugs. Implications for Practice This prospective cohort study was initiated to investigate the feasibility and utility of clinical application of FoundationOne CDx. A total of 181 samples were processed for genomic testing between September 2018 and June 2019, with data being successfully obtained for 175 of these samples, yielding a success rate of 96.7%, and 24 individuals (14%) received matched targeted therapy.

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Background and Aim Contrast‐enhanced harmonic endoscopic ultrasonography (CH‐EUS) is useful for the diagnosis of lesions inside and outside the digestive tract. This study evaluated the value of artificial intelligence (AI) in the diagnosis of gastric submucosal tumors by CH‐EUS. Methods This retrospective study included 53 patients with gastrointestinal stromal tumors (GISTs) and leiomyomas, all of whom underwent CH‐EUS between June 2015 and February 2020. A novel technology, SiamMask, was used to track and trim the lesions in CH‐EUS videos. CH‐EUS was evaluated by AI using deep learning involving a residual neural network and leave‐one‐out cross‐validation. The diagnostic accuracy of AI in discriminating between GISTs and leiomyomas was assessed and compared with that of blind reading by two expert endosonographers. Results Of the 53 patients, 42 had GISTs and 11 had leiomyomas. Mean tumor size was 26.4 mm. The consistency rate of the segment range of the tumor image extracted by SiamMask and marked by the endosonographer was 96% with a Dice coefficient. The sensitivity, specificity, and accuracy of AI in diagnosing GIST were 90.5%, 90.9%, and 90.6%, respectively, whereas those of blind reading were 90.5%, 81.8%, and 88.7%, respectively (P = 0.683). The κ coefficient between the two reviewers was 0.713. Conclusions The diagnostic ability of CH‐EUS results evaluated by AI to distinguish between GISTs and leiomyomas was comparable with that of blind reading by expert endosonographers.

Abstract-On marginal winter nights, highway authorities face a difficult decision as to whether or not to salt the road network. The consequences of making a wrong decision are serious, as an untreated network is a major hazard. However, if salt is spread when it is not actually required, there are unnecessary financial and environmental consequences. In this paper, a new salting route optimisation system is proposed which combines Evolutionary Computation (EC) with the neXt generation Road Weather Information Systems (XRWIS). XRWIS is a new high resolution forecast system which predicts road surface temperature and condition across the road network over a 24 hour period. ECs are used to optimise a series of salting routes for winter gritting by considering XRWIS temperature data along with treatment vehicle and road network constraints. This synergy realises daily dynamic routing and it will yield considerable benefits for areas with a marginal ice problem.