Objectives:Computer-aided detection (CADe) using deep learning is promising for reducing missed gastric cancers (MGCs) and supporting physicians in double-checking endoscopic images. We aimed to evaluate the CADe efficacy for MGCs after endoscopic submucosal dissection (ESD). Methods:We collected 2324 endoscopic images, including 60 of MGCs detected during surveillance esophagogastroduodenoscopy within 18 months after initial ESD. The performance of the CADe system, developed for early GC detection using deep learning, was compared with that of 10 endoscopists in a detection study using collected images. Per-lesion sensitivity, per-image detection performance, and diagnostic time were compared. Results:The per-lesion sensitivity for MGCs was 15.0% (9/60) and 13.8% (83/600) for CADe and endoscopist detection, respectively (p = 0.538). The respective per-image computer-aided and endoscopist performance sensitivity was 11.3% and 10.6% (p = 0.548), specificity 88.5% and 94.8% (p < 0.001), positive predictive value 4.4% and 8.3%, and negative predictive value 95.7% and 96.0%. The per-image CADe time was significantly shorter (0.03 s vs. 2.86 s, p < 0.001). CADe showed higher sensitivity in certain subgroups, although these findings should be interpreted cautiously given the small sample size. Conclusions:No significant difference in sensitivity was observed between CADe and endoscopist detection for MGCs after ESD, and the absolute sensitivity remained low. Further improvements are needed before clinical implementation of CADe as a double-checking tool. Trial Registration:The authors have confirmed clinical trial registration is not needed for this submission.
Self-weighing can be effective in obesity prevention; however, many individuals tend to procrastinate in adopting this behavior. Nudges can address this procrastination; however, concerns about their long-term durability and their effectiveness under conditions of transparency continue to impede their implementation. This study aimed to evaluate the one-year effects of a self-weighing promotion workshop incorporating disclosed nudges and health education through a randomized controlled trial. In September 2019, participants in a Japanese community-based workshop were assigned to either a negative-priming group or a positive-priming group. At the beginning, priming nudge-based interventions were implemented: the negative-priming group completed quizzes highlighting the disadvantages of obesity, whereas the positive-priming group shared success experiences and received positive feedback. Subsequently, all participants received the same health education and were asked to declare specific self-weighing plans, incorporating a commitment nudge. The modified intention-to-treat analysis was conducted among 62 and 51 participants in the negative-priming and positive-priming groups, respectively. Within-group comparisons showed that both groups had a significant increase in weekly self-weighing frequency after one year; by 1.06 measurements in the negative-priming group (p < 0.001, Cohen's d = 0.44) and by 1.17 measurements in the positive-priming group (p < 0.001, Cohen's d = 0.52). No significant differences were observed between the groups. The effect sizes observed in this study appear to be comparable to or larger than those reported in previous studies, even after one year. These findings suggest that combining health education with transparently disclosed nudges may support self-weighing over one year.
BackgroundThe automated classification of Helicobacter pylori infection status is gaining attention, distinguishing among uninfected (no history of H. pylori infection), current infection, and post-eradication. However, this classification has relatively low performance, primarily due to the intricate nature of the task. This study aims to develop a new multistage deep learning method for automatically classifying H. pylori infection status.MethodsThe proposed multistage deep learning method was developed using a training set of 538 subjects, then tested on a validation set of 146 subjects. The classification performance of this new method was compared with the findings of four physicians.ResultsThe accuracy of our method was 87.7%, 83.6%, and 95.9% for uninfected, post-eradication, and currently infected cases, respectively, whereas that of the physicians was 81.7%, 76.5%, and 90.3%, respectively. When including the patient's H. pylori eradication history information, the classification accuracy of the method was 92.5%, 91.1%, and 98.6% for uninfected, post-eradication, and currently infected cases, respectively, whereas that of the physicians was 85.6%, 85.1%, and 97.4%, respectively.ConclusionThe new multistage deep learning method shows potential for an innovative approach to gastric cancer screening. It can evaluate individual subjects' cancer risk based on endoscopic images and reduce the burden of physicians.
Procurement activities are generally assumed to be simple, routine tasks, such as managing deliveries and costs, and this has been in prior academic research as well. However, procurement also engages in strategic activities that affect the requirements and elements of business strategy considerations. Strategic procurement activities are innovation-driven in a way that triggers innovation by new materials and the products by using those new materials. This is the positive aspects of strategic procurement activities. However, the author encountered cases in which strategic procurement had a negative impact. The case is that of Japanese manufacturers in the solar panel industry. In the three Japanese manufacturers, the strategic activities of procurement contributed to the prolongation of their business without a fundamental review of their business strategy, with the effect of a certain level of cost reduction, while weakening the product differentiation that was originally at the core of each company's business strategy. In other words, strategic activities in procurement had a negative impact in the form of prolonging the life of ill-conceived business strategy. Thus, When the reverse functionality is occurring, it becomes necessary to suspend strategic procurement activities implemented in a partially-optimal manner, and to fundamentally review the business strategy itself.
