Background and Aim: It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer‐aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection.Methods: The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP‐positive and ‐negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post‐eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis.Results: Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system.Conclusions: The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post‐eradication patients. By learning more images and considering a diagnosis algorithm for post‐eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.

INF-β has been widely used to treat patients with multiple sclerosis (MS) in relapse. Accurate prediction of treatment response is important for effective personalization of treatment. Microarray data have been frequently used to discover new genes and to predict treatment responses. However, conventional analytical methods suffer from three difficulties: high-dimensionality of datasets; high degree of multi-collinearity; and achieving gene identification in time-course data. The use of Elastic net, a sparse modelling method, would decrease the first two issues; however, Elastic net is currently unable to solve these three issues simultaneously. Here, we improved Elastic net to accommodate time-course data analyses. Numerical experiments were conducted using two time-course microarray datasets derived from peripheral blood mononuclear cells collected from patients with MS. The proposed methods successfully identified genes showing a high predictive ability for INF-β treatment response. Bootstrap sampling resulted in an 81% and 78% accuracy for each dataset, which was significantly higher than the 71% and 73% accuracy obtained using conventional methods. Our methods selected genes showing consistent differentiation throughout all time-courses. These genes are expected to provide new predictive biomarkers that can influence INF-β treatment for MS patients.

This study examines the effects of focused-attention meditation on functional brain states in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.

Introduction: Many people spend a considerable amount of time performing intellectual activities within auditory environments that affect work efficiency. To investigate auditory environments that improve working efficiency, we investigated the relationship between brain activity and performance of the number memory task in environments with and without white noise using functional near‐infrared spectroscopy (fNIRS).Methods: Twenty‐nine healthy subjects (aged 21.9 ± 1.4 years) performed the number memory task in both the white noise and silent environments. Cerebral blood flow changes during the task were measured using an ETG‐7100 fNIRS system (Hitachi, Ltd., Tokyo, Japan). The psychological states of the subjects were also estimated by subjective ratings of the pleasantness of the auditory environment. Then, they were divided into three groups based on their task scores. The differences in the cerebral blood flow (CBF) changes, functional connection strength, and the subjects’ feelings of pleasantness to the noise between the subject groups were analyzed and discussed.Results: The first group felt that the white noise was pleasant, which strengthened the bilateral functional connections between the brain regions related to the memory task. Therefore, the subjects’ task performance improved in the white noise environment. Although the second group felt that the white noise was uncomfortable, the frontal regions related to attention control were more activated in the white noise environment to sustain the task performance in the noisy environment. The third group felt that the white noise was unpleasant, and their CBF decreased in that environment, which was associated with deteriorated task performance.Conclusions: Task performance was closely related to the subjects’ feelings of pleasantness to the noise. The results of the analysis of the CBF changes and functional connectivity suggested that the effects of the white noise on brain activity differed among the three groups.