川平 洋

Surgeons have increased physical stress during laparoscopic surgery due to operative site constraints. We developed a wearable device to reduce the physical stress on surgeons' lower extremities. The device mechanically facilitates maintaining a near-upright posture. The surgeon's knees are gently bent by a knee-joint locking mechanism, and fixing and releasing are performed independently on each side. The subjects were one female and two male surgeons, who wore the device during laparoscopic inguinal hernia repair or high anterior resection. Surface electromyogram (EMG) was conducted for both iliopsoas muscles. Control values were determined with the subject not wearing the device. Participants completed a post-procedure questionnaire. With the device, EMG activity had a tendency to decrease in the left iliopsoas muscle (P = .055), but it changed little on the right (P = .406). The post-procedure questionnaire showed an overall positive impression, although subjects reported some difficulty walking. This device decreases EMG activity and may improve a surgeon's work environment.

BACKGROUND: Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer persists in patients with advanced mucosal atrophy and/or intestinal metaplasia. This study aimed to develop a computer-aided diagnosis (CAD) system to classify the status of H. pylori infection of patients into three categories: uninfected (with no history of H. pylori infection), currently infected, and post-eradication. METHODS: The CAD system was based on linked color imaging (LCI) combined with deep learning (DL). First, a validation dataset was formed for the CAD systems by recording endoscopic movies of 120 subjects. Next, a training dataset of 395 subjects was prepared to enable DL. All endoscopic examinations were recorded using both LCI and white-light imaging (WLI). These endoscopic data were used to develop two different CAD systems, one for LCI (LCI-CAD) and one for WLI (WLI-CAD) images. RESULTS: The diagnostic accuracy of the LCI-CAD system was 84.2% for uninfected, 82.5% for currently infected, and 79.2% for post-eradication status. Comparisons revealed superior accuracy of diagnoses based on LCI-CAD data relative based on WLI-CAD for uninfected, currently infected, and post-eradication cases. Furthermore, the LCI-CAD system demonstrated comparable diagnostic accuracy to that of experienced endoscopists with the validation data set of LCI. CONCLUSIONS: The results of this study suggest the feasibility of an innovative gastric cancer screening program to determine cancer risk in individual subjects based on LCI-CAD.

Accumulating evidence suggests that metformin reduces the incidence and mortality of colorectal cancer (CRC). However, underlying mechanisms have not been fully clarified. The aim of this study was to examine the pathological characteristics of resected CRC from patients treated with metformin for type 2 diabetes mellitus (DM). In total, 267 patients with DM underwent curative colectomy for Stage I-III CRC and 53 (19.9%) patients had been treated medically including metformin. Pathological N-stage was significantly lower in metformin-treated patients (P < .05) with prolonged disease-free survival (DFS) (P < .05). Immunohistochemistry showed that the densities of CD3(+) and CD8(+) tumor-infiltrating lymphocytes (TILs) in the invasive front area were significantly higher in 40 patients treated with metformin compared with propensity score matched cases without metformin (P < .05). The density of tertiary lymphoid structures (TLS) in tumor stroma was markedly increased in metformin-treated patients (P < .001). In those tumors, there were more CD68(+) tumor-associated macrophages (TAM) infiltrated (P < .05), while the ratio of CD163(+) M2-phenotype was markedly reduced (P < .001). Stromal fibrosis tended to be suppressed by metformin intake (P = .051). These findings suggested that metformin drastically changes the characteristics of infiltrating immune cells in CRC and reprograms the tumor microenvironment from immunosuppressive to immunocompetent status, which may lead to suppression of microscopic tumor spread and improve the outcomes of patients with CRC and type 2 DM.

Objectives: To evaluate the effect of simulator fidelity on procedure skill training through a review of existing studies. Methods: MEDLINE, OVID and EMBASE databases were searched between January 1990 and January 2019. Search terms included "simulator fidelity and comparison" and "low fidelity" and "high fidelity" and "comparison" and "simulator". Author classification of low- and high-fidelity was used for non-laparoscopic procedures. Laparoscopic simulators are classified using a proposed schema. All included studies used a randomized methodology with two or more groups and were written in English. Data was abstracted to a standard data sheet and critically appraised from 17 eligible full papers. Results: Of 17 studies, eight were for laparoscopic and nine for other skill training. Studies employed evaluation methodologies, including subjective and objective measures. The evaluation was conducted once in 13/17 studies and before-after in 4/17. Didactic training only or control groups were used in 5/17 studies, while 10/17 studies included two groups only. Skill acquisition and simulator fidelity were different for the level of training in 1/17 studies. Simulation training was followed by clinical evaluation or a live animal evaluation in 3/17 studies. Low-fidelity training was not inferior to training with a high-fidelity simulator in 15/17 studies. Conclusions: Procedure skill after training with low fidelity simulators was not inferior to skill after training with high fidelity simulators in 15/17 studies. Some data suggest that the effectiveness of different fidelity simulators depends on the level of training of participants and requires further study.

Laparoscopic surgery requires highly trained skills to manipulate the laparoscopic instruments. The effectiveness and efficiency of manipulation training are expected to increase by quantitatively evaluating how a series of trainee's manipulations are different from those conducted by skilled surgeons and providing feedback. However, such detailed feed- back is not available because adequate measurement systems have not been proposed to measure the precise forces applied to suture training pads without disturbing the delicate manipulations. Therefore, we proposed a sensorized suture training pad satisfying the above requirements. Three surgical residents participated in an experiment to measure time sequences of three-dimensional forces applied to the pad when executing a transfixion suture. As a result, the differences in manipulation patterns among the residents were found by dividing the transfixion operation based on the sequential force data and recorded videos.

OBJECTIVE: This study aimed to use convolutional neural network (CNN), a deep learning software, to assist in cT1b diagnosis. METHODS: This retrospective study used 190 colon lesion images from 41 cases of colon endoscopies performed between February 2015 and October 2016. Unenhanced colon endoscopy images (520 × 520 pixels) with white light were used. Images included 14 cTis cases with endoscopic resection and 14 cT1a and 13 cT1b cases with surgical resection. Protruding, flat, and recessed lesions were analyzed. AlexNet and Caffe were used for machine learning. Fine tuning of data to increase image numbers was performed. Oversampling for the training images was conducted to avoid impartiality in image numbers, and learning was carried out. The 3-fold cross-validation method was used. Sensitivity, specificity, accuracy, and area under the curve (AUC) values in the receiver operating characteristic curve were calculated for each group. RESULTS: The results were the average of obtained values. With CNN learning, cT1b sensitivity, specificity, and accuracy were 67.5, 89.0, and 81.2%, respectively, and AUC was 0.871. CONCLUSION: Quantitative diagnosis is possible using an endoscopic diagnostic support system with machine learning, without relying on the skill and experience of endoscopists. Moreover, this system could be used to objectively evaluate endoscopic diagnoses.

Background and study aims : Helicobacter pylori (HP)-associated chronic gastritis can cause mucosal atrophy and intestinal metaplasia, both of which increase the risk of gastric cancer. The accurate diagnosis of HP infection during routine medical checks is important. We aimed to develop a convolutional neural network (CNN), which is a machine-learning algorithm similar to deep learning, capable of recognizing specific features of gastric endoscopy images. The goal behind developing such a system was to detect HP infection early, thus preventing gastric cancer. Patients and methods:  For the development of the CNN, we used 179 upper gastrointestinal endoscopy images obtained from 139 patients (65 were HP-positive: ≥ 10 U/mL and 74 were HP-negative: < 3 U/mL on HP IgG antibody assessment). Of the 179 images, 149 were used as training images, and the remaining 30 (15 from HP-negative patients and 15 from HP-positive patients) were set aside to be used as test images. The 149 training images were subjected to data augmentation, which yielded 596 images. We used the CNN to create a learning tool that would recognize HP infection and assessed the decision accuracy of the CNN with the 30 test images by calculating the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Results:  The sensitivity and specificity of the CNN for the detection of HP infection were 86.7 % and 86.7 %, respectively, and the AUC was 0.956. Conclusions:  CNN-aided diagnosis of HP infection seems feasible and is expected to facilitate and improve diagnosis during health check-ups.