Ryoko Murayama

Highlights Automated system detects thrombi and edema using ultrasonographic images. Thrombi and subcutaneous tissue characteristics were accurately estimated. Machine learning model achieved 0.723 accuracy for thrombus detection. Edema detection had 0.881 accuracy with 0.928 sensitivity. Abstract Aim: Blood vessel and subcutaneous tissue assessment using ultrasonographic (US) images prevents peripheral intravenous catheter (PIVC) failure but requires training and is often subjective. In this study, we aimed to develop an automated image processing system for detecting thrombi and edema. Methods: US images were collected from patients with catheters, featuring subcutaneous thrombi and edema. Using supervised machine learning with fully convolutional networks, we analyzed 263 images for training and 452 images for evaluation. Ground truth data were manually annotated by calculating accuracy, sensitivity, and specificity. Results: In the test dataset of 452 images, 99 thrombi and 359 edema cases were manually detected. In the automatic estimation, thrombi and edema cases were detected in 102 and 360 images, respectively. The accuracy, sensitivity, and specificity were 0.723, 0.383, and 0.818 for thrombus and 0.881, 0.928, and 0.697 for edema, respectively. Conclusions: This study used a new artificial intelligence tool to detect thrombi and subcutaneous edemas in US images. The sensitivity of the thrombus detection was low in this study, and authors of future studies should focus on improving the tool’s performance. This will increase the accuracy and convenience of US imaging for PIVC use.

OBJECTIVES: We aimed to determine (1) the prevalence of constipation among inpatients, (2) the prevalence and symptoms of difficult defecation among constipated inpatients, and (3) the factors associated with constipation. METHODS: We performed a retrospective cohort study over a single day at one university hospital. We analyzed the nursing records for inpatients who had been hospitalized for at least 3 days. The survey items included the symptoms associated with defecation difficulty and nutritional intake. The symptoms of difficult defecation were defined as (1) fewer than three spontaneous bowel movements per week; (2) lumpy or hard stools (Bristol stool form scale types 1-2); (3) straining during defecation; and (4) the sensation of incomplete evacuation during defecation, based on the Roma-IV diagnostic criteria. Constipation was defined as the presence of two or more symptoms of defecation difficulty. Univariate and multivariate analyses were performed to determine the constipation status of the patients. RESULTS: The prevalence of constipation in the university hospital was 12.2%, and the department with the highest prevalence of difficulty with defecation was the Psychiatry Department (64.1%). Of the patients with constipation, 36.8% exhibited symptoms of defecation difficulty other than low frequency of defecation. The factor that was significantly associated with constipation after admission was pre-admission constipation (odds ratio=8.92, p<0.01). CONCLUSIONS: Subjective assessment has limitations for the accurate determination of constipation status. In addition, patients with a history of constipation before admission require early interventions to aid defecation following their admission.