Automatic vein measurement by ultrasonography to prevent peripheral intravenous catheter failure for clinical practice using artificial intelligence: development and evaluation study of an automatic detection method based on deep learning. Issue 5 (24th May 2022)

• Record Type: Journal Article
• Authors: Takahashi, Toshiaki; Nakagami, Gojiro; Murayama, Ryoko; Abe-Doi, Mari; Matsumoto, Masaru; Sanada, Hiromi
• Is Part Of: BMJ open. Volume 12:Issue 5(2022)
• Publication Date: 2022-05-24
• Subjects: ultrasonography -- education & training (see medical education & training) -- information technology; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2021-051466
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Complications due to peripheral intravenous catheters (PIVC) can be assessed using ultrasound imaging; however, it is not routinely conducted due to the need for training in image reading techniques. This study aimed to develop and validate a system that automatically measures blood vessel diameters on ultrasound images using artificial intelligence (AI) and provide recommendations for selecting an implantation site. Design: Pilot study. Setting: The University of Tokyo Hospital, Japan. Primary and secondary outcome measures: First, based on previous studies, the vessel diameter was calculated as the mean value of the maximum long diameter plus the maximum short diameter orthogonal to it. Second, the size of the PIVC to be recommended was evaluated based on previous studies. For the development and validation of an automatic detection tool, we used a fully convoluted network for automatic estimation of vein location and diameter. The agreement between manually generated correct data and automatically estimated data was assessed using Pearson's product correlation coefficient, systematic error was identified using the Bland-Altman plot, and agreement between catheter sizes recommended by the research nurse and those recommended by the system was evaluated. Results: Through supervised machine learning, automated determination was performed using 998 ultrasound images, of which 739 and 259 were used as the training and test data set, respectively. There were 24 false-negatives indicating no arteries detected and 178 true-positives indicating correct detection. Correlation of the results between the system and the nurse was calculated from the 178 images detected (r=0.843); no systematic error was identified. The agreement between the sizes of the PIVC recommended by the research nurse and the system was 70.2%; 7% were underestimated and 21.9% were overestimated. Conclusions: Our automated AI-based image processing system may aid nurses in assessing peripheral veins using ultrasound images for catheterisation; however, further studies are still warranted.t