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
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 vesse...
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BMJ Publishing Group
2022-05-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/5/e051466.full |
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| author | Hiromi Sanada Toshiaki Takahashi Gojiro Nakagami Ryoko Murayama Mari Abe-Doi Masaru Matsumoto |
| author_facet | Hiromi Sanada Toshiaki Takahashi Gojiro Nakagami Ryoko Murayama Mari Abe-Doi Masaru Matsumoto |
| author_sort | Hiromi Sanada |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-8a2bd1b96218434cb6fecdd8112452d7 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-8a2bd1b96218434cb6fecdd8112452d72025-08-20T01:47:50ZengBMJ Publishing GroupBMJ Open2044-60552022-05-0112510.1136/bmjopen-2021-051466Automatic 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 learningHiromi Sanada0Toshiaki Takahashi1Gojiro Nakagami2Ryoko Murayama3Mari Abe-Doi4Masaru Matsumoto54 Ishikawa Prefectural Nursing University, Ishikawa, Japan1 Department of Gerontological Nursing/Wound Care Management, The University of Tokyo, Tokyo, Japan1 Department of Gerontological Nursing/Wound Care Management, The University of Tokyo, Tokyo, Japan3 Research Center for Implementation Nursing Science Initiative, Reseach Promotion Headquarters, Fujita Health University, Aichi, Japan1 Department of Gerontological Nursing/Wound Care Management, The University of Tokyo, Tokyo, Japan4 Ishikawa Prefectural Nursing University, Ishikawa, JapanObjectives 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.thttps://bmjopen.bmj.com/content/12/5/e051466.full |
| spellingShingle | Hiromi Sanada Toshiaki Takahashi Gojiro Nakagami Ryoko Murayama Mari Abe-Doi Masaru Matsumoto 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 BMJ Open |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopen.bmj.com/content/12/5/e051466.full |
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