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Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial
BACKGROUND: Sedative gastrointestinal endoscopy is extensively used worldwide. An appropriate degree of sedation leads to more acceptability and satisfaction. Artificial intelligence has rapidly developed in the field of digestive endoscopy in recent years and we have constructed a mature computer-a...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540709/ https://www.ncbi.nlm.nih.gov/pubmed/36207701 http://dx.doi.org/10.1186/s12871-022-01796-1 |
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author | Xu, Cheng Zhu, Yijie Wu, Lianlian Yu, Honggang Liu, Jun Zhou, Fang Xiong, Qiutang Wang, Shanshan Cui, Shanshan Huang, Xu Yin, Anning Xu, Tingting Lei, Shaoqing Xia, Zhongyuan |
author_facet | Xu, Cheng Zhu, Yijie Wu, Lianlian Yu, Honggang Liu, Jun Zhou, Fang Xiong, Qiutang Wang, Shanshan Cui, Shanshan Huang, Xu Yin, Anning Xu, Tingting Lei, Shaoqing Xia, Zhongyuan |
author_sort | Xu, Cheng |
collection | PubMed |
description | BACKGROUND: Sedative gastrointestinal endoscopy is extensively used worldwide. An appropriate degree of sedation leads to more acceptability and satisfaction. Artificial intelligence has rapidly developed in the field of digestive endoscopy in recent years and we have constructed a mature computer-aided diagnosis (CAD) system. This system can identify the remaining parts to be examined in real-time endoscopic procedures, which may help anesthetists use anesthetics properly to keep patients in an appropriate degree of sedation. AIMS: This study aimed to evaluate the effects of the CAD system on anesthesia quality control during gastrointestinal endoscopy. METHODS: We recruited 154 consecutive patients at Renmin Hospital of Wuhan University, including 76 patients in the CAD group and 78 in the control group. Anesthetists in the CAD group were able to see the CAD system’s indications, while anesthetists in the control group could not. The primary outcomes included emergence time (from examination completion to spontaneous eye opening when doctors called the patients’ names), recovery time (from examination completion to achievement of the primary recovery endpoints) and patient satisfaction scores. The secondary outcomes included anesthesia induction time (from sedative administration to successful sedation), procedure time (from scope insertion to scope withdrawal), total dose of propofol, vital signs, etc. This trial was registered in the Primary Registries of the WHO Registry Network, with registration number ChiCTR2100042621. RESULTS: Emergence time in the CAD group was significantly shorter than that in the control group (p < 0.01). The recovery time was also significantly shorter in the CAD group (p < 0.01). Patients in the CAD group were significantly more satisfied with their sedation than those in control group (p < 0.01). Vital signs were stable during the examinations in both groups. Propofol doses during the examinations were comparable between the two groups. CONCLUSION: This CAD system possesses great potential for anesthesia quality control. It can improve patient satisfaction during endoscopic examinations with sedation. TRIAL REGISTRATION: ChiCTR2100042621. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-022-01796-1. |
format | Online Article Text |
id | pubmed-9540709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95407092022-10-08 Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial Xu, Cheng Zhu, Yijie Wu, Lianlian Yu, Honggang Liu, Jun Zhou, Fang Xiong, Qiutang Wang, Shanshan Cui, Shanshan Huang, Xu Yin, Anning Xu, Tingting Lei, Shaoqing Xia, Zhongyuan BMC Anesthesiol Research BACKGROUND: Sedative gastrointestinal endoscopy is extensively used worldwide. An appropriate degree of sedation leads to more acceptability and satisfaction. Artificial intelligence has rapidly developed in the field of digestive endoscopy in recent years and we have constructed a mature computer-aided diagnosis (CAD) system. This system can identify the remaining parts to be examined in real-time endoscopic procedures, which may help anesthetists use anesthetics properly to keep patients in an appropriate degree of sedation. AIMS: This study aimed to evaluate the effects of the CAD system on anesthesia quality control during gastrointestinal endoscopy. METHODS: We recruited 154 consecutive patients at Renmin Hospital of Wuhan University, including 76 patients in the CAD group and 78 in the control group. Anesthetists in the CAD group were able to see the CAD system’s indications, while anesthetists in the control group could not. The primary outcomes included emergence time (from examination completion to spontaneous eye opening when doctors called the patients’ names), recovery time (from examination completion to achievement of the primary recovery endpoints) and patient satisfaction scores. The secondary outcomes included anesthesia induction time (from sedative administration to successful sedation), procedure time (from scope insertion to scope withdrawal), total dose of propofol, vital signs, etc. This trial was registered in the Primary Registries of the WHO Registry Network, with registration number ChiCTR2100042621. RESULTS: Emergence time in the CAD group was significantly shorter than that in the control group (p < 0.01). The recovery time was also significantly shorter in the CAD group (p < 0.01). Patients in the CAD group were significantly more satisfied with their sedation than those in control group (p < 0.01). Vital signs were stable during the examinations in both groups. Propofol doses during the examinations were comparable between the two groups. CONCLUSION: This CAD system possesses great potential for anesthesia quality control. It can improve patient satisfaction during endoscopic examinations with sedation. TRIAL REGISTRATION: ChiCTR2100042621. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-022-01796-1. BioMed Central 2022-10-07 /pmc/articles/PMC9540709/ /pubmed/36207701 http://dx.doi.org/10.1186/s12871-022-01796-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xu, Cheng Zhu, Yijie Wu, Lianlian Yu, Honggang Liu, Jun Zhou, Fang Xiong, Qiutang Wang, Shanshan Cui, Shanshan Huang, Xu Yin, Anning Xu, Tingting Lei, Shaoqing Xia, Zhongyuan Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
title | Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
title_full | Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
title_fullStr | Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
title_full_unstemmed | Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
title_short | Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
title_sort | evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540709/ https://www.ncbi.nlm.nih.gov/pubmed/36207701 http://dx.doi.org/10.1186/s12871-022-01796-1 |
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