Application of AI image recognition system in improving the quality of training on ultrasound-guided nerve block technique for residents

Ma Xinwei; Wang Xuan; Yu Jie; Zhang Bowen; Yang Jingchao; Liu Jin; Wei Shijing; Wang Qiang; Zheng Hui

doi:10.3760/cma.j.cn116021-20240208-01906

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Application of AI image recognition system in improving the quality of training on ultrasound-guided nerve block technique for residents

PUMC Column on Exploring Postgraduate Education in Anesthesiology | 更新时间：2024-12-26

- Application of AI image recognition system in improving the quality of training on ultrasound-guided nerve block technique for residents
- Chinese Journal of Medical Education Research Vol. 23, Issue 12, Pages: 1608-1612(2024)
- 作者机构：
  
  1.国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院　北京协和医学院　肿瘤医院麻醉科，北京　100021
  2.青岛大学附属医院麻醉科，青岛　266000
- 作者简介：
  
  Wang Qiang, Email: wqzjbd001@163.com
- 基金信息：
  
  Beijing Natural Science Foundation(7232133);Residential Training and Teaching Research Project of Cancer Hospital of Chinese Academy of Medical Sciences(E2023009);Medical and Health Science and Technology Innovation Project of Chinese Academy of Medical Sciences(2022-I2M-C&T-B-086)
- DOI：10.3760/cma.j.cn116021-20240208-01906
  CLC： R614
- Published：20 December 2024，
  
  Received：08 February 2024，
- 稿件说明：
移动端阅览
马欣伟, 王璇, 于洁, 等. AI图像识别系统用于提升住院医师超声引导神经阻滞培训质量的研究[J]. 中华医学教育探索杂志, 2024, 23(12): 1608-1612.

MA XINWEI, WANG XUAN, YU JIE, et al. Application of AI image recognition system in improving the quality of training on ultrasound-guided nerve block technique for residents. [J]. Chinese journal of medical education research, 2024, 23(12): 1608-1612.
马欣伟, 王璇, 于洁, 等. AI图像识别系统用于提升住院医师超声引导神经阻滞培训质量的研究[J]. 中华医学教育探索杂志, 2024, 23(12): 1608-1612. DOI： 10.3760/cma.j.cn116021-20240208-01906.

MA XINWEI, WANG XUAN, YU JIE, et al. Application of AI image recognition system in improving the quality of training on ultrasound-guided nerve block technique for residents. [J]. Chinese journal of medical education research, 2024, 23(12): 1608-1612. DOI： 10.3760/cma.j.cn116021-20240208-01906.

摘要

目的

探讨人工智能（artificial intelligence，AI）图像识别教育系统在麻醉科初学住院医师超声引导神经阻滞技术规范化培训中的应用及教学效果。

方法

选取自2023年1月至12月在中国医学科学院肿瘤医院

麻醉科与青岛大学附属医院麻醉科接受培训的40名住院医师为研究对象，将其随机分为试验组（AI图像识别教育系统带教）和对照组（传统带教），旨在进行神经阻滞技术的培训及考核，每组各20名。通过比较两组住院医师的临床操作实践评分、教学效果评价及教学质量评价，以评估两组住院医师的培训质量。采用SPSS 23.0软件对数据进行

检验和卡方检验。

结果

试验组住院医师的临床操作实践评分高于对照组[（4.65±0.49） vs. （3.60±0.75），

0.001

]

；试验组住院医师在超声引导神经阻滞技术的方法掌握程度[（17.45±1.23） vs. （13.85±1.27），

0.001

]

、临床操作满意度[（16.70±1.34） vs.（13.95±1.00），

0.001

]

及学习积极性[（17.35±1.50） vs. （13.55±0.94），

0.001

]

均优于对照组；试验组的教学质量评分也高于对照组[（17.30±1.59） vs. （14.25±1.68），

0.001

]

，说明试验组住院医师的培训质量优于对照组。

结论

应用AI图像识别教育系统进行超声引导神经阻滞技术的规范化培训，能够提升带教教师的教学质量，增强规培医师的学习效果，并调动其学习积极性，值得进一步推广和应用。

Abstract

Objective

To explore the application and teaching effect of artificial intelligence (AI) image recognition education system in standardized training on ultrasound-guided nerve block technique for novice anesthesiology residents.

Methods

Forty residents trained in the Department of Anesthesiology of Cancer Hospital of Chinese Academy of Medical Sciences and the Department of Anesthesiology of the Affiliated Hospital of Qingdao University from January 2023 to December 2023 were selected as study subjects

and randomly divided into experimental group (teaching with the AI image recognition education system) and control group (traditional teaching) to undergo training and assessments on the nerve block technique

with 20 residents in each group. The training quality for the two groups was assessed by comparing the clinical practice score

teaching effect evaluation

and teaching quality evaluation. SPSS 23.0 software was used for the

-test and chi-square test on data.

Results

Compared with the control group

residents in the experimental group had higher clinical practice scores [(4.65±0.49) vs. (3.60±0.75)

0.001

]

and better method mastery [(17.45±1.23) vs. (13.85±1.27)

0.001

]

satisfaction with clinical operation [(16.70±1.34) vs. (13.95±1.00)

0.001

]

and learning enthusiasm [(17.35±1.50) vs. (13.55±0.94)

0.001

]

for the ultrasound-guided nerve block technique. The teaching quality score of the experimental group was higher than that of the control group [(17.30±1.59) vs. (14.25±1.68)

0.001

]

. Therefore

the training quality of residents in the experimental group was better than that of the control group.

Conclusions

The application of AI image recognition education system in standardized training on the ultrasound-guided nerve block technique improves the teaching quality of teachers

enhances the learning effect of novice anesthesiology residents

and mobilizes learning enthusiasm

which is worthy of further promotion and application.

关键词

人工智能超声成像神经阻滞医学继续教育

Keywords

Artificial intelligenceUltrasonic imagingNerve blockContinuing medical education

references

李方燕，文勇，雷平贵，等. 中国住院医师规范化培训的现状及思考[J]. 中国继续医学教育， 2019， 11（35）： 92-94. DOI: 10.3969/j.issn.1674-9308.2019.35.035http://dx.doi.org/10.3969/j.issn.1674-9308.2019.35.035.

Viderman D, Dossov M, Seitenov S, et al. Artificial intelligence in ultrasound-guided regional anesthesia: a scoping review [J]. Front Med, 2022, 9: 994805. DOI: 10.3389/fmed.2022.994805http://dx.doi.org/10.3389/fmed.2022.994805.

Bowness JS, Burckett-St Laurent D, Hernandez N, et al. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study [J]. Br J Anaesth, 2023, 130(2): 217-225. DOI: 10.1016/j.bja.2022.06.031http://dx.doi.org/10.1016/j.bja.2022.06.031.

王梦溪，王娜，张欣多，等. 人工智能医学教学平台的构建[J]. 中国高等医学教育， 2020（3）： 46-48. DOI: 10.3969/j.issn.1002-1701.2020.03.023http://dx.doi.org/10.3969/j.issn.1002-1701.2020.03.023.

闫琦，马晓冉，霍飞，等. 超声引导下神经阻滞麻醉教学难点与对策[J]. 中华医学教育杂志， 2021， 41（2）： 167-170. DOI: 10.3760/cma.j.cn115259-20200508-00717http://dx.doi.org/10.3760/cma.j.cn115259-20200508-00717.

Wang Y, Zhu B, Kong L, et al. BPSegSys: a brachial plexus nerve trunk segmentation system using deep learning [J]. Ultrasound Med Biol, 2024, 50(3): 374-383. DOI: 10.1016/j.ultrasmedbio.2023.11.009http://dx.doi.org/10.1016/j.ultrasmedbio.2023.11.009.

Gungor I, Gunaydin B, Oktar SO, et al. A real-time anatomy ıdentification via tool based on artificial ıntelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study [J]. J Anesth, 2021, 35(4): 591-594. DOI: 10.1007/s00540-021-02947-3http://dx.doi.org/10.1007/s00540-021-02947-3.

Bowness J, Varsou O, Turbitt L, et al. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia [J]. Clin Anat, 2021, 34(5): 802-809. DOI: 10.1002/ca.23742http://dx.doi.org/10.1002/ca.23742.

Connor CW. Artificial intelligence and machine learning in anesthesiology [J]. Anesthesiology, 2019, 131(6): 1346-1359. DOI: 10.1097/ALN.0000000000002694http://dx.doi.org/10.1097/ALN.0000000000002694.

Bowness JS, El-Boghdadly K, Woodworth G, et al. Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia [J]. Reg Anesth Pain Med, 2022, 47(6): 375-379. DOI: 10.1136/rapm-2021-103368http://dx.doi.org/10.1136/rapm-2021-103368.

Bowness JS, MacFarlane AJR, Burckett-St Laurent D, et al. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia [J]. Br J Anaesth, 2023, 130(2): 226-233. DOI: 10.1016/j.bja.2022.07.049http://dx.doi.org/10.1016/j.bja.2022.07.049.

易斌，陈芋文，钟坤华，等. 人工智能在麻醉学专业硕士临床技能培训考核中的应用可行性探讨[J]. 医学教育研究与实践， 2019， 27（3）： 421-425. DOI: 10.13555/j.cnki.c.m.e.2019.03.015http://dx.doi.org/10.13555/j.cnki.c.m.e.2019.03.015.

傅海龙，蒋鑫，王启龙，等. 人工智能在临床麻醉教学领域应用现状研究[J]. 中华医学教育探索杂志， 2023， 22（9）： 1295-1298. DOI: 10.3760/cma.j.cn116021-20210711-01405http://dx.doi.org/10.3760/cma.j.cn116021-20210711-01405.

Chae D. Data science and machine learning in anesthesiology [J]. Korean J Anesthesiol, 2020, 73(4): 285-295. DOI: 10.4097/kja.20124http://dx.doi.org/10.4097/kja.20124.

Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept "black box" medicine? [J]. Ann Intern Med, 2020, 172(1): 59-60. DOI: 10.7326/M19-2548http://dx.doi.org/10.7326/M19-2548.

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