人工智能辅助识别经腹腹膜前腹股沟疝修补术中的解剖标记和腹腔镜器械。

IF 1.2 4区 医学 Q3 SURGERY
Surgical Innovation Pub Date : 2024-04-01 Epub Date: 2024-01-09 DOI:10.1177/15533506241226502
Apollon Zygomalas, Dimitrios Kalles, Nikolaos Katsiakis, Andreas Anastasopoulos, Georgios Skroubis
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引用次数: 0

摘要

腹腔镜 TAPP(经腹膜前)是一种用于修复腹股沟疝的微创手术。可以说,TAPP疝修补术的一个重要方面是识别解剖标志和正确使用各种腹腔镜器械。关于人工智能在腹腔镜腹股沟疝修补术中的应用,尤其是在 TAPP 中的应用,目前还鲜有研究。本研究旨在评估人工智能在腹腔镜 TAPP 视频中识别解剖标志和工具的可行性和实用性。图像数据来自作者所做的 20 个腹腔镜 TAPP 视频和其他外科医生从互联网(免费访问)上所做的另外 5 个高质量 TAPP 视频。总计输出了 1095 张选定的图像进行标注。为了实现计算机视觉的人工智能成果,我们使用了深度学习的 YOLOv8 模型。总共输出了 2716 个感兴趣区段。当置信度阈值设置为 0.406 时,人工智能模型能够以 0.82 的最高 F1 分数检测出各种类别。所有类别的 mAP50 均为 0.873。当置信度超过 10%时,精确率超过 50%。当置信度低于 70% 时,召回率高于 50%。这些结果表明,该模型能有效平衡精确度和召回率,既能捕捉真阳性,又能最大限度地减少假阴性。人工智能识别 TAPP 中的解剖地标和腹腔镜器械是可行的,成功率可以接受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Assisted Recognition of Anatomical Landmarks and Laparoscopic Instruments in Transabdominal Preperitoneal Inguinal Hernia Repair.

Laparoscopic TAPP (Trans-Abdominal PrePeritoneal) is a minimally invasive surgical procedure used to repair inguinal hernias. Arguably, one important aspect to TAPP hernia repair is the identification of anatomical landmarks and the correct use of various laparoscopic instruments. There are very few studies regarding the use of artificial intelligence in laparoscopic inguinal hernia repair and more specifically in TAPP. The aim of this study is to evaluate the feasibility and usefulness of AI in the recognition of anatomical landmarks and tools in laparoscopic TAPP videos. Imaging data have been exported from 20 Laparoscopic TAPP videos that have been performed by the authors and another 5 high quality TAPP videos from the internet (free access) performed by other surgeons. In total 1095 selected images have been exported for annotation. To accomplish the AI result of computer vision, the YOLOv8 model of deep learning was used. In total 2716 segmented areas of interest have been exported. The AI model was able to detect the various classes with a maximum F1 score of .82 when the confidence threshold was set to .406. The mAP50 was .873 for all classes. The Precision was above 50% when the confidence was over 10%. The Recall rate was above 50% when confidence was less than 70%. These results suggest that the model is effective at balancing precision and recall, capturing both true positives and minimizing false negatives. Artificial Intelligence recognition of anatomical landmarks and laparoscopic instruments in TAPP is feasible with acceptable success rates.

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来源期刊
Surgical Innovation
Surgical Innovation 医学-外科
CiteScore
2.90
自引率
0.00%
发文量
72
审稿时长
6-12 weeks
期刊介绍: Surgical Innovation (SRI) is a peer-reviewed bi-monthly journal focusing on minimally invasive surgical techniques, new instruments such as laparoscopes and endoscopes, and new technologies. SRI prepares surgeons to think and work in "the operating room of the future" through learning new techniques, understanding and adapting to new technologies, maintaining surgical competencies, and applying surgical outcomes data to their practices. This journal is a member of the Committee on Publication Ethics (COPE).
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