EUS导航与解剖地标识别的人工智能系统

Q3 Medicine

VideoGIE Pub Date : 2025-03-22 DOI:10.1016/j.vgie.2025.03.027

Gianenrico Rizzatti PhD, Giulia Tripodi MD, Sara Sofia De Lucia MD, Antonio Pellegrino MD, Ivo Boskoski PhD, Alberto Larghi PhD, Cristiano Spada PhD

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引用次数: 0

摘要

背景和目的人工智能（AI）的应用已被引入多个医学领域，并取得了可喜的成果，包括内窥镜检查。在EUS领域，使用人工智能的研究仍然有限，主要集中在胰腺肿块的识别和表征上。最近，基于深度学习的人工智能系统已经被开发出来，用于识别诊断EUS过程中的解剖标志。方法Endoangel系统（武汉Endoangel医疗技术有限公司，武汉，中国）采用深度卷积神经网络（DCNNs）构建，能够在EUS诊断过程中实时提供导航提示和识别解剖标志。该系统接受了550多个EUS程序的训练，并使用DCNN通过提取特征、引入非线性、降低复杂性和通过完全连接的层进行预测，通过多层处理图像。结果AI EUS系统在3例诊断性EUS患者中进行了测试。在每种情况下，AI EUS系统对解剖标志的正确识别都是由一位执行EUS检查的专家来判断的。该系统不能识别病理改变，如胰腺肿块或囊性病变。结论基于AI EUS dcnn的系统能够正确识别EUS解剖标志。在不久的将来，该系统将在EUS的培训和质量控制中发挥重要作用。此外，许多其他特征可能会逐渐增加，下一个理想的步骤是病理改变的识别。

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Artificial intelligence system for EUS navigation and anatomical landmark recognition

Background and Aims

The use of artificial intelligence (AI) has been introduced in several medical fields with promising results, including endoscopy. In the field of EUS, studies using AI are still limited and have mostly focused on the identification and characterization of pancreatic masses. Recently, AI systems based on deep learning have been developed to identify anatomical landmarks during diagnostic EUS.

Methods

The Endoangel system (Wuhan ENDOANGEL Medical Technology, Wuhan, China), built using deep convolutional neural networks (DCNNs), is able to provide navigation hints and identify anatomical landmarks in real time during diagnostic EUS. The system was trained with more than 550 EUS procedures and uses a DCNN that processes images through multiple layers by extracting features, introducing nonlinearity, reducing complexity, and making predictions via fully connected layers.

Results

The AI EUS system was tested in 3 patients undergoing diagnostic EUS. In each case, the correct recognition of anatomical landmarks by the AI EUS system was judged by a single expert performing the EUS examination. The system did not recognize pathologic alterations such as pancreatic masses or cystic lesions.

Conclusions

The AI EUS DCNN-based system is able to correctly identify EUS anatomical landmarks. In the near future, this system might play an important role in EUS training and quality control. In addition, many other features might progressively be added, with the next ideal step being the identification of pathologic alterations.

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来源期刊

VideoGIE Medicine-Gastroenterology

CiteScore

1.50

自引率

0.00%

发文量

132

审稿时长

105 days

期刊介绍： VideoGIE, an official video journal of the American Society for Gastrointestinal Endoscopy, is an Open Access, online-only journal to serve patients with digestive diseases. VideoGIE publishes original, single-blinded peer-reviewed video case reports and case series of endoscopic procedures used in the study, diagnosis, and treatment of digestive diseases. Videos demonstrate use of endoscopic systems, devices, and techniques; report outcomes of endoscopic interventions; and educate physicians and patients about gastrointestinal endoscopy. VideoGIE serves the educational needs of endoscopists in training as well as advanced endoscopists, endoscopy staff and industry, and patients. VideoGIE brings video commentaries from experts, legends, committees, and leadership of the society. Careful adherence to submission guidelines will avoid unnecessary delays, as incomplete submissions may be returned to the authors before initiation of the peer review process.