基于放大内镜窄带成像的人工智能在早期胃癌诊断中的应用。

IF 2.1 Q3 GASTROENTEROLOGY & HEPATOLOGY Clinical Endoscopy Pub Date : 2024-01-01 Epub Date: 2024-01-05 DOI:10.5946/ce.2023.173
Yusuke Horiuchi, Toshiaki Hirasawa, Junko Fujisaki
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引用次数: 0

摘要

虽然窄带成像的放大内镜检查是胃癌的标准诊断检查,但使用这种技术诊断胃癌需要相当高的技术。人工智能具有卓越的图像识别能力,其在内窥镜图像诊断中的实用性已有许多案例报道。人工智能利用放大内镜窄带静态图像和视频对胃癌的诊断性能(准确性、灵敏度和特异性)高于内镜专家,这表明人工智能在诊断胃癌方面非常有用。利用人工智能对胃癌进行组织学诊断也很有前景。然而,以往利用人工智能诊断胃癌的研究规模较小,因此有必要进行大规模研究,以检验是否能达到较高的诊断性能。此外,利用人工智能诊断胃癌尚未在临床实践中普及,还需要进一步研究。因此,未来必须进一步发展人工智能这一工具,随着全国范围内大量病例的积累,其诊断性能有望得到改善。
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Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

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来源期刊
Clinical Endoscopy
Clinical Endoscopy GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
4.40
自引率
8.00%
发文量
95
审稿时长
26 weeks
期刊最新文献
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