Maximilian Rink, Julian Künzel, Christian Stroszczynski, Friedrich Jung, Ernst Michael Jung
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引用次数: 0
Abstract
Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assisted sonography directly on the device. However, this is not yet the case for lymph nodes.The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes. For this purpose, the two programs were used as a supplement to routine ultrasound examinations of superficial lymph nodes. The accuracy of detection by AI was then evaluated using a previously defined score. If available, a comparison was made with cross-sectional imaging.The programs that were used are able to adequately detect lymph nodes in the majority of cases (78.6%). Problems were caused in particular by a high proportion of echo-rich fat, blurred differentiation from the surrounding tissues and the occurrence of lymph node conglomerates. The available cross-sectional images did not contradict the classification of the lesion as a lymph node in any case.In the majority of cases, the tested programs are already able to detect and measure superficial lymph nodes. Further improvement can be expected through specific training of the software. Further developments and studies are required to assess risk of malignancy. · The inclusion of AI in imaging is increasingly becoming a scientific focus.. · The detection of lymph nodes is already possible using device-integrated AI software.. · Malignancy assessment of the detected lymph nodes is not yet possible.. · Rink M, Künzel J, Stroszczynski C et al. Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results. Rofo 2025; DOI 10.1055/a-2331-0951.
在过去几年中,人们越来越重视将人工智能(AI)集成到现有的成像系统中。这同样适用于超声波。目前已有针对甲状腺和乳腺病变的应用程序,可直接在设备上进行人工智能辅助超声造影。我们的目的是测试已建立的乳腺病变和甲状腺结节人工智能辅助超声造影程序是否也适用于识别和测量浅表淋巴结。为此,这两个程序被用作浅表淋巴结常规超声检查的补充。然后使用之前定义的评分来评估人工智能检测的准确性。在大多数情况下(78.6%),所使用的程序都能充分检测出淋巴结。出现问题的原因主要是富含回声的脂肪比例较高,与周围组织的分界模糊,以及出现淋巴结聚集。在大多数情况下,测试程序已经能够检测和测量浅表淋巴结。在大多数情况下,测试程序已能检测和测量浅表淋巴结,通过对软件进行专门培训,可望进一步提高检测和测量能力。评估恶性肿瘤风险还需要进一步的开发和研究。- 将人工智能纳入成像技术正日益成为科学界关注的焦点。- 使用设备集成的人工智能软件已经可以检测淋巴结。- 目前还无法对检测到的淋巴结进行恶性程度评估。- Rink M, Künzel J, Stroszczynski C et al. 智能扫描:利用超声波自动检测浅表淋巴结 - 初步结果。Fortschr Röntgenstr 2024; DOI 10.1055/a-2331-0951.
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