ArticleMedicine2026
Clinical value of artificial intelligence computer-aided diagnosis system in lymph node ultrasonography: A STARD-compliant article.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
This article explores the clinical value of the artificial intelligence computer-aided diagnosis system in the diagnosis of superficial lymph nodes. In this study, characteristic ultrasonic parameters of 185 cases of lymph nodes (including 110 benign and 75 malignant) confirmed by pathology were quantified by the superficial lymphadenopathy computer-aided diagnosis system, including margin, appearance, medulla proportion, medulla distribution, echogenicity, echogeneity, vascular density, and vascular pattern. A nonparametric rank sum test was used between the parameters. The sensitivity, specificity, and accuracy of the artificial intelligence computer-aided diagnosis system in the diagnosis of superficial lymph nodes were 80.0%, 80.9%, and 80.5%. The characteristics of margin, appearance, medulla proportion, medulla distribution, echogenicity, echogeneity, vascular density, and vascular pattern could identify benign and malignant lymph nodes. The artificial intelligence superficial lymphadenopathy computer-aided diagnosis system can screen out the characteristic ultrasonic parameters of benign and malignant lymph nodes, which has important value in lymph node clinical diagnosis.
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