Progress in CT AI diagnosis of pulmonary nodules

Corresponding author: zhoujianguo, 13645132158@163.com

DOI: 10.12201/bmr.202504.00068

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  Abstract: With the popularization of low-dose CT screening, the detection rate of pulmonary nodules has significantly increased. The rapid development of artificial intelligence (AI) technology has brought revolutionary breakthroughs to the precise diagnosis and treatment of pulmonary nodules. This article systematically reviews the latest research progress of AI in the detection of pulmonary nodules, the differentiation of benign and malignant nodules, the prediction of pathological types, and treatment planning. Deep learning algorithms and multimodal fusion models have obvious application advantages in the detection of pulmonary nodules, the differentiation of benign and malignant nodules, and the selection of clinical treatment methods. Prediction models based on radiomics can accurately assess the invasiveness and gene mutation status of nodules. Despite facing challenges such as data standardization and clinical transformation, AI technology is promoting the development of pulmonary nodule diagnosis and treatment towards intelligence and precision.

  Key words: artificial intelligence; diagnosis and treatment of pulmonary nodules; multimodal fusion; imaging omics

  Submit time: 23 April 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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