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• Review •
Advancements in the integration of artificial intelligence and imaging technology for the detection of metastatic cervical lymph nodes
CHEN Lijun  WANG Bing  WANG Lin 

DOI:10.12015/issn.1674-8034.2025.11.032.


[Abstract] Metastatic cervical lymph nodes (MCLN) are crucial in the diagnosis, staging, and clinical decision-making processes for various head and neck tumors. Despite the widespread use of conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT) in clinical practice, their sensitivity and specificity in accurately identifying all instances of MCLN remain suboptimal. In recent years, artificial intelligence (AI), and deep learning (DL) in particular, have made significant advancements in the field of medical image analysis. This review provides a comprehensive review of the latest research on the combined use of different modal imaging techniques and AI (CT enhancement combined with automatic segmentation, MRI high soft tissue contrast combined with automatic segmentation, PET-CT metabolic image fusion AI model, ultrasound combined with DL for real-time automatic auxiliary diagnosis, etc.) in head and neck MCLN. It elaborates on the application of AI in the diagnosis, therapeutic effect and prognosis assessment of MCLN, summarizes the existing shortcomings and technical challenges in current research, and proposes future development directions. This review aims to provide a reference for future research collaboration, model optimization and clinical application.
[Keywords] head and neck neoplasms；lymph node metastasis；artificial intelligence；deep learning；imaging technology；magnetic resonance imaging

CHEN Lijun1, 2   WANG Bing3   WANG Lin4*  

1 The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China

2 Department of Radiology, Gansu Provincial People's Hospital, Lanzhou 730050, China

3 Department of Orthopaedics, the First People's Hospital of Baiyin City, Baiyin 730900, China

4 Department of Radiology, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China

Corresponding author: WANG L, E-mail: tedyong@163.com

Conflicts of interest   None.

Received  2025-06-08
Accepted  2025-09-30
DOI: 10.12015/issn.1674-8034.2025.11.032
DOI:10.12015/issn.1674-8034.2025.11.032.

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