Artificial intelligence-assisted enhancement of diagnostic accuracy and efficiency in detecting cervical lymph node metastases in oral squamous cell carcinoma
Year: 2025
Type: article
Abstract: Background/purpose: Cervical lymph node metastasis represents a critical prognostic factor in oral squamous cell carcinoma (SCC); however, early-stage or subtle metastases often pose diagnostic challenges under conventional microscopy. Recent advancements in artificial intelligence (AI) offer promising solutions to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the extent to which AI-assisted tools can improve diagnostic performance and efficiency in the detection of cervical lymph node metastases. Materials and methods: Sixty-six hematoxylin-eosin-stained slides containing 621 lymph nodes from oral SCC cases were digitized. Four participants (two oral pathologists, one postgraduate year (PGY) resident, one fourth-year dental student) reviewed slides with and without the AI-assistant tool. Diagnostic accuracy and interpretation time were compared. Results: < 0.05, respectively), with the greatest benefit seen among less-experienced participants. Conclusion: The AI-assisted tool improved diagnostic accuracy and efficiency in detecting cervical lymph node metastases in oral SCC. It may serve as a preliminary screening tool and a valuable educational aid for training junior pathologists, underscoring its potential for broader application in digital pathology.
Source: Journal of Dental Sciences
Authors: Chih-Huang Tseng, Chang-Wei Su, Yu-Min Lin, Yu-Hsun Kao, Wei-Cheng Lin, Hao-Tang Wang, Ching-Yi Chen, Wen-Chen Wang, Yuk-Kwan Chen
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,600
OpenAlex ID: W4413254804
