Sub‐diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification
Integrating sub-diffuse reflectance spectroscopy measurement and suitable machine learning methods can obtain remarkable precision in differentiating different sites of oral mucosa and identifying different types of oral mucosal diseases, especially based on DE features. It is of great help in detecting OSCC and is expected to be a highly sensitive, time-sensitive, and accurate method for oral disease detection.
Authors
- Siyi Zou (ORCID: https://orcid.org/0000-0002-6196-6306)
- Dongyuan Liu (ORCID: https://orcid.org/0000-0002-9128-5747)
- Qi Zhang (ORCID: https://orcid.org/0009-0001-9115-6603)
- Limin Zhang (ORCID: https://orcid.org/0000-0002-2933-7227)
- Feng Gao (ORCID: https://orcid.org/0000-0002-3772-6193)
- Chenlu Liu
- Qing Chang (ORCID: https://orcid.org/0009-0004-9343-8649)
Institutions
- Tianjin University (CN)
- Tianjin Stomatological Hospital (CN)
Publication Details
- Journal
- Lasers in Surgery and Medicine
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1002/lsm.70011
- Citations
- 3
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 4.90
Funders
- National Natural Science Foundation of China