Liang, A., Ge, J., Liu, Z., Han, X., Hou, S., Li, G., Liu, M., & Zhao, J. (2025). Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-90679-9
ISO-690 (author-date, English)LIANG, Aohui, GE, Jiaming, LIU, Zhaowei, HAN, Xiangli, HOU, Songtao, LI, Gang, LIU, Ming und ZHAO, Jing, 2025. Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method. Scientific Reports. 1 Dezember 2025. Vol. 15, no. 1, . DOI 10.1038/s41598-025-90679-9.
Modern Language Association 9th editionLiang, A., J. Ge, Z. Liu, X. Han, S. Hou, G. Li, M. Liu, und J. Zhao. „Reliability of Noninvasive Hyperspectral Tongue Diagnosis for Menstrual Diseases Using Machine Learning Method“. Scientific Reports, Bd. 15, Nr. 1, Dezember 2025, https://doi.org/10.1038/s41598-025-90679-9.
Mohr Siebeck - Recht (Deutsch - Österreich)Liang, Aohui/Ge, Jiaming/Liu, Zhaowei/Han, Xiangli/Hou, Songtao/Li, Gang u. a.: Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method, Scientific Reports 2025,
Emerald - HarvardLiang, A., Ge, J., Liu, Z., Han, X., Hou, S., Li, G., Liu, M. und Zhao, J. (2025), „Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method“, Scientific Reports, Vol. 15 No. 1, verfügbar unter:https://doi.org/10.1038/s41598-025-90679-9.