@inproceedings{4dfa8767b0134f7581a478d9a29cd40f,
title = "Smartphone-Based Method for Detecting Periodontal Disease",
abstract = "In this paper, we propose a novel periodontal disease detection method using smartphones, image processing, and machine learning techniques. Periodontal disease is an inflammatory disease known to be the main cause of tooth loss. Here, a CIELAB color space is adopted for feature extraction and the support vector machine (SVM) is applied for distinguishing healthy gum from diseased gum. A gadget is designed to block ambient light and eliminate refraction effect as well. We recruited 30 subjects consisting of 15 gum-diseased and 15 healthy subjects. Experimental results show that our proposed method detects periodontal infection with 94.3\% accuracy, 92.6\% sensitivity, and 93\% specificity, respectively.",
keywords = "CIELAB, Color space, Mobile health, Periodontal disease, Smartphone, Support vector machine (SVM)",
author = "Behnam Askarian and Fatemehsadat Tabei and Tipton, \{Grace Anne\} and Chong, \{Jo Woon\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019 ; Conference date: 20-11-2019 Through 22-11-2019",
year = "2019",
month = nov,
doi = "10.1109/HI-POCT45284.2019.8962844",
language = "English",
series = "2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "53--55",
booktitle = "2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019",
}