Publication

Automated segmentation of gingival diseases from oral images

A. Rana, G. Yauney, L. C. Wong, O. Gupta, A. Muftu, P. Shah. "Automated segmentation of gingival diseases from oral images." 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT). IEEE. 2017. DOI: 10.1109/HIC.2017.8227605

Abstract

Periodontal diseases are the largest cause of tooth loss among people of all ages and are also correlated with systemic diseases such as endocarditis. Advanced periodontal disease comprises degradation of surrounding tooth structures, severe inflammation and gingival bleeding. Inflammation is an early indicator of periodontal disease. Early detection and preventive measures can help prevent serious occurrences of periodontal diseases and in most cases restore oral health. We report a machine learning classifier, trained with annotations from dental professionals, that successfully provides pixel-wise inflammation segmentations of color-augmented intraoral images. The classifier successfully distinguishes between inflamed and healthy gingiva and its area under the receiver operating characteristic curve is 0.746, with precision and recall of 0.347 and 0.621 respectively. Dental professionals and patients can benefit from automated point-of-care early diagnosis of periodontal diseases provided by this classifier using oral images acquired by intraoral imaging devices. 

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