Pratik Shah

Machine Learning and Automated Segmentation of Oral Diseases Using Biomarker Images

We report a novel method that processes biomarker images collected at the point of care and uses machine learning algorithms to provide a first level of screening against oral diseases. A machine learning classifier is trained to learn pixel-by-pixel mappings from RGB oral images and output areas with disease. This method can be adapted to process biomarker images from other organs as well.

Why is this work important?

Visual inspection and probing techniques have been traditionally used for diagnosis of oral diseases in patients. These traditional methods are subjective and not scalable. We describe the use of RGB color images acquired by low-cost camera devices coupled with machine learning to detect areas with poor oral health.

What has been done before?

Currently the gold standard for oral diagnosis is visual inspections by a dentist followed by X-rays. These methods are expensive and invasive.

What are our contributions?

We implement a novel technique to combine medical expert knowledge with biomarker signatures. We use RGB color images taken directly at the point-of-care, using low-cost hand-held devices, to provide a first level machine-learning powered screening for patients.

What are the next steps?

Expanding the repertoire of biomarkers that can be detected in RGB color images acquired at the point-of-care, and pairing them with automated machine learning exams.

Related projects

  1. Technology-Enabled Mobile Phone Screenings Augment Routine Primary Care

Dr. Pratik Shah

Dr. Pratik Shah

Faculty Member

Other Contributors

Dr. Lawrence C. Wong, DMD

Dr. Ali Muftu, DMD

G. Yauney, A. Rana, L. C. Wong, P. Javia, A. Muftu and P. Shah, “Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 3387-3393, doi: 10.1109/EMBC.2019.8857965.