Dr. Shah will be presenting two conference publications at the 41st EMBConference in Berlin, Germany between July 23-27, 2019. Hosted by the IEEE Engineering in Medicine and Biology Society, the flagship conference will consist of workshops on topics such as medical imaging and modeling, machine learning, healthcare, biofeedback, and deep learning, followed by minisymposia and poster and oral presentation sessions and is attended by leading researchers in the field. Dr. Shah and his colleagues will present two papers, one as an invited oral talk and another as a poster, at this conference which have been accepted for publication:
1. Automated process incorporating machine learning segmentation and correlation of oral diseases with systemic health
Gregory Yauney, Aman Rana, Lawrence Wong, Perikumar Javia, Ali Muftu, Pratik Shah
Abstract: Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for healthconditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indicating a learned association between disease signatures in collected images. Periodontal diseases were more prevalent among males (p=0.0012) and older subjects (p=0.0224) in the screened population. Physicians independently examined the collected images, assigning localized modified gingival indices (MGIs). MGIs and periodontal disease were then cross-correlated with responses to a medical history questionnaire, blood pressure and body mass index measurements, and optic nerve, tympanic membrane, neurological, and cardiac rhythm imaging examinations. Gingivitis and early periodontal disease were associated with subjects diagnosed with optic nerve abnormalities (p<0.0001) in their retinal scans. We also report significant co-occurrences of periodontal disease in subjects reporting swollen joints (p=0.0422) and a family history of eye disease (p=0.0337). These results indicate cross-correlation of poor periodontal health with systemic health outcomes and stress the importance of oral health screenings at the primary care level. Our screening process and analysis method, using images and machine learning, can be generalized for automated diagnoses and systemic health screenings for other diseases.
2. Digital reconstruction and tomography of teeth using near-infrared light
(Selected for oral presentation at the conference)
Keith Angelino, Gregory Yauney, Aman Rana, David Edlund, Pratik Shah
Abstract: Cone beam computed tomography has demonstrated value by offering enhanced conceptualization of features of teeth in the 3D space. However, these systems require higher effective radiation doses to image teeth. Previous research from our group has used non-ionizing near-infrared (NIR) light for diagnosing demineralization and caries in human tooth enamel. However, use of safe NIR radiation for rapid, 3D imaging of tooth anatomy has not been described previously. Here we describe a optical setup to rapidly laser scan teeth ex vivo using 1310nm NIR laser diode.We also detail a novel process that uses laser scanning to create stacks of images of extracted teeth, and construct highly accurate 3D models. Our 3D reconstructive models offer promising starting points to recover anatomical details using pixel intensities within these images as projection data to diagnose carious lesions, and can assist in providing rapid and affordable technology-enabled early caries screenings to patients.