Mr. Andrew Peitzsch Profile

Andrew Peitzsch

at Univ of Connecticut

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Proceedings Article | 16 March 2020 Paper

Verification of accuracy of an algorithmic image-based dental pulp vitality test

Sarah Bi, Laura Martinez , Justin Bequette, Andrew Peitzsch, William D'Angelo

Proceedings Volume 11314, 1131443 (2020) https://doi.org/10.1117/12.2549744

KEYWORDS: Teeth, Blood, Video, Cameras, Diagnostics, Computer simulations, Blood circulation, Oximetry, Light emitting diodes, Imaging systems

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Background: Endodontic maladies incur significant costs to the military and are difficult to diagnose. Early identification and treatment of endodontic conditions are critical. Our research focused on designing a novel, vitality-based photoplethysmography imaging (PPGI) system capable of determining the pulse frequency within the dental pulp, allowing for direct, accurate, real-time visualization of tooth vitality. Objective: The objective of this study was to verify the accuracy of a developed video stabilization and digital signal processing algorithm – Pulp Assessment by Local Observation (PABLO). Methodology: MATLAB was used to create the PABLO video analysis algorithm to determine the pulse frequency of the dental pulp from PPGI signals. In order to verify the accuracy of this algorithm, four trials were conducted to test the algorithm under various parameters. The control group was simply a measurement of the algorithm’s ability to detect a pulse within an ex vivo tooth model. Following this, separate trials were conducted to test the effects of a simulated gumline, adjacent teeth, and a video stabilization protocol on the algorithm’s accuracy. Results: Video recordings from the ex vivo model were analyzed using the PABLO algorithm to determine its accuracy in detecting the pulse frequency of the pulp. Results of the analysis showed that the algorithm had a pulse detection sensitivity above 90% and a percent error less than 11% in all trials. In the control case, results showed a sensitivity of 93% and a pulse detection error of 7.3%, indicating that this algorithm has promise as a diagnostic tool for clinicians.

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