Measuring Endodontic Working Length Using Artificial Intelligence

Abstract

The objective of an endodontic treatment is to eliminate infection and inflammation caused by microbes within root canal and the Periapical region of the tooth. This involves cleaning, shaping, disinfecting, and closing the canals to the proper working length. With traditional image processing methods, it is difficult to precisely measure the root canal length. This retrospective clinical study evaluates a self-created dataset of X-ray images of teeth, annotated by medical technicians with defined root canal measurements, was used as input for the system. To measure the endodontic working length the proposed system involvesseveral steps which includes high-resolution dental image acquisition, pre-processing and reducing noise through Gaussian filtering and improving contrast with histogram equalization, Image cropping, Segmentation using Thresholding methods and edge detection with Canny algorithms, Bounding box to get tooth height and calculation of measurement of root canal length by measuring the distance along the skeleton from the entrance to the apex, with curvature analysis algorithms. The results of proposed measurement are validated with review by dental professionals and found that system shows 86.51 % of accuracy. The use of artificial intelligence for root canal measurement significantly advances dental diagnostics. By automating the measurement process, the Artificial Intelligence (AI) system enhances accuracy, efficiency, and reproducibility, ultimately contributing to improved patient outcomes.