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Application Of Machine Learning Technologies For Detection Of Proximal Lesions In Intraoral Digital Images: In Vitro Study., Rohit Vadlamani

Electronic Theses and Dissertations

Background: Interpretation of bitewing radiographs is influenced by factors such as acquisition parameters (e.g. exposure, type of sensor), clinical technique, visualization (e.g. monitor type and calibration) and the observer (e.g. experience and fatigue bias). We hypothesized that the use of artificial intelligence (AI) will reduce visualization and observer factor in bitewing interpretation and improve diagnostic accuracy. Objective: The purpose of the present study was to evaluate the use of AI in the form of a machine-learning algorithm to detect and quantify proximal lesions compared with human trained observers. Methods: 16,000 anonymized, digital bitewings of patients were hand searched and non-bitewing, …