Our work in Restorative Dentistry

A Multimodal Annotation and Segmentation System to Detect Carious Lesions From Intraoral Photographs

To develop and validate a multimodal annotation workflow for identifying carious and non-carious lesions using intraoral photographs, and to assess the effects of confidence thresholds on model performance.

Clinical Annotation and Segmentation Tool (CAST) 

This study aims to document the early stages of development of an unsupervised, deep learning-based tool capable of isolating clinically significant teeth in both intraoral photographs and their corresponding oral radiographs

Computer vision with smartphone microphotography for detection of carious lesions

To evaluate the similarities in microphotographic images across different smartphones and to establish whether computer vision can use microphotographs to successfully classify dental caries.

One-Stage Methods of Computer Vision Object Detection to Classify Carious Lesions from Smartphone Imaging

The study aimed to implement and validate an automation system to detect carious lesions from smartphone images using different one-stage deep learning techniques.

Visual diagnostics of dental caries using hybrid computer vision models

The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy

Digital Shade Matching in Dentistry: A Systematic Review

 The study aimed to synthesise literature that identified digital non-proximity recording instruments and associated colour spaces in dentistry and compare the clinical outcomes of digital systems with spectrophotometers and conventional visual methods.