AssistDent® Technology

AssistDent® Technology

Powerful AI assisted image analysis to help dentists identify dental decay sooner, in particular enamel-only proximal caries

An overview of the tools and methods our technology uses to help dentists achieve better detection rates

1.

We get machine learning algorithms to do what dentists find difficult

AssistDent, developed and patented by Manchester Imaging Limited, uses a range of machine learning algorithms to identify regions of bitewing radiographs which are indicative of enamel-only proximal caries. Detection of early enamel interproximal caries by dentists is known to be difficult and yet once identified is at the stage when progress may be prevented, or in some cases reversed, with measures such as cleaning or diet advice, fluoride varnishes and resin infiltration. A higher-grade caries that has penetrated beyond the enamel into dentine can be detected with relative ease by dentists, but preventative action is no longer possible.

2.

We go to great lengths to train AssistDent to the highest standard

For machine learning algorithms to offer clinical gains, the training examples need to be as close to a recognised Gold Standard as possible. Any false positive examples in the training data will be captured as true positives in the models and result in similar features being erroneously labelled positive in the analysed images.

Manchester Imaging has taken great care to ensure that data used to train the AssistDent® machine learning algorithms are of the highest possible standard. Five internationally recognised dento-maxillofacial radiologists were instructed to independently identify proximal caries in a set of training images. A consensus data set from these experts, deemed to be the Gold Standard, was then used to train the AssistDent machine learning models. The result is a software tool which uses the combined expertise from five authoritative dento-maxillofacial radiologists in its learning algorithm and is able to detect the often very subtle patterns indicative of enamel-only proximal caries.

3.

We ensure the AI algorithms are continuously improved

The AssistDent model of machine learning uses offline training, meaning the training exercise is separate from the run-time execution of the software, and allows Manchester Imaging to train the models using expert observers.

An alternative model of machine learning is online learning. In this case human input into the process at run-time continually adjusts and improves the models. This is often used in applications where the intention is to achieve “human-like” performance such as face or object recognition. In the case of caries detection, the intention is to improve on the general human performance, making the performance by the dentists more like an expert dentomaxillofacial radiologist. Online training by the dentist would result in the models becoming increasingly trained to behave like the dentist and less like the expert. An automated caries detection system is only worth having if it improves dentists’ performance.

Manchester Imaging Limited

Room E43, Sackville Street Building
Sackville Street
Manchester, M1 3BU
United Kingdom

© Manchester Imaging 2021 / Privacy Policy / Created by HTP Digital

Manchester Imaging Limited

Room E43
Sackville Street Building
Sackville Street
Manchester, M1 3BU
United Kingdom

© Manchester Imaging 2021
Privacy Policy
Created by HTP Digital