Artificial intelligence enhances caries diagnostic efficacy, reports study

According to recent research reports, AI algorithms are able to detect caries and caries-related cavities on machine-readable intraoral photographs with an accuracy of at least 90%. It has been documented that AI can increase dentists' diagnostic accuracy, mainly via increasing their sensitivity for detecting enamel lesions, but may also increase invasive therapy decisions. However,differences in the effects of AI for different dentists should be explored, and dentists should be guided as to which therapy to choose when detecting caries lesions using AI support.

To gain a deeper understanding on the matter, a team of researchers aimed to assess the impact of an artificial intelligence (AI)-based diagnostic-support software for proximal caries detection on bitewing radiographs.

For the study methodology, a cluster-randomized cross-over controlled trial was conducted. A commercially available software employing a fully convolutional neural network for caries detection (dentalXrai Pro, dentalXrai Ltd.) was randomly employed by 22 dentists, supporting their caries detection on 20 bitewings randomly chosen from a pool of 140 bitewings, with 10 bitewings randomly being supported by AI and 10 not.

Caries was subgrouped as enamel, early dentin and advanced dentin caries, and accuracy and treatment decisions for each caries lesion assessed.

Results revealed some interesting facts.

• Dentists with AI showed a significantly higher mean (95% CI) area under the Receiver-Operating-Characteristics curve (0.89; 0.87-0.90) than those without AI (0.85; 0.83-0.86; p<0.05), mainly as their sensitivity was significantly higher (0.81; 0.74-0.87 compared with 0.72; 0.64-0.79; p<0.05) while the specificity was not significantly affected (p>0.05).
• This increase in sensitivity was found for enamel, but not early or advanced dentin lesions. \
• Higher sensitivity came with an increase in non-invasive, but also invasive treatment decisions (p<0.05).

The clinical application of AI methods might potentially become feasible in the future but requires more fundamental research to overcome existing limitations and has to consider relevant differential diagnostic findings.

For full article follow the link: https://doi.org/10.1016/j.jdent.2021.103849

Source: Journal of Dentistry