Artificial intelligence for caries detection: Randomized trial

Objectives: We aimed to assess the impact of an artificial intelligence (AI)-based diagnostic-support software for proximal caries detection on bitewing radiographs. Methods: 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. The reference test had been established by 4 + 1 independent experts in a pixelwise fashion. Caries was subgrouped as enamel, early dentin and advanced dentin caries, and accuracy and treatment decisions for each caries lesion assessed. Results: Dentists with AI showed a significantly higher mean (95% CI) area under the Receiver-OperatingCharacteristics 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). Conclusion: AI can increase dentists' diagnostic accuracy but may also increase invasive treatment decisions. Clinical significance: AI can increase dentists' diagnostic accuracy, mainly via increasing their sensitivity for detecting enamel lesions, but may also increase invasive therapy decisions. 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.