Evaluating the accuracy of AI-based software vs human interpretation in the diagnosis of dental caries using intraoral radiographs: An RCT

Abstract

Background: Dental caries is a prevalent oral health issue, often diagnosed through intraoral radiographs. The accuracy of Artificial Intelligence (AI) in diagnosing dental caries from these radiographs is a subject of growing interest. Materials and Methods: In this RCT, 200 intraoral radiographs were collected from patients seeking dental care. These radiographs were independently evaluated by both AI-based software and experienced human dentists. The software utilized deep learning algorithms to analyze the radiographs for signs of dental caries. The performance of both AI and human interpretations was compared by calculating sensitivity, specificity, and overall accuracy. Arbitrary values of 85% sensitivity, 90% specificity, and 88% overall accuracy were set as benchmarks. Results: The AI-based software demonstrated a sensitivity of 88%, a specificity of 91%, and an overall accuracy of 89% in diagnosing dental caries from intraoral radiographs. Human interpretation, however, yielded a sensitivity of 84%, a specificity of 88%, and an overall accuracy of 86%. The AI-based software performed consistently close to or above the predefined benchmarks, while human interpretation showed slightly lower accuracy rates. Conclusion: This RCT suggests that AI-based software is a valuable tool for diagnosing dental caries from intraoral radiographs, with performance comparable to or exceeding that of experienced human dentists. The consistent accuracy of AI in this context highlights its potential as an adjunctive diagnostic tool, which can aid dental professionals in more efficient and precise caries detection.

Keywords

• artificial intelligence
• dental caries
• diagnosis
• intraoral radiographs
• randomized controlled trial
• sensitivity
• specificity