Artificial Intelligence in the Detection and Classification of Dental Caries | Summary
AI models outperform dental professionals in detecting dental caries on radiographs, according to a study published in The Journal of Prosthetic Dentistry.
AI
In a study conducted by Ahmed et al., published in The Journal of Prosthetic Dentistry, researchers utilized advanced technology to improve the detection of dental caries. They collected and analyzed bitewing radiographs processed using software that segmented and anonymized the images. The researchers employed supervised learning algorithms trained on segmentation tasks to identify and classify carious lesions based on the modified King Abdulaziz University classification.
They utilized popular deep learning models – ResNet50, ResNext101, and Vgg19 – as encoders, all pretrained on ImageNet weights. Results showed promising outcomes. The model achieved a mean IoU score of 0.55 for proximal carious lesions and an F1 score of 0.535 using 554 training samples. The segmentation model displayed a sensitivity of 0.76, precision of 0.87, and an F1 score of 0.81. Comparison tests revealed that the AI models outperformed dental professionals when assessing F1 scores.
This study validates the potential of developing an accurate caries detection model that can expedite identification, improve clinician decision-making, and enhance patient care.