Detection of Finish Lines In Indirect Ceramic And Composite Veneers Using Clinical Images With The Help of Developed AI-based System

Artificial intelligence (AI) has shown great promise in dentistry, especially when using clinical photographs to identify and analyze finish lines for indirect ceramic and composite veneers. This study investigates the detection and assessment of finish lines in indirect ceramic and composite veneers by analyzing clinical images. Accurate identification of finish lines is crucial for ensuring the fit, durability, and aesthetic outcomes of dental restorations. We use deep learning to identify, detect, and classify finish lines from composite and ceramic veneers.A study analyzed 125 teeth prepared for indirect veneer restorations using high-resolution intraoral cameras, digital scanners, and magnification tools. Clinical images were captured to visualize finish line designs. The study compared imaging modalities for detecting finish lines, focusing on precision, reproducibility, and statistical differences. Experts annotated and segmented images for different restorations, and deep squeeze net embeddings were performed with 80 percent training and 20 percent test data.The study compares Logistic Regression and Naive Bayes models for predicting ceramic and composite veneer finish lines, finding Logistic Regression superior in precision and F1-score. Clinical imaging techniques offer reliable veneer assessment for optimal restorations, with further research recommended to explore advanced image processing methods and finish line detection accuracy.