Radu Alexandru, Tibeica Andreea, Curca Razvan, Agop-Forna Doriana, Cretu Ionut Cosmin, Camilar Maria, Forna Norina
ABSTRACT
This paper compares technician-driven CAD against automated CNN frameworks using 45 digital preparation models. Results show that AI minimizes human error, keeping marginal and internal discrepancies under the critical clinical threshold. While deep learning accelerates design speed by over 80%—enabling same-day chairside restorations—expert clinical validation remains mandatory to verify dynamic patient occlusion.(1)(2)
Aim of the study: The primary objective of this comprehensive academic investigation is to evaluate the foundational computational paradigms, automated software integrations, mathematical optimization matrices, and current clinical boundaries of artificial intelligence (AI) workflows within fixed and removable dental prosthodontics, establishing a technical benchmark against conventional human-driven workflows.(3) Materials and methods: A rigorous multi-center evaluation of digital dental workflows was carried out by cross-analyzing margin line detection errors, total computational design runtimes, and three-dimensional internal adaptation metrics across 45 distinct clinical digital preparation datasets. These datasets were allocated into two technical pathways: Group A utilized standardized, user-guided computer-aided design (CAD) systems, whereas Group B utilized a fully automated, deep-learning convolutional neural network (CNN) architecture trained on verified multi-dimensional dental topographies.(4) Results: The deep-learning computational models demonstrated automated finish line tracking precision rates that significantly surpassed human operator variations, reducing the mean margin delineation discrepancy from 42.6±4.2µm in the manual cohort down to 18.3±1.9µm in the automated cohort. The structural internal adaptation spaces were consistently bounded below the 35µm threshold, which optimizes cement distribution. Furthermore, total computational design cycles were compressed by more than 80%, enabling effective chairside restorative generation.(5) Conclusions: AI architectures provide measurable structural, precision, and efficiency enhancements to modern restorative dental workflows. While these deep-learning models eliminate subjective technician errors and optimize material biomechanics, direct expert human oversight remains indispensable to validate contextual aesthetic profiles and dynamic patient-specific jaw relationships.(6)(7)