What question did this study set out to answer?

The aim is to analyze deep learning architectures used in oral rehabilitation and offer guidance on their selection based on specific tasks.

April 12, 2026Open Access

Artificial Intelligence Architectures in Oral Rehabilitation: A Focused Review of Deep Learning Models for Implant Planning, Prosthodontic Design, and Peri-Implant Diagnosis

Puntos clave

The aim is to analyze deep learning architectures used in oral rehabilitation and offer guidance on their selection based on specific tasks.
Conducted a focused narrative review of 10 studies, each addressing deep learning in oral rehabilitation.
Emphasized key tasks such as implant planning using cone–beam computed tomography segmentation.
Assessed evidence certainty and performance metrics using GRADE-informed ratings.
Performed a computational and hardware feasibility analysis for various architecture families.
Encoder–decoder networks are most effective for CBCT segmentation in implant planning.
Detection architectures enhance implant localization and peri-implant assessment on radiographs.
Generative models can effectively design crowns from 3D scans.
Hybrid architectures show potential for integrating CBCT and intraoral scan data.

Resumen

Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across tasks, modalities, and validation designs. The goal of this study was to critically analyze deep learning architecture families applied to oral rehabilitation and to provide task-driven selection guidance supported by an evidence table reporting dataset characteristics, validation strategy, and performance metrics. A focused narrative review was conducted using transparent, database-specific search criteria (final n = 10 included studies), emphasizing implant planning (cone–beam computed tomography CBCT-based segmentation), prosthodontic design (intraoral scan IOS/mesh inputs), and peri-implant diagnosis (periapical/panoramic radiographs). Evidence certainty for each clinical task was assessed using GRADE-informed ratings (High/Moderate/Low/Very Low). Extracted variables included clinical task, imaging modality, dataset size, architecture, validation strategy (internal vs. internal + external), split level, ground truth protocol, and performance metrics. A structured computational and hardware feasibility analysis was conducted for each architecture family to support real-world deployment planning. Encoder–decoder networks (U-Net/nnU-Net) dominate CBCT segmentation for implant planning, while detection architectures (Faster R-CNN, YOLO) support implant localization and peri-implant assessment on radiographs. Generative models (3D GANs, transformer-based point-to-mesh networks) enable crown design from three-dimensional scans. Hybrid CNN–Transformer architectures show promise for multimodal CBCT–IOS fusion, though direct evidence from the included studies remains limited to a single study. External validation remains uncommon yet essential given the risk of domain shift. In conclusion, architecture selection should be anchored to task geometry (2D vs. 3D), artifact burden, and required clinical output type. Reporting standards should prioritize dataset transparency, validation rigor, multi-center external testing, and uncertainty-aware outputs.

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