ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction

2026-05-07

AI Summary

This study addresses the challenge of accurately predicting dental implant position and angulation in surgical guide design, where limited textural information in medical images of the implant site hinders precise localization. To overcome this, the authors propose ImplantMamba, the first method to integrate the Mamba architecture into this domain. It employs a CNN-Mamba hybrid encoder that leverages CNNs for local anatomical feature extraction and utilizes Mamba’s selective state space mechanism to model global contextual dependencies among adjacent teeth. Furthermore, a novel Slope-Coupled Prediction (SCP) branch is introduced to jointly regress implant position and angulation, ensuring both anatomical plausibility and internal consistency. Evaluated on a large-scale clinical dataset, the proposed approach significantly outperforms existing methods, achieving more accurate and clinically compliant implant placement.

Abstract

In the design of surgical guides for implant placement, determining the precise implant position is a critical step. However, the implant region itself is often characterized by a lack of distinctive texture in medical images. Consequently, artificial intelligence (AI) models must infer the correct implant position and angulation (slope) primarily by analyzing the texture of the surrounding teeth, which poses a significant challenge. To address this, we propose ImplantMamba, a network architecture designed for long-range sequential modeling to integrate texture information from adjacent teeth. Our approach explicitly couples the regression of the implant position with its slope. The core of ImplantMamba is a hybrid encoder that combines Convolutional Neural Networks (CNNs) with Mamba layers. This design enables the network to hierarchically extract local anatomical features through CNNs while simultaneously modeling global contextual dependencies across the entire scan volume via Mamba's selective scan operations, leading to a more comprehensive understanding of the implant site. Furthermore, we introduce a Slope-Coupled Prediction Branch (SCP). This branch is designed to connect the prediction of implant position with the slope, ensuring internal consistency and anatomical plausibility by thereby enforcing a coherent relationship between the predicted implant location and its angulation. Extensive experiments on a large-scale dental implant dataset demonstrate that the proposed ImplantMamba achieves superior performance compared to existing methods.

Problem

• dental implant
• position prediction
• texture deficiency
• angulation
• medical image

Innovation

• Mamba
• long-range sequential modeling
• slope-coupled prediction
• hybrid CNN-Mamba encoder
• dental implant positioning