Mamba-Based Deep Learning Model for Automated Periapical Index Classification Using Periapical Radiographs and...

Apical periodontitis (AP) diagnosis primarily relies on periapical radiographs (PRs) and the Periapical Index (PAI) scoring system. However, existing automated approaches often simplify PAI into binary categories or ignore essential clinical metadata, limiting diagnostic performance and applicability. Such limitations hinder timely and accurate diagnosis of AP, which may complicate treatment planning by creating uncertainty about the appropriate timing and type of intervention, and ultimately challenge clinicians' ability to make consistent and informed decisions. This study aimed to develop and validate a novel Mamba-based classification model that integrates PR with structured clinical metadata to predict detailed PAI scores across the full 5-class.

In this retrospective diagnostic accuracy study, PRs and corresponding metadata-including patient age, tooth location, tooth number and arch type-were collected from a single institution. Two expert endodontists independently assigned PAI scores (1-5) based on Ørstavik's criteria, with the final reference standard set by consensus. The proposed artificial intelligence (AI) model utilized a Mamba-based state-space architecture to capture spatial dependencies and incorporate structured clinical metadata features. Training and evaluation were conducted using stratified 5-fold cross-validation.

The model achieved 54.72% accuracy and a quadratic-weighted kappa (QWK) of 0.713 in 5-class classification, outperforming the latest models based on convolutional neural networks (CNNs) and object detection networks. Ablation analysis further supported the value of integrating patient information, showing that age was the largest impact on model performance. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis for model explainability demonstrated that the model's highlighted areas were aligned with clinically meaningful periapical regions.

The proposed model addresses limitations of prior methods by leveraging the full range of the PAI scores and incorporating structured clinical information. It has the potential to support more consistent radiographic interpretation, reduce inter-examiner variability and serve as an interpretable tool in educational and clinical decision-support.