'Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification

TL;DR

This paper introduces Aariz, a comprehensive and diverse dataset of 1000 cephalometric radiographs with expert annotations for landmarks and CVM stages, aiming to advance automated orthodontic analysis.

Contribution

The creation of a large, diverse, and meticulously annotated dataset for cephalometric landmark detection and CVM classification, addressing previous data limitations in orthodontic AI research.

Findings

First dataset with 29 landmarks and CVM labels from diverse imaging devices.

Enables development of robust automated landmark detection systems.

Facilitates improved orthodontic diagnosis and treatment planning.

Abstract

The accurate identification and precise localization of cephalometric landmarks enable the classification and quantification of anatomical abnormalities. The traditional way of marking cephalometric landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeavours to develop automated landmark detection systems have persistently been made, however, they are inadequate for orthodontic applications due to unavailability of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate the development of robust AI solutions for quantitative morphometric analysis. The dataset includes 1000 lateral cephalometric radiographs (LCRs) obtained from 7 different radiographic imaging devices with varying resolutions, making it the most diverse and comprehensive cephalometric dataset to date. The clinical experts of our team meticulously annotated each radiograph with 29 cephalometric landmarks, including the most significant soft tissue landmarks ever marked in any publicly available dataset. Additionally, our experts also labelled the cervical vertebral maturation (CVM) stage of the patient in a radiograph, making this dataset the first standard resource for CVM classification. We believe that this dataset will be instrumental in the development of reliable automated landmark detection frameworks for use in orthodontics and beyond.