Does the FARNet neural network algorithm accurately identify Posteroanterior cephalometric landmarks?

ABSTRACT:

Background

We explored whether the feature aggregation and refinement network (FARNet) algorithm accurately identified posteroanterior (PA) cephalometric landmarks.

Methods

We identified 47 landmarks on 1,431 PA cephalograms of which 1,177 were used for training, 117 for validation, and 137 for testing. A FARNet-based artificial intelligence (AI) algorithm automatically detected the landmarks. Model effectiveness was calculated by deriving the mean radial error (MRE) and the successful detection rates (SDRs) within 2, 2.5, 3, and 4 mm. The Mann-Whitney U test was performed on the Euclidean differences between repeated manual identifications and AI trials. The direction in differences was analyzed, and whether differences moved in the same or opposite directions relative to g

SUBMITTER: Gonca M 

PROVIDER: S-EPMC11526671 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature