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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Camera-Based Monocular Depth Estimation in Orthodontics: Vision Transformer vs. CNN Model Performance.

Arda Arısan¹, Gökhan Serhat Duran²

¹Independent Researcher, Ankara 06000, Turkey.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

Monocular Depth Estimation (MDE) using vision transformers can accurately assess facial profiles from 2D photos. This computer vision technique shows promise for orthodontic diagnosis and treatment planning.

Keywords:

Convolutional Neural Networks Monocular Depth Estimation computer vision medical imaging orthodontic diagnostics vision transformer

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Area of Science:

Computer Vision
Medical Imaging
Orthodontics

Background:

Monocular Depth Estimation (MDE) predicts depth from single 2D images.
Orthodontics utilizes facial soft-tissue evaluation for diagnosis and treatment.
MDE offers potential for sagittal profile information from standard frontal photographs.

Purpose of the Study:

To determine if MDE can extract clinically meaningful data for facial profile assessment.
To evaluate the efficacy of MDE in orthodontic applications.

Main Methods:

Retrospective analysis of 82 adult patients' frontal photos and cephalometric radiographs.
Annotation of Upper Lip Anterior (ULA), Lower Lip Anterior (LLA), and Soft Tissue Pogonion (Pog') landmarks.
Comparison of MDE-derived depth rankings against cephalometric true vertical line (TVL) analysis.
Evaluation of DPT-Large (vision transformer) and CNN-based models (DepthAnything-v2, ZoeDepth).

Main Results:

Vision transformer DPT-Large achieved 92.7% accuracy, significantly outperforming CNN models.
CNN models DepthAnything-v2 (9.8%) and ZoeDepth (4.9%) performed below chance level (16.7%).
DPT-Large demonstrated clinically acceptable accuracy in most cases.

Conclusions:

Vision transformer-based MDE shows potential for clinically meaningful soft-tissue profiling from frontal photos.
Depth information from 2D images can support facial profile evaluation in orthodontics.
Findings lay groundwork for integrating depth-based analysis into digital orthodontic diagnostics.