ARTIFICIAL INTELLIGENCE AVAILABLE TO THE DEVELOPMENT OF A VIRTUAL REALITY SOFTWARE FOR AN AUTOMATED CEPHALOMETRIC ANALYSIS OF ULTRA-REDUCED CBCT FOV
type: CreativeWork
description: This thesis investigates the integration of artificial intelligence (AI), virtual reality (VR), and ultra-reduced cone-beam computed tomography (CBCT) fields of view (FOV) to develop a novel approach for automated cephalometric analysis in orthodontics. The study addresses key challenges such as enhancing diagnostic precision, ensuring radioprotection, and optimizing clinical workflows through innovative technological solutions. Cephalometric analysis, a cornerstone of orthodontic diagnosis and treatment planning, has traditionally relied on manual or semi-automated methods using 2D radiographs. However, these methods face limitations, including superimposition of structures and the inability to assess depth. While 3D CBCT imaging offers significant advantages, its associated higher radiation exposure raises safety concerns, particularly for pediatric patients and cases requiring multiple imaging sessions. This research responds to the need for safer, more efficient imaging solutions by focusing on AI-driven automation and ultra-reduced FOVs in CBCT imaging. The project developed an AI system based on the V-Net deep learning architecture to automate the detection of anatomical landmarks critical for cephalometric analysis. Using a robust training and validation process with expert-annotated CBCT datasets, the system achieved high accuracy, with a mean Euclidean distance error of less than 2 mm. Preprocessing techniques, such as intensity thresholding and data augmentation, enhanced the model’s reliability and robustness. The introduction of ultra-reduced FOV CBCT scans further minimized radiation exposure by up to 70% compared to conventional protocols, without compromising diagnostic quality. The integration of intraoral scans with CBCT data provided a comprehensive and accurate anatomical representation, enhancing the precision of automated analyses. The use of VR technology added a transformative dimension to the system, offering an immersive platform for clinici…
abstract: This thesis investigates the integration of artificial intelligence (AI), virtual reality (VR), and ultra-reduced cone-beam computed tomography (CBCT) fields of view (FOV) to develop a novel approach for automated cephalometric analysis in orthodontics. The study addresses key challenges such as enhancing diagnostic precision, ensuring radioprotection, and optimizing clinical workflows through innovative technological solutions. Cephalometric analysis, a cornerstone of orthodontic diagnosis and treatment planning, has traditionally relied on manual or semi-automated methods using 2D radiographs. However, these methods face limitations, including superimposition of structures and the inability to assess depth. While 3D CBCT imaging offers significant advantages, its associated higher radiation exposure raises safety concerns, particularly for pediatric patients and cases requiring multiple imaging sessions. This research responds to the need for safer, more efficient imaging solutions by focusing on AI-driven automation and ultra-reduced FOVs in CBCT imaging. The project developed an AI system based on the V-Net deep learning architecture to automate the detection of anatomical landmarks critical for cephalometric analysis. Using a robust training and validation process with expert-annotated CBCT datasets, the system achieved high accuracy, with a mean Euclidean distance error of less than 2 mm. Preprocessing techniques, such as intensity thresholding and data augmentation, enhanced the model’s reliability and robustness. The introduction of ultra-reduced FOV CBCT scans further minimized radiation exposure by up to 70% compared to conventional protocols, without compromising diagnostic quality. The integration of intraoral scans with CBCT data provided a comprehensive and accurate anatomical representation, enhancing the precision of automated analyses. The use of VR technology added a transformative dimension to the system, offering an immersive platform for clinici…
author: SERAFIN, MARCO
language: eng
keywords: 3D cephalometry, artificial intelligence, deep learning, orthodontics, Settore MEDS-16/A - Malattie odontostomatologiche
date published: 2025-03-25
date modified: 2026-07-27T20:19:05Z
provider: Università degli Studi di Milano
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli Studi di Milano

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2434/1157055

PropertyValue
property ID: DOI
value: 10.13130/serafin-marco_phd2025-03-25