Work

In vitro Assessment of a Deep Learning–Based System for Computer-Aided Diagnosis and Treatment Planning in Orthodontics: A Virtual Study Using Digital Models

Year: 2025

Type: article

Abstract: Introduction: The use of artificial intelligence (AI) has the potential to improve both efficiency and accuracy in the process of automating orthodontic diagnosis and treatment planning operations. The purpose of this work is to evaluate the efficacy of a deep learning-based system for computer-aided diagnosis and treatment planning by using digital orthodontic models. Materials and Methods: The AI system was trained to recognize malocclusions and generate treatment recommendations by using a collection of one hundred digital models in three dimensions. It was determined whether the results generated by AI were comparable with the evaluations made by qualified orthodontists. Results: The technique obtained a high level of accuracy in malocclusion categorization, with sensitivity and specificity that were both greater than 90% in the majority of cases. Conclusion: Despite the fact that these findings lend credence to the potential of AI in orthodontic operations, more clinical validation requires further investigation.

Source: Journal of Pharmacy And Bioallied Sciences

DOI: 10.4103/jpbs.jpbs_1053_25

Authors: Amit Anthony, Maheswari Eluru, Rahul Sharma, Imad Mohammed, Merlin Jayaraj, Nevin Abraham

Institutions: Sinhgad Dental College and Hospital, Arizona State University

Language: English

Topic: Orthodontics and Dentofacial Orthopedics

Subfield: Orthodontics

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $250