Join me for a podcast summary looking at Ai in orthodonticsand its clinical application. A growing topic in orthodontics, and one of themost featured topics at this years AAO. This summary is based on 3 lectures fromthis year’s summer meeting by Juan Francisco Gonzalez & Jean Marc Retrouvey,Tarek ElShebiny , Jonas Bianchi and Lucia Cevidanes. We will look whatAi is, the way it works and its clinical application, as well as a criticalview on this young field.

What is Ai:

1.       Technology that enables computers and machinesto simulate human intelligence, perform 1 task very well, e.g. voice command, Youtuberecommendations

2.       Predictive modelling, makes calculations,  convert information into numbers or categoriesand recognise patterns

Levels of Ai: Machine learning, Neural Networks and Deep Learning

1.       Machine learning

a.       The ability for a machine to learn from data andpast experience to identify patterns and make predictions

2.       Neural Networks  

a.       Specific model which relies on interconnectednodes, which perform a mathematical calculation of associations , patterns, andprobabilities

3.       Deep learning

a.       Is a complex version of neural networks

Virtual patient

·     CBCT segment + STL file – segmentation of theteeth and roots, with labelling of different stuctures

o  Can print model, visualise ideal vector andcalculate ideal vector

o  However clinician still required to establish biomechanics

·     CBCT integration for aligner cases, Unpublishedthesis Khalid Alotaibi:

o  Treatment planning confidence increased 50%, leastchange was treatment planning modification

Diagnostic data:

·     Ai cephalometric tracing

o  46% of 24 landmarks 2.0mm within

o  4 different programmes  Iortho, Webceph, Orthodc, cephx

o  All landmarks had good overall agreement butvariation in identification

·     Facial Analysis

·     Automated 3D facial asymmetry analysis usingmachine learning  Adel 2025

o  Study – 7 landmarks

o  Identified manually and with deep learning

o  5 accurate, 2 significant difference but notclinically relevant

Diagnostic accuracy of photos

·     Clinical photos assessment by Ai, and comparedto clinical examination

·     Sensitivity 72%, specificity 54% Vaughan & Ahmed2025

Growth prediction

·     Poor agreement age 9

Comparison between direct, virtual and AI bonding

·     DIBs – uses Ai for bonding

·     Compare Ai Vs user modified indirect bonding Vsdirect bonding (gold standard), 0.5mm significant

·     Incisors accurate

·     Premolars and lower laterals inaccurate

Monitoring

Previous podcast exploring the accuracy of remote monitoring

o   with Ferlito 2022 80%repeatability from 2 scans 44.7% repeatability and reproducibility 

Bracket removal from scan and retainer fit

Tarek Assessment of virtual bracket removal by artificialintelligence and thermoplastic retainer fit AJODO 2024

o  Retainers for both – clinically acceptable

FDA approval of Ai in dentistry

·     FDA - Software of Medical Diagnosis

§ 4  dental:

·     Dental Monitoring

·     Ray Co

·     X-Nav technologies

·     Densply Sirona

What’s next

·     More data learning to train AI model

·     Robotics customising appliances per patient