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
