AI in Orthodontics, Where Are We And Where Are We Going 10 MINUTE SUMMARY
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, Youtuberecommendations2. Predictive modelling, makes calculations, convert information into numbers or categoriesand recognise patterns Levels of Ai: Machine learning, Neural Networks and Deep Learning1. Machine learninga. 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 learninga. Is a complex version of neural networks Virtual patient· CBCT segment + STL file – segmentation of theteeth and roots, with labelling of different stuctureso Can print model, visualise ideal vector andcalculate ideal vectoro 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 tracingo 46% of 24 landmarks 2.0mm withino 4 different programmes Iortho, Webceph, Orthodc, cephxo All landmarks had good overall agreement butvariation in identification · Facial Analysis· Automated 3D facial asymmetry analysis usingmachine learning Adel 2025o 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 monitoringo with Ferlito 2022 80%repeatability from 2 scans 44.7% repeatability and reproducibility Bracket removal from scan and retainer fitTarek Assessment of virtual bracket removal by artificialintelligence and thermoplastic retainer fit AJODO 2024o 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
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