Synopsis:

One of the more challenging diagnostic efforts in dentistry is the assessment of the structural integrity of teeth and dental implants.  Cracked teeth, deteriorating/fracturing restorations, loss of cement seal or loss of osseointegration are often hard to diagnose until the results are advanced or catastrophic.

Quantitative Percussion Diagnostics (QPD) has been shown in over 29 peer reviewed studies in engineering and dental journals to be an accurate and reliable test for determining the structural wellness of teeth or implants.  QPD is a mechanical test similar to evaluation methods used in engineering for assessing structural stability of objects, in contrast to visually based dental diagnostics such as radiographs and visual examination.  The mechanism that creates QPD data has been illustrated by Finite Element Analysis of novel models of uncracked and cracked teeth.  

QPD, FEA modeling, fatigue fracturing of 3D printed tooth models, plus the addition of AI and Machine Learning are currently creating a future for dentistry of predictive diagnostics, where structural problems can be diagnosed early and rated as to type or severity.  Preventive or therapeutic steps can then be instituted earlier in the damage cycle prior to the occurrence of advanced or catastrophic results, providing dentists with a new aid to diagnostics.

Learning Objectives:

1. Understand how percussion can be a diagnostic risk assessment aid
2. Know the scientific foundation of QPD
3. Envision the power of AI/ML combined with increasing amounts of QPD data
4. Explore the clinical possibilities of QPD