Digitization and validation of the open bite checklist manifesto: a step toward artificial intelligence

Area of Science:

• Dentistry
• Medical Informatics

Background:

• Anterior open bite (AOB) diagnosis requires comprehensive assessment of skeletal, dental, and soft tissue factors.
• Current diagnostic methods may lack standardization and completeness.

Purpose of the Study:

• To introduce and validate novel computer software for diagnosing anterior open bite (AOB).
• To assess the software's efficiency and reliability compared to conventional diagnostic approaches.

Main Methods:

• Software developed using an algorithm based on a standardized AOB checklist (skeletal, dental, soft tissue, smile characteristics).
• Digital form output (DFO) generated by software, detailing AOB phenotype, components, severity, and functional factors.
• DFO compared against conventional form output (CFO) based on expert opinion for validation.

Main Results:

• High agreement (82.2%, kappa=0.78) between DFO and CFO for AOB phenotype.
• DFO demonstrated greater reliability, with fewer missing diagnostic components compared to CFO.
• Statistically significant correlation between missing components in CFO and disagreement levels.

Conclusions:

• The new software is an efficient and valid tool for diagnosing AOB and its contributing factors.
• DFO offers superior comprehensiveness and reliability over CFO.
• The software's algorithm can serve as a foundation for future AI-driven AOB diagnostic models.