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CBCT-Based Orthodontic Classification Using Commercial AI: Completeness and Accuracy in Independent Validation.

Natalia Kazimierczak¹, Nora Sultani², Szymon Krzykowski²

¹Kazimierczak Clinic, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland.

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|February 27, 2026

Summary

The Diagnocat artificial intelligence (AI) platform shows poor diagnostic reliability for orthodontic diagnoses from cone-beam computed tomography (CBCT) scans, failing to generate sufficient data for clinical use.

Keywords:

CBCT artificial intelligence cephalometry diagnostic accuracy machine learning orthodontics

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Area of Science:

Orthodontics
Artificial Intelligence
Radiology

Background:

Artificial intelligence (AI) tools are emerging for orthodontic diagnosis using CBCT.
Limited evidence exists on AI performance in CBCT-based orthodontic assessments.
The Diagnocat platform's diagnostic reliability for CBCT data needs evaluation.

Purpose of the Study:

To assess the diagnostic reliability of the Diagnocat AI platform.
To evaluate categorical orthodontic diagnoses derived from CBCT examinations.

Main Methods:

Fifty-nine patients with CBCT and cephalograms were analyzed.
CBCT scans were processed using the Diagnocat platform (v1.0).
AI outputs were compared to manual cephalometric analyses (reference standard).

Main Results:

AI platform generated skeletal and vertical classifications for <10% of patients.
Fair agreement (Cohen's kappa = 0.324) was observed for overbite categorization.
Low data completeness (<10% for skeletal parameters) indicates poor usability.

Conclusions:

The Diagnocat platform demonstrated insufficient diagnostic reliability and low data completeness.
The AI tool is currently unsuitable for independent clinical decision-making in orthodontics.
Further development is needed to improve AI performance in CBCT-based assessments.