Clinical Trial Summary
The study titled "Reliability Of Artificial Intelligence for Treatment Decision
Recommendation of Adult Skeletal Class III Patients" aims to assess the accuracy and
dependability of artificial intelligence (AI) in providing treatment decision recommendations
for adult patients with skeletal Class III malocclusion. Skeletal Class III malocclusion is
characterized by an underdeveloped upper jaw or an overdeveloped lower jaw, leading to facial
and dental irregularities. The study focuses on evaluating whether AI-based recommendations
can reliably guide orthodontic treatment planning for this specific patient group.
This diagnostic test accuracy study involves collecting a diverse dataset of adult patients
diagnosed with skeletal Class III malocclusion. AI algorithms will be trained on this dataset
using various clinical and radiographic parameters to learn patterns and make treatment
recommendations. The study will then compare the AI-generated treatment recommendations to
those provided by experienced orthodontists.
Key aspects of the study include:
AI Reliability: The primary objective is to assess how consistently and accurately the AI
system can recommend appropriate treatment decisions for adult skeletal Class III patients.
Diagnostic Test Accuracy: The study will determine the sensitivity, specificity, positive
predictive value, and negative predictive value of the AI-generated treatment
recommendations. This analysis will highlight the AI's ability to correctly identify patients
who require specific treatment interventions.
Clinical Validity: Researchers will investigate whether the AI recommendations align with the
decisions made by experienced orthodontists. This assessment is crucial to establish the AI
system's clinical applicability.
Potential Benefits: If the AI system proves reliable and accurate, it could offer a
time-efficient and standardized method for treatment decision support, aiding orthodontists
in providing personalized care to adult skeletal Class III patients.
By conducting this study, researchers aim to contribute to the advancement of AI-assisted
medical decision-making within the field of orthodontics. Successful outcomes would have the
potential to revolutionize treatment planning processes, improve patient outcomes, and
provide a valuable tool for orthodontists to make informed treatment decisions for adult
skeletal Class III patients
