Reliability of Artificial Intelligence for Treatment Decision for Adult Skeletal Open Bite Patients (AIASOBP)

Cairo University (CU)

Status: Completed

Conditions: Artifical Intelligence; Openbite

Study type: Observational

Funder types: Other

Identifiers

NCT06992908

Self-funded by me. (Other Identifier)

Adult skeletal open bite cases

Details and patient eligibility

About

This study evaluated a specially designed AI model developed by a programmer using x-ray readings and corresponding treatment decisions from 70% of the cases (either orthodontic treatment only or orthodontic treatment with surgery).

For the evaluation, we will use the remaining 30% of cases. "Subsequently, to assess its performance, the model was tested on the remaining 30% of cases. The programmer provided only the X-ray readings as input. The AI model was then tasked with classifying.

Full description

In this study, all patients were treated completely with a well-finished result by expert orthodontists. This study evaluates whether an artificial intelligence (AI) model can enhance treatment decisions for adult skeletal open bite cases by predicting the optimal intervention, either orthognathic surgery or camouflage, using cephalometric readings as input data.

First, a total of 53 cases were analyzed, which were divided into two groups:

70% were allocated to the machine learning group (MLG), while the remaining 30% constituted the test group (TG). Cephalometric analysis for all patients was performed using Dolphin Imaging 11.5 Premium software, along with determining the appropriate treatment decision, either camouflage or orthognathic surgery.

The data obtained from MLG serves as training data for the AI model to classify cases based on their cephalometric data, whether for camouflage or orthognathic surgery. The input data consisted of cephalometric readings along with a decision.

Second, after machine learning, validation takes place to examine the ability of the machine to make decisions through some cases from MLG.

The third step will evaluate the machine's ability to accurately determine case decisions based on cephalometric readings. The results produced by the machine will be compared to the actual decisions made, as all these cases were treated under the supervision of orthodontic professors.

Enrollment: 53 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Moderate to severe Adult patients with anterior skeletal open bite (at least 3mm opening)
• Completed their treatment successfully.
• Well-documented cases with comprehensive preoperative and postoperative lateral cephalometric x-rays were considered.

Exclusion criteria

• Patient below 18 years.
• Improperly finished orthodontic treatment.
• Incomplete documentation.
• cleft lip and palate patient, patient with syndromes.
• Dental open bite.

Trial design

53 participants in 2 patient groups

Group 1: MLG acts as learning group.

Description:

Cephalometric readings parameters of cases with their decisions will be used in this group for training the machine to be able to make a decision.

Group 2: TG acts as a test group

Description:

The programmer will put parameters as input and then ask the AI model to decide on an output of the model, either camouflage or surgery.