Accuracy and Time Efficiency of AI Versus Manual Segmentation in Adult Crowding Using Aligner Software
NCT07919652, Active not recruiting, Dina ahmed
Trial 9
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
| Field | Value | Source |
|---|---|---|
| Title | Accuracy and Time Efficiency of AI Versus Manual Segmentation in Adult Crowding Using Aligner Software | ClinicalTrials.gov |
| NCT number | NCT07919652 | ClinicalTrials.gov |
| Status | Active not recruiting | ClinicalTrials.gov |
| Enrollment | 155 participants, actual | ClinicalTrials.gov |
| Lead sponsor | Dina ahmed | ClinicalTrials.gov |
| Conditions | Dental Crowding | ClinicalTrials.gov |
| Interventions | Automated tooth segmentation and manual segmentation | ClinicalTrials.gov |
| First posted | 2026-11-05 | ClinicalTrials.gov |
| Last updated | 2026-11-05 | ClinicalTrials.gov |
Registered outcomes 4
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description | Source |
|---|---|---|---|---|
| Primary | Accuracy and Success of AI-Based Tooth Segmentation | Baseline assessment immedialy after completion of AI-based and manual tooth segmentation of each pretreatment digital model . | The primary outcome is the accuracy and success of AI-based automated tooth segmentation compared with manual segmentation as the reference standard. Segmentation will be considered successful when all tooth margins are correctly identified and separated. Failure will be recorded when tooth margins are not correctly identified or when deficient or excessive segmentation is present. The performance of AI-based segmentation will be evaluated using the segmented tooth morphology and mesiodistal tooth width and compared with the manually segmented digital models. | ClinicalTrials.gov |
| Secondary | Tooth Segmentation Success Rate | Baseline assessment immedialy after completion of AI-based and manual tooth segmentation of each pretreatment digital model . | Percentage of teeth successfully identified and segmented by AI-based and manual segmentation, assessed by tooth identification and surface quality (occlusal, vestibular, and lingual surfaces), including adequate segmentation and deficit or surplus errors. Results will be stratified by tooth type and crowding severity. | ClinicalTrials.gov |
| Secondary | Frequency and Types of Segmentation Errors | Baseline assessment immedialy after completion of AI-based and manual tooth segmentation of each pretreatment digital model . | Frequency and types of segmentation errors identified during AI-based and manual tooth segmentation, including proximal contact reconstruction failures and gingival margin deficits, in adult patients with moderate-to-severe dental crowding. | ClinicalTrials.gov |
| Secondary | Time Efficiency of AI-Based Versus Manual Segmentation | Time required to complete AI-based and manual tooth segmentation of each pretreatment digital model, recorded immediately upon completion of each segmentation procedure. The time taken for each method will be measured in minutes and compared | Comparison of the time required to complete tooth segmentation using AI-based automated software versus manual segmentation, measured in minutes per digital model. | ClinicalTrials.gov |
Outcomes 0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
| Type | Outcome | Time frame | Population | Units | Measured as | Source |
|---|
Results by group 0
Each measurement the trial posted, group by group, in the words and numbers it posted.
| Outcome | Group | Category | Value | Spread | Units | Source |
|---|
Participant flow 0
How many started, completed or left each group, period by period.
| Period | Milestone | Group | Participants | Units counted | Source |
|---|
Adverse events 0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
| Type | Event | Organ system | Group | Affected | At risk | Events | Source |
|---|
Adverse event totals 0
The totals the trial posted for each group.
| Total | Group | Affected | At risk | Source |
|---|
Papers 4
The papers the trial lists on ClinicalTrials.gov.
Grants 0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
| Project | Title | Principal investigators | Organization | Funder | Fiscal years | Source |
|---|
Trial comparisons 0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
| Comparison | Source |
|---|
Tests 0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
| Test | Named as | Source |
|---|
Source 5
| Field | Value | Source |
|---|---|---|
| Release | trials-2026-11-12-0.4.0 | ClinicalTrials.gov |
| Release manifest SHA-256 | 0f4a8d8c48aee2a7573ae0fa94ecbeab70c1adbe449bbc95c424bc7d3e97b9d4 | ClinicalTrials.gov |
| Registry capture | 2026-11-12 | ClinicalTrials.gov |
| Results copy, AACT | 2026-03-23 | ClinicalTrials.gov |
| NIH clinical studies file | 2026-11-13 | ClinicalTrials.gov |
Release trials-2026-11-12-0.4.0
ClinicalTrials.gov's posted results, through CTTI's AACT: ClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299 ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
ClinicalTrials.gov: none stated ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
NIH RePORTER's clinical studies and project files: Public domain (NIH RePORTER)