Quantifying Gains in Treatment Efficiency and Predictability by OrthoSelect

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Categories: Orthodontics; Practice Management, HR and Accounting;
Quantifying Gains in Treatment Efficiency and Predictability

A clinical comparison of DIBS AI digital bonding versus traditional direct bonding

by OrthoSelect

Abstract
Objective
To quantify the clinical and operational efficiency gains associated with OrthoSelect’s DIBS AI digital bonding platform compared to traditional direct bonding by evaluating treatment duration, appointment frequency, mid-course adjustments, and overall treatment predictability.

Methodology
A retrospective analysis was conducted on 1,308 completed orthodontic cases across 16 practices in 12 U.S. states. Treatment outcomes for direct bonding and DIBS AI digital bonding were compared using independent t-tests, F-tests, and effect size (Cohen’s d) analysis.

Results
DIBS AI digital bonding demonstrated substantial efficiency gains, including a 21.1% reduction in overall treatment time (19.4 vs 24.6 months), a 23.8% reduction in office visits (14.4 vs 18.9 visits), a 46.0% reduction in wire bends, and a 40.1% reduction in bracket repositions (all p<0.001). In addition to lower mean values, DIBS AI cases exhibited significantly reduced variability across all measured outcomes.

Conclusion
Compared to traditional direct bonding, DIBS AI digital bonding delivers fewer visits, fewer mid-course adjustments, and shorter treatment durations. These gains translate into clinically meaningful improvements in efficiency, predictability, and workflow consistency, enhancing operational value for both orthodontic practices and patients.

Introduction
Previous investigations comparing digital bonding to traditional direct bonding have suggested reductions in treatment duration for the digital approach. However, questions remain regarding whether these time savings consistently extend across diverse practice settings and whether they translate into broader efficiency gains—such as fewer appointments, fewer mid-course adjustments, and more predictable treatment progression.

From a clinical and operational standpoint, efficiency in orthodontic treatment is multidimensional. Reduced treatment time alone does not fully capture the burden on practices or patients if achieved through increased visit frequency or greater chairside adjustments. Accordingly, this study evaluates whether DIBS AI digital bonding delivers comprehensive efficiency improvements that impact clinical workflow, treatment consistency, and patient experience across a wide range of real-world practices.

Methodology
For this study, patients treated at 16 unrelated orthodontic practices in 12 different states (Colorado, Connecticut, Florida, Indiana, Michigan, Missouri, Nevada, Texas, Utah, Virginia, Washington, and Wisconsin) were selected post hoc. As such, practitioners had no prior knowledge of the research study that would affect case selection or treatment approach. The information was anonymized and then segmented into two groups: those who received bracket placement via direct bonding and those who had brackets placed via DIBS AI digital bonding. A total of 1,308 completed cases were selected for this study (306 direct bonding cases and 1,002 digital bonding cases). All reported cases treated both maxillary and mandibular arches. Patients constituted a mix of genders and ages representing both adolescents and adults, with the majority of patients being in their teenage years. Orthodontists followed the prescribed protocols for DIBS AI software treatment planning and clinical procedures as laid out by OrthoSelect. No guidelines were provided for their use of direct bonding techniques. Accordingly, there likely may have been some variation in treatment techniques for the direct bonding cases.

Using data stored in each practice’s patient management system, information was collected and aggregated for both direct and digital bonding cases.

The sample data were analyzed using standard statistical testing methods to determine if meaningful differences exist. In the case of comparing the mean results of both samples, an independent two-sample t-test was used with a p-value threshold < 0.05. An F-test was used for comparing standard deviations between samples, p-value threshold also < 0.05. Cohen’s d was used to quantify the differences between samples.

Results
Based on aggregated data across all participating practices, DIBS AI digital bonding demonstrated a mean treatment duration that was 5.2 months shorter than direct bonding, representing a 21.1% improvement in treatment efficiency (p<0.001).

Importantly, these reductions in treatment duration were accompanied by a 23.8% decrease in the number of treatment appointments, equating to a mean of 4.5 fewer office visits per patient (p<0.001). This finding indicates that efficiency gains were not achieved by compressing visit intervals, but rather through more effective treatment progression.

Mid-course adjustments showed similarly meaningful reductions. DIBS AI cases required 46.1% fewer wire bends (p<0.001) and 40.1% fewer bracket repositions (p<0.001), reflecting improved initial bracket placement accuracy and reduced need for corrective interventions during treatment (Fig. 1).
Quantifying Gains in Treatment Efficiency and Predictability
Fig. 1: Summary of differences between direct bonding and DIBS AI


Statistical significance alone does not always provide an indication of how meaningful the differences are, especially for clinical use. Therefore, Cohen’s d statistical test was also applied to determine the magnitude of the differences between the various outcomes. The results of this test are summarized in Table 1. A Cohen’s d value ≈ 0.5 indicates a medium effect and would be clinically relevant. Values > 0.80 would be considered to have a large effect and exhibit a substantial difference in a clinical environment.
Quantifying Gains in Treatment Efficiency and Predictability
Table 1: Cohen’s d measure of magnitude between DIBS AI and direct bonding


Compared with direct bonding, DIBS AI demonstrated a large reduction in treatment time (d=0.83), a medium-to-large reduction in the number of appointments (d=0.75), medium reduction in bracket repositions (d=0.47), and small-to-medium reduction in wire bends (d=0.41).

Beyond mean reductions, DIBS AI cases demonstrated significantly lower variability across all measured outcomes. Standard deviations were reduced by 28–58%, indicating a narrower distribution of treatment experiences and greater consistency in clinical outcomes (Table 2).
Quantifying Gains in Treatment Efficiency and Predictability
Table 2: Standard deviation of key variables

Conclusion
This multi-practice clinical analysis across multiple geographies demonstrates that DIBS AI delivers substantial and clinically meaningful gains in orthodontic efficiency compared to traditional direct bonding. On average, DIBS AI reduced treatment duration by more than five months, eliminated more than four office visits per case, and significantly decreased the need for mid-course wire adjustments and bracket repositioning.

Crucially, these efficiency gains were achieved without increasing the frequency of appointments, indicating that improvements stem from enhanced treatment precision rather than scheduling manipulation or compression. The consistently lower standard deviations across all outcome measures further indicate that DIBS AI produces more predictable and uniform treatment experiences, which may simplify treatment planning and improve expectation-setting for both clinicians and patients.

From a patient perspective, fewer visits, fewer adjustments, and shorter treatment durations may reduce treatment fatigue, improve compliance and enhance the overall treatment experience. From a practice perspective, these efficiency gains create opportunities to optimize chair utilization, increase capacity, and improve overall practice performance.

While this study did not stratify cases by complexity, the magnitude and consistency of the observed efficiency gains across a large and diverse sample suggest that DIBS AI digital bonding demonstrates clinical and operational advantages. Future research examining outcomes by case difficulty may further refine these findings and expand understanding of digital bonding’s role in modern orthodontic workflows. 

This content is sponsored by OrthoSelect. For more information, visit dibsai.com.

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