ENTERPRISE AI ANALYSIS
Comparison between automated and manual digital diagnostic setups of orthodontic extraction cases: an in silico study
This in silico study evaluated automated digital diagnostic setups in bimaxillary dentoalveolar protrusion extraction cases using two software packages (dentOne® and Ortho Simulation) and compared them to a manual digital setup. While automated setups were significantly faster, they constricted the dental arch and simulated anchorage loss due to poor extraction space management, highlighting the critical need for manual refinement for predictable clinical outcomes. Manual setups maintained original arch forms and achieved desired anterior teeth retraction.
Key Metrics & Immediate Impact
AI-powered diagnostic setups offer significant time savings, but current automated solutions fall short in complex cases like bimaxillary protrusion, leading to suboptimal outcomes. Enterprise adoption requires robust validation against manual precision to ensure clinical predictability and patient satisfaction.
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Faster Setup Time
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Difference in Arch Width (Manual vs. Automated)
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Mean Mesial Molar Translation (Automated)
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Clinical Significance of Manual Adjustment
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Automated Speed vs. Manual Precision
Automated digital setups dramatically reduce the time required for diagnostic setup, making them an attractive option for high-volume practices. However, this speed often comes at the cost of precision, particularly in complex extraction cases where detailed tooth movement and arch form control are critical. Manual refinement remains essential for achieving clinically acceptable results.
80%
Faster Setup Time with Automated AI
Arch Form & Stability
Maintaining original arch form, especially inter-canine and inter-molar widths, is crucial for post-treatment stability. Manual setups achieved this by focusing on anterior retraction. Automated setups, however, demonstrated a significant tendency to constrict the dental arch, potentially leading to instability and undesirable aesthetic outcomes.
| Feature | Manual Setup | Automated Setup |
|---|---|---|
| Inter-canine Width (ICW) |
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| Inter-molar Width (IMW) |
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| Arch Length (AL) |
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Automated Digital Setup Process
The automated process streamlines several steps, particularly segmentation and initial alignment. However, the lack of root information, individual patient cephalometric/soft tissue data, and explicit anchorage requirements within current automated algorithms leads to compromise in precise tooth movement. This highlights a gap where AI needs further development to integrate comprehensive diagnostic data for optimal outcomes.
Enterprise Process Flow
Intraoral Scans Imported (STL)
→
Automatic Tooth Segmentation
→
Define Extraction Teeth
→
Auto-Align/Space Closure
→
Final Automated Setup
Anchorage Management in Extraction Cases
In bimaxillary dentoalveolar protrusion cases requiring extraction of four first premolars, absolute anchorage is often paramount to achieve maximal anterior teeth retraction and facial profile improvement. The study revealed stark differences in how manual and automated setups managed this critical aspect.
Key Finding
Manual setups successfully achieved absolute anchorage by prioritizing anterior retraction and preventing posterior mesialization. In contrast, automated setups consistently resulted in clinically significant mesial translation of posterior teeth (up to 8.69mm), indicating anchorage loss and compromised treatment goals.
Implication for Enterprise
This finding directly impacts the predictability of automated solutions for complex extraction cases. Without explicit anchorage control mechanisms, automated systems may not be suitable for planning treatments where precise posterior segment control is required. Manual oversight is indispensable to prevent unintended tooth movements.
Calculate Your Potential AI ROI
Estimate the time and cost savings your organization could achieve by strategically integrating AI, based on your industry and operational specifics. The calculations reflect the general efficiency gains seen in similar AI implementations, adjusted for industry-specific nuances.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating AI ensures successful adoption and maximized returns. This roadmap outlines key phases for leveraging AI in your enterprise.
Phase 1: Pilot Integration & Manual Validation (3-6 Months)
Implement automated setup tools for non-extraction cases. Establish rigorous manual validation protocols to compare AI-generated setups against orthodontist-planned outcomes for accuracy and clinical relevance.
Phase 2: Advanced Data Integration (6-12 Months)
Develop or integrate modules for AI systems to incorporate root information from CBCT and patient-specific cephalometric data. This is crucial for improving the accuracy of tooth inclination and anchorage predictions.
Phase 3: Custom Algorithm Development for Complex Cases (12-18 Months)
Work with AI developers to create custom algorithms that allow orthodontists to define specific anchorage requirements (e.g., maximum anterior retraction) for extraction cases, preventing unintended posterior mesialization.
Phase 4: Comprehensive AI-Assisted Treatment Planning (18-24 Months)
Roll out AI-assisted planning for a broader range of cases, ensuring that automated setups can be quickly refined manually when necessary. Focus on a hybrid approach where AI accelerates initial planning and orthodontists provide the critical clinical oversight.
Phase 5: Continuous Learning & Outcome Tracking (Ongoing)
Implement systems for continuous feedback where AI models learn from manually refined setups and actual treatment outcomes. This iterative improvement will enhance AI predictability over time.
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