Orthodontics has traditionally relied on a combination of clinical expertise, diagnostic tools, and biomechanical principles to plan and execute tooth movement. Treatment planning requires careful evaluation of dental and skeletal relationships, patient compliance, and force mechanics. However, outcomes often vary based on practitioner skill, case complexity, and biological response. As orthodontics moves toward greater precision and predictability, artificial intelligence (AI) has emerged as a transformative tool in treatment planning and outcome forecasting1.

AI refers to the simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction2. In healthcare, AI applications have shown remarkable capabilities in areas like radiographic interpretation, surgical planning, and disease prediction3. In orthodontics, machine learning (ML) and deep learning (DL) models trained on large datasets can detect patterns and relationships that are not easily apparent to human observers4. These models can assist clinicians in diagnosing malocclusions, segmenting craniofacial structures, simulating treatment outcomes, and predicting the trajectory of tooth movement under specific forces5.

Tooth movement during orthodontic treatment is governed by complex biomechanical and biological factors. Traditional planning involves estimations of force vectors, anchorage control, and space management, which can be inconsistent due to biological variability6. AI models can analyze 3D dental scans, cephalometric radiographs, and intraoral photographs to simulate how teeth are likely to move over time. By using convolutional neural networks (CNNs), AI systems can perform volumetric image analysis to create detailed treatment simulations with improved accuracy7.

Recent studies have demonstrated the efficacy of AI in orthodontic prediction tasks. A study by Lee et al. (2021) trained a DL model on 3D CBCT data and achieved a 92% accuracy rate in predicting molar and incisor displacement8. Similarly, Zhang et al. (2022) employed AI to simulate extraction and non-extraction scenarios and reported significant alignment between predicted and actual post-treatment outcomes9. These advances hold the potential to reduce treatment duration, enhance alignment accuracy, and minimize complications such as root resorption and anchorage loss10.

Despite its promise, the integration of AI into clinical orthodontics is still in its infancy. Concerns regarding data privacy, algorithm transparency, model generalizability, and the need for clinical validation persist11. Moreover, practitioners must be trained to interpret AI-generated recommendations critically and understand their limitations. Nevertheless, the future of orthodontic treatment appears increasingly data-driven, with AI enabling more personalized, efficient, and outcome-focused care12.

The present study aims to assess the effectiveness of AI-assisted treatment planning in predicting tooth movement compared to traditional planning methods. By evaluating metrics such as alignment accuracy, anchorage loss, and treatment duration, this research seeks to validate the clinical utility of AI in enhancing orthodontic outcomes.

A retrospective cohort study was conducted using de-identified digital records of 850 patients treated with clear aligners (Invisalign) between January 2025 and May 2025. Data was sourced from a university orthodontic clinic’s digital archive. Inclusion criteria targeted Class I malocclusion patients (ANB 0–4°, normal overjet/overbite) to minimize skeletal confounders. Exclusion criteria rigorously eliminated:

• Craniofacial syndromes (e.g., Crouzon, Apert syndrome)
• Missing posterior teeth (excluding third molars)
• History of orthognathic surgery or TMJ disorders
• Incomplete treatment records or poor-quality scans.
  Rationale:This ensured homogeneous biomechanical conditions for AI training.

Data Acquisition and Preprocessing

• Imaging:Pre- and post-treatment intraoral scans (iTero Element 5D, Align Technology) were exported as high-resolution .STL files (mean point density: 50 μm).
• Alignment:Scans were co-registered using an iterative closest point (ICP) algorithm with 1,000 iterations (Python open3d library), achieving <0.1 mm mean surface error.
• Segmentation:Teeth were isolated using a U-Net architecture (Tensor Flow) pretrained on 5,000 annotated scans. Each tooth was assigned a 3D coordinate system with the centroid as the origin.
• Ground Truth Displacements:6-DOF vectors (translation: x,y,z; rotation: roll, pitch, yaw) were computed via rigid-body transformation between pre- and post-treatment positions (MATLAB Robotics Toolbox).

AI Model Architecture and Training

• Network Design:A 3D convolutional neural network (CNN) with 12 layers processed volumetric inputs (128x128x128 voxels):
  • Encoder:Four 3D convolution blocks (kernel: 3×3×3; ReLU activation; max-pooling).
  • Bottleneck:Two dense layers (512 units) with dropout (0.3).
  • Decoder:Four transposed convolution blocks generating per-tooth displacement vectors.
• Training Protocol:
  • Optimizer:Adam (learning rate: 5×10⁻⁴).
  • Loss Function:Mean squared error (MSE) on displacement vectors.
  • Augmentation:Random rotations (±10°), translations (±2 mm), and Gaussian noise (σ=0.05 mm).
  • Validation:5-fold cross-validation; 80% training (n=680), 20% testing (n=170).
• Hardware:NVIDIA A100 GPUs (40GB VRAM); training time: 48 hours.

Statistical Analysis

Mean absolute error (MAE), root mean square error (RMSE). ANOVA with Tukey’s HSD compared errors across tooth types. Pearson’s *r* assessed relationships between movement magnitude and prediction error. Ten board-certified orthodontists predicted displacements for 50 randomly selected cases; results compared to AI via paired *t*-tests. Python 3.9 (PyTorch, SciPy), SPSS v28.

Table 1: Patient Demographics

In Table 1, 850 patients were included in the study, providing a robust sample for statistical reliability. The mean patient age was 24.3 years (±8.2), indicating a young adult population, likely typical of orthodontic treatment cohorts. 62% female and 38% male, suggesting a higher uptake of orthodontic treatment among females. Average duration was 14.2 months, aligning with standard orthodontic treatment timelines.

Table 2: Prediction Accuracy by Tooth Type

In Table 2, AI prediction was most accurate for incisors (rotation MAE: 0.87°, translation MAE: 0.38 mm) and least accurate for molars (rotation MAE: 1.34°, translation MAE: 0.43 mm). Prediction accuracy was highest for incisors (92.3%), decreasing progressively for canines (89.7%), premolars (90.5%), and lowest for molars (88.1%).

Table 3: Error vs. Movement Magnitude

In Table 3: As the magnitude of movement increases, both MAE and RMSE also increase.

• For movements <2 mm: MAE = 0.31 mm
• For 2–4 mm: MAE = 0.49 mm
• For >4 mm: MAE = 0.87 mm

Table 4: AI vs. Orthodontist Performance

In Table 4, Rotation MAE: AI = 1.10°, Orthodontists = 1.85°. Translation MAE: AI = 0.40 mm, Orthodontists = 0.68 mm. Both are <0.001, indicating statistically significant differences.

Table 5: Impact of Root Length on Error

In Table 5, MAE increases with root length:

• Short: 0.37 mm
• Medium: 0.41 mm
• Long: 0.62 mm

Table 6: Algorithm Runtime

In Table 6: Algorithm Runtime

• Segmentation:2 seconds
• Prediction:6 seconds

Artificial intelligence (AI) in orthodontics has gained considerable traction due to its ability to improve precision, reduce chair-side time, and enhance treatment outcomes. The present study assessed the performance of an AI model in predicting tooth movement by evaluating mean absolute errors (MAE) in both rotational and translational axes, analyzing influencing variables like tooth type, movement magnitude, and root length, and comparing AI performance with human orthodontists. The findings underscore AI’s potential as a reliable tool in orthodontic treatment planning and execution.

The demographic profile of the study population (mean age 24.3 ± 8.2 years) aligns with prior orthodontic epidemiology data suggesting that young adults constitute the majority of treatment seekers. Female predominance (62%) also mirrors earlier studies that observed higher esthetic treatment demand among women12.

In terms of tooth-specific accuracy, AI performed best on incisors (rotation MAE: 0.87°, translation MAE: 0.38 mm, accuracy: 92.3%) and least accurately on molars (rotation MAE: 1.34°, accuracy: 88.1%). Similar findings were reported by Park et al. 13, who noted that anterior teeth allow better imaging resolution and present simpler biomechanics, making their movements easier to predict. Posterior teeth, particularly molars, have more complex root morphology and engage in multifactorial occlusal functions, which can introduce variability14.

The analysis of movement magnitude revealed that error metrics (MAE and RMSE) increased with larger displacements. This trend supports findings by Xie et al. 15, who showed that prediction errors in AI-based tooth tracking algorithms correlate with movement complexity and extent. Movements >4 mm demonstrated the highest MAE (0.87 mm), emphasizing the need for more refined models when predicting substantial orthodontic shifts.

A major highlight of the study was the superior performance of AI compared to human orthodontists. The AI exhibited statistically significantly lower MAEs in both rotation (1.10° vs. 1.85°) and translation (0.40 mm vs. 0.68 mm), with p-values <0.001. These results are consistent with Liu et al. 16, who demonstrated that deep learning models could outperform clinicians in cephalometric landmark identification and tooth alignment accuracy. AI's consistency stems from its ability to avoid subjective variability inherent to manual assessments.

Root length was another influential factor, with longer roots associated with higher MAEs. This observation parallels a study by Kim et al. 17, who noted that longer roots generate greater anchorage and resistance to movement, complicating prediction. This suggests that integrating root morphology into AI modeling could further enhance predictive fidelity. From a computational perspective, the AI model demonstrated real-time feasibility, with an average runtime of only 17.8 seconds for segmentation and prediction combined. This rapid processing has been echoed in works like those of Hwang et al. 18, promoting AI adoption in chairside applications and aligner therapy planning.

AI-driven tooth movement prediction enhances orthodontic precision, reducing human error and treatment duration. While effective for straightforward displacements, complex movements necessitate biological inputs. Integration with clinical workflows promises personalized, efficient care. The current findings affirm the clinical utility of AI in orthodontic prediction, especially in reducing human error, streamlining treatment, and enhancing precision. Future studies should focus on integrating patient-specific variables such as bone density, periodontal status, and three-dimensional root orientation to further optimize prediction accuracy.

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