AI in Dentistry

Dental Caries Detection

Lesion Depth

import torch.nn as nn

class CariesDetector(nn.Module):
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(),
            nn.MaxPool2d(2), nn.Conv2d(32, 64, 3, padding=1),
            nn.BatchNorm2d(64), nn.ReLU(), nn.AdaptiveAvgPool2d(1))
        self.classifier = nn.Linear(64, 4)

    def forward(self, x):
        return self.classifier(self.features(x).flatten(1))

Orthodontic Planning

Cephalometric Analysis

Treatment Simulation

class OrthodonticPlanner:
    def plan_treatment(self, ceph_image):
        landmarks = self.landmark_detector.predict(ceph_image)
        measurements = self._compute_cephalometric(landmarks)
        plan = self._generate_plan(measurements)
        return {'landmarks': landmarks, 'measurements': measurements, 'plan': plan}

CBCT Analysis

Evaluation

• Dice score for segmentation tasks
• Landmark detection accuracy: Mean radial error
• Caries detection AUROC: vs expert dentists