TMJ X-Sectional Findings
š¹ Description:
This API analyzes transverse sectional (X-sectional) images of the Temporomandibular Joint (TMJ) to detect and classify signs of TMJ disorders. It uses a deep learning model to recognize key pathological changes and returns the predicted diagnostic class associated with the input image.
š¦· What is the purpose of this API?
ā¢ To screen and classify TMJ conditions using X-sectional images.
ā¢ Helps identify specific structural abnormalities such as:
ā¢ Condylar flattening
ā¢ Subcortical cyst
ā¢ Surface erosion
ā¢ Or confirm a normal TMJ
ā¢ Supports radiologists and dentists in early detection of TMJ disorders.
š When to use it?
Use this API when:
ā¢ You have TMJ sectional images (CT or CBCT slices) and need automated classification.
ā¢ You’re developing or using a diagnostic tool for TMJ health analysis.
ā¢ You want to support clinical decisions with AI-based assessments for TMJ symptoms.
š¹ Notes:
- Supported class labels:
ā¢ Normal
ā¢ Condylar flatenning
ā¢ Subcortical cyst
ā¢ Surface erosion - Designed specifically for TMJ cross-sectional radiographs (not panoramic or ceph images).
- Ensure images are clear and focused on the condylar region for accurate prediction.
- Can be integrated into TMJ diagnostic pipelines or radiology review systems.
Image Input for TMJ X-Sectional