A mini-review on the application of AI technology in the segmentation of maxillary sinus for dentistry - Scorecard - DentalSpire

A mini-review on the application of AI technology in the segmentation of maxillary sinus for dentistry

  • By

  • Jiayi Chen

  • May 20, 2026

  • 0 min

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Clinical Scorecard: A Brief Review of AI Technology's Role in Maxillary Sinus Segmentation for Dental Applications

At a Glance

CategoryDetail
ConditionMaxillary sinus segmentation
Key MechanismsDeep Learning (DL) for image analysis and segmentation
Target PopulationDental and ENT practitioners, junior physicians, graduates, and interns
Care SettingDental clinics and radiology departments

Key Highlights

  • DL enhances accuracy in identifying maxillary sinus boundaries on CT/CBCT images.
  • Automated segmentation can save time and reduce bias in medical imaging.
  • Integration of segmentation algorithms into radiology software is expected to improve medical education.

Guideline-Based Recommendations

Diagnosis

  • Utilize DL models for accurate identification of maxillary sinus anatomy on imaging.

Management

  • Incorporate automated segmentation in clinical practice to assist in procedures like sinus floor elevation.

Monitoring & Follow-up

  • Apply advanced algorithms to monitor changes in maxillary sinus floor elevation post-surgery.

Risks

  • Be aware of anatomical variations that may affect segmentation accuracy.

Patient & Prescribing Data

Patients requiring dental implants or treatment for maxillary sinus inflammation.

DL models can assist in evaluating the suitability of maxillary sinus for surgical interventions.

Clinical Best Practices

  • Ensure adequate training sample sizes for DL models to avoid overfitting.
  • Educate junior practitioners on the use of automated segmentation tools.

Related Resources & Content

Original Source(s)

A mini-review on the application of AI technology in the segmentation of maxillary sinus for dentistry

  • by Jiayi Chen

  • May 20, 2026

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