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Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny + DeiT Gated MLP-Based Hybrid Deep

Furkan Talo1, Nurullah Duger2, Emre Aslan2

  • 1Department of Computer Engineering, Firat University, Elazig 23119, Türkiye.

Diagnostics (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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A new hybrid deep learning model accurately detects maxillary sinus ostium patency, crucial for dental implant surgery. This AI tool aids specialists, reducing errors and surgical risks in posterior maxilla procedures.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • Maxillary sinus ostium patency is vital for successful dental implant and sinus lift surgeries.
  • Obstruction increases risks of sinusitis and graft failure, necessitating careful preoperative evaluation.
  • Manual radiographic assessment is time-consuming and prone to errors due to experience gaps.

Purpose of the Study:

  • To develop an automated method for reliable maxillary sinus ostium evaluation.
  • To improve the accuracy and efficiency of preoperative radiographic assessments.
  • To reduce surgical risks associated with maxillary sinus procedures.

Main Methods:

  • A hybrid deep learning model combining CNNs and transformers was proposed.
  • Gated fusion technique was employed to enhance feature integration and classification performance.
Keywords:
CNNViTartificial intelligencedeep learningostium

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  • The model's performance was benchmarked against existing ViT and CNN architectures.

    • Main Results:

    • The hybrid model achieved a test accuracy of 95.03%.
    • This surpasses the highest accuracy of 89.36% from other pre-trained models.
    • The results demonstrate strong clinical diagnostic capabilities.

    Conclusions:

    • The proposed model effectively determines maxillary sinus ostium patency.
    • It offers a reliable tool to assist specialists in preoperative planning.
    • The AI approach can reduce workload and minimize diagnostic errors.