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BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep

Pinky Agarwal1, Anju Yadav1, Pratistha Mathur1

  • 1SCIT, Manipal University Jaipur, India.

Computational Intelligence and Neuroscience
|February 10, 2022
PubMed
Summary

A new deep learning model, BID-Net, accurately detects bone invasion in oral squamous cell carcinoma (OSCC) patients using CT scans. This AI tool aids in treatment planning by classifying images with or without tumor bone invasion.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate detection of bone invasion in oral squamous cell carcinoma (OSCC) is crucial for effective treatment planning and surgical intervention.
  • Computed Tomography (CT) scans are the standard imaging modality for assessing bone invasion due to their high sensitivity and specificity.

Purpose of the Study:

  • To develop and evaluate an automated deep learning model for detecting bone invasion in OSCC patients.
  • To compare the performance of the proposed model against established transfer learning models.

Main Methods:

  • A novel deep learning model, BID-Net, was developed for the binary classification of CT scan images.
  • BID-Net was trained and tested on CT images to differentiate between the presence and absence of bone invasion.
  • The model's performance was benchmarked against VGG16, VGG19, ResNet-50, MobileNetV2, DenseNet-121, and ResNet-101.

Main Results:

  • The proposed BID-Net model achieved a high accuracy of 93.62% in detecting bone invasion.
  • BID-Net demonstrated superior performance compared to the six evaluated transfer learning models.
  • Results were validated by expert radiologists, confirming the model's clinical relevance.

Conclusions:

  • Deep learning, specifically BID-Net, offers a promising automated solution for bone invasion detection in OSCC.
  • The model's high accuracy and superior performance suggest its potential to enhance diagnostic efficiency and patient care.
  • This study represents a novel application of deep learning in the detection of bone invasion in oral cancer.