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Updated: May 10, 2025

Olfactory Neurons Obtained through Nasal Biopsy Combined with Laser-Capture Microdissection: A Potential Approach to Study Treatment Response in Mental Disorders

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NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.

Nesrine Atitallah¹, Safa Ben Atitallah^2,3, Maha Driss^2,3

¹Faculty of Computer Studies, Arab Open University, Riyadh 11681, Saudi Arabia.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

Artificial Intelligence (AI) enhances sinus disease diagnosis using Self-Supervised Learning (SSL) and Random Forest (RF) algorithms. This AI approach achieved 92.62% accuracy in classifying sinonasal pathology from medical images.

Keywords:

AI pathology detection random forest self-supervised learning sinonasal

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Sinus diseases significantly impact quality of life, causing symptoms like facial pain and reduced smell.
Accurate diagnosis of sinus diseases is challenging due to factors like poor patient adherence to protocols.
Artificial Intelligence (AI) offers a promising avenue to improve diagnostic precision for sinonasal pathology.

Purpose of the Study:

To develop and evaluate a novel AI-based approach for detecting sinonasal pathology.
To leverage Self-Supervised Learning (SSL) and Random Forest (RF) algorithms for improved classification accuracy.
To introduce a new, expert-labeled dataset of CT and MRI images for sinonasal pathology research.

Main Methods:

Utilized a new dataset of 137 CT and MRI images, labeled by expert radiologists into healthy and unhealthy classes.
Employed the Deep InfoMax (DIM) model within a self-supervised framework to extract global and local image features.
Integrated extracted features into a Random Forest (RF) classifier for distinguishing between healthy and pathological sinus cases.

Main Results:

The AI-based approach demonstrated high efficacy in classifying sinonasal pathology.
Achieved a mean classification accuracy of 92.62% in distinguishing healthy from diseased sinus cases.
The combination of Deep InfoMax (DIM) and Random Forest (RF) proved effective for feature learning and classification.

Conclusions:

The proposed AI-driven method shows significant potential for enhancing the accuracy and effectiveness of sinonasal pathology diagnosis.
The developed dataset serves as a valuable resource for future research in AI-based medical image analysis for sinus diseases.
This study highlights the successful application of Self-Supervised Learning (SSL) and Random Forest (RF) in improving diagnostic outcomes for sinus conditions.