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Computer-Aided Diagnosis of Maxillary Sinus Anomalies: Validation and Clinical Correlation
Debayan Bhattacharya1,2, Benjamin Tobias Becker2, Finn Behrendt1
1Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany.
The Laryngoscope
|March 23, 2024
Summary
A new computer-aided diagnostics (CAD) system effectively differentiates maxillary sinus opacifications using 3D CNNs. This automated approach aids in discovering clinical correlations within large population studies.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Population Health Studies
Background:
- Maxillary sinus (MS) opacification diagnosis is labor-intensive.
- Automated differentiation can streamline clinical insight discovery in large cohorts.
Purpose of the Study:
- To develop and evaluate a computer-aided diagnostics (CAD) system for automated maxillary sinus opacification detection.
- To explore correlations between MS opacifications and various clinical factors in a population-based study.
Main Methods:
- Utilized the Hamburg City Health Study (HCHS) cohort.
- Developed a CAD system employing an ensemble of 3D Convolutional Neural Networks (CNNs) to analyze cranial MRIs.
- Correlated MS opacification status with clinical data including lifestyle, medical history, and biomarkers.
Main Results:
- The CAD system achieved high performance (AUROC: 0.95, sensitivity: 0.85, specificity: 0.90).
- MS opacification was associated with higher alcohol consumption, BMI, and asthma incidence (intrinsic and extrinsic).
- Male sex, hay fever, and house dust allergy were linked to higher prevalence, while bee/wasp venom allergy was less common.
Conclusions:
- A 3D CNN-based CAD system effectively distinguishes MS with and without opacifications.
- The automated system enhances diagnostic capabilities and facilitates clinical data correlation in population studies.
