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Computer-aided diagnosis of maxillary sinus anomalies : validation and clinical correlation
Citation Link: https://doi.org/10.15480/882.13345
Publikationstyp
Journal Article
Date Issued
2024-09
Sprache
English
Author(s)
TORE-DOI
Journal
Volume
134
Issue
9
Start Page
3927
End Page
3934
Citation
Laryngoscope 134 (9): 3927-3934 (2024)
Publisher DOI
Scopus ID
Publisher
Wiley
Objective: Computer aided diagnostics (CAD) systems can automate the differentiation of maxillary sinus (MS) with and without opacification, simplifying the typically laborious process and aiding in clinical insight discovery within large cohorts. Methods: This study uses Hamburg City Health Study (HCHS) a large, prospective, long-term, population-based cohort study of participants between 45 and 74 years of age. We develop a CAD system using an ensemble of 3D Convolutional Neural Network (CNN) to analyze cranial MRIs, distinguishing MS with opacifications (polyps, cysts, mucosal thickening) from MS without opacifications. The system is used to find correlations of participants with and without MS opacifications with clinical data (smoking, alcohol, BMI, asthma, bronchitis, sex, age, leukocyte count, C-reactive protein, allergies). Results: The evaluation metrics of CAD system (Area Under Receiver Operator Characteristic: 0.95, sensitivity: 0.85, specificity: 0.90) demonstrated the effectiveness of our approach. MS with opacification group exhibited higher alcohol consumption, higher BMI, higher incidence of intrinsic asthma and extrinsic asthma. Male sex had higher prevalence of MS opacifications. Participants with MS opacifications had higher incidence of hay fever and house dust allergy but lower incidence of bee/wasp venom allergy. Conclusion: The study demonstrates a 3D CNN's ability to distinguish MS with and without opacifications, improving automated diagnosis and aiding in correlating clinical data in population studies. Level of Evidence: 3 Laryngoscope, 134:3927–3934, 2024.
Subjects
Convolutional Neural Network
deep learning
maxillary sinus
Paranasal sinus
Population study
MLE@TUHH
DDC Class
610: Medicine, Health
006: Special computer methods
519: Applied Mathematics, Probabilities
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The Laryngoscope - 2024 - Bhattacharya - Computer‐Aided Diagnosis of Maxillary Sinus Anomalies Validation and Clinical.pdf
Type
Main Article
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1.94 MB
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