ArticlePloS one2020
Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Development and Validation of a Picture Archiving and Communication System-Integrated Artificial Intelligence for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.PloS one · 2026Article
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.Scientific reports · 2023Article
- Research and application progress in deep learning in otology.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- Article
- External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.Radiology. Artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to compare the diagnostic performance of deep learning algorithm trained by single view (anterior-posterior (AP) or lateral view) with that trained by multiple views (both views together) in diagnosis of mastoiditis on mastoid series and compare the diagnostic performance between the algorithm and radiologists.
methodsTotal 9,988 mastoid series (AP and lateral views) were classified as normal or abnormal (mastoiditis) based on radiographic findings. Among them 792 image sets with temporal bone CT were classified as the gold standard test set and remaining sets were randomly divided into training (n = 8,276) and validation (n = 920) sets by 9:1 for developing a deep learning algorithm. Temporal (n = 294) and geographic (n = 308) external test sets were also collected. Diagnostic performance of deep learning algorithm trained by single view was compared with that trained by multiple views. Diagnostic performance of the algorithm and two radiologists was assessed. Inter-observer agreement between the algorithm and radiologists and between two radiologists was calculated.
resultsArea under the receiver operating characteristic curves of algorithm using multiple views (0.971, 0.978, and 0.965 for gold standard, temporal, and geographic external test sets, respectively) showed higher values than those using single view (0.964/0.953, 0.952/0.961, and 0.961/0.942 for AP view/lateral view of gold standard, temporal external, and geographic external test sets, respectively) in all test sets. The algorithm showed statistically significant higher specificity compared with radiologists (p = 0.018 and 0.012). There was substantial agreement between the algorithm and two radiologists and between two radiologists (κ = 0.79, 0.8, and 0.76).
conclusionThe deep learning algorithm trained by multiple views showed better performance than that trained by single view. The diagnostic performance of the algorithm for detecting mastoiditis on mastoid series was similar to or higher than that of radiologists.
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