ArticlePloS one2022
Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Explainable hybrid CNN-transformer with self-supervised learning for structural analysis of paranasal sinus CT.Frontiers in computational neuroscience · 2026Article
- Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.PloS one · 2026Article
- Convolutional neural network for maxillary sinus segmentation based on the U-Net architecture at different planes in the Chinese population: a semantic segmentation study.BMC oral health · 2025Article
- A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients.Animals : an open access journal from MDPI · 2025Article
- Diagnostic accuracy of artificial intelligence in the detection of maxillary sinus pathology using computed tomography: A concise systematic review.Imaging science in dentistry · 2025Article
- Detection of concha bullosa using deep learning models in cone-beam computed tomography images: a feasibility study.Archives of craniofacial surgery · 2025Article
- The relationships of personality traits on perceptions and attitudes of dentistry students towards AI.BMC medical education · 2025Article
- The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Preclinical and Preliminary Evaluation of Perceived Image Quality of AI-Processed Low-Dose CBCT Analysis of a Single Tooth.Bioengineering (Basel, Switzerland) · 2024Article
- A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to develop artificial intelligence (AI) system to automatically classify patients with maxillary sinus fungal ball (MFB), chronic rhinosinusitis (CRS), and healthy controls (HCs).
methodsWe collected 512 coronal image sets from ostiomeatal unit computed tomography (OMU CT) performed on subjects who visited a single tertiary hospital. These data included 254 MFB, 128 CRS, and 130 HC subjects and were used for training the proposed AI system. The AI system takes these 1024 sets of half CT images as input and classifies these as MFB, CRS, or HC. To optimize the classification performance, we adopted a 3-D convolutional neural network of ResNet 18. We also collected 64 coronal OMU CT image sets for external validation, including 26 MFB, 18 CRS, and 20 HCs from subjects from another referral hospital. Finally, the performance of the developed AI system was compared with that of the otolaryngology resident physicians.
resultsClassification performance was evaluated using internal 5-fold cross-validation (818 training and 206 internal validation data) and external validation (128 data). The area under the receiver operating characteristic over the internal 5-fold cross-validation and the external validation was 0.96 ±0.006 and 0.97 ±0.006, respectively. The accuracy of the internal 5-fold cross-validation and the external validation was 87.5 ±2.3% and 88.4 ±3.1%, respectively. As a result of performing a classification test on external validation data from six otolaryngology resident physicians, the accuracy was obtained as 84.6 ±11.3%.
conclusionsThis AI system is the first study to classify MFB, CRS, and HC using deep neural networks to the best of our knowledge. The proposed system is fully automatic but performs similarly to or better than otolaryngology resident physicians. Therefore, we believe that in regions where otolaryngology specialists are scarce, the proposed AI will perform sufficiently effective diagnosis on behalf of doctors.
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