ArticleOdontology2021
A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs.
Article in Odontology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Determination of Fungiform Papilla Number Using Deep Learning Methods.Journal of imaging informatics in medicine · 2026Article
- Automated detection and classification of maxillary sinus variations using slice-based and full-volume CBCT deep learning models.BMC oral health · 2026Article
- Maxillary sinus classification for sex and age using 23 artificial intelligence architectures.Scientific reports · 2026Article
- Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.BMC oral health · 2024Article
- Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.Imaging science in dentistry · 2024Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
- Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography.Journal of dental sciences · 2023Article
- The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Review
- A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023Article
- Transfer learning in diagnosis of maxillary sinusitis using panoramic radiography and conventional radiography.Oral radiology · 2023Article
- Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.Scientific reports · 2023Article
- Deep learning for preliminary profiling of panoramic images.Oral radiology · 2023Article
- Deep convolution neural network for screening carotid calcification in dental panoramic radiographs.PLOS digital health · 2023Article
- Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs.Imaging science in dentistry · 2023Article
- Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.Scientific reports · 2023Article
- Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography.Scientific reports · 2022Article
- Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma.Annals of translational medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To investigate the use of transfer learning when applying a deep learning source model from one institution (institution A) to another institution (institution B) for creating effective models (target models) for the detection of maxillary sinuses and diagnosis of maxillary sinusitis on panoramic radiographs. In addition, to determine appropriate numbers of training data for the transfer learning. Source model was created using 350 panoramic radiographs from institution A as training data. Transfer learning was performed by adding 25, 50, 100, 150, or 225 panoramic radiographs as training data from institution B to the source model; this yielded the target models T25, T50, T100, T150 and T225. Each model was then evaluated using test data that comprised 40 images from institution A, 30 images from institution B. The performance indices (recall, precision and F1 score) for detecting the maxillary sinuses by the source model exceeded 0.98 when using test data A from institution A, but they deteriorated when using test data B from institution B. In the evaluation of target models using test data B, model T25 showed improved detection performance (recall of 0.967). The diagnostic performance of model T50 for maxillary sinusitis exceeded 0.9 in sensitivity. Transfer learning, which involves applying a small amount of data to the source model, yielded high performances in detecting the maxillary sinuses and diagnosing the maxillary sinusitis on panoramic radiographs. This study serves as a reference when adapting source models to other institutions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.