ArticleSensors (Basel, Switzerland)2021
Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses 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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.BMC oral health · 2023Pooled it
- Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Performance Validation of ORTHOSEG, a Novel Artificial Intelligence Tool for the Segmentation of Orthopantomographs and Intra-Oral X-Rays.Clinics and practice · 2026Article
- AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns.Scientific reports · 2026Article
- Machine learning assisted classification of periodontal health and periodontitis using alveolar bone loss measurements on bitewing radiographs.Frontiers in oral health · 2026Article
- Prognostic Evaluation of Lower Third Molar Eruption Status from Panoramic Radiographs Using Artificial Intelligence-Supported Machine and Deep Learning Models.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning for dentomaxillofacial cone-beam computed tomography image quality enhancement: A pilot study.Imaging science in dentistry · 2025Article
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomography.Dento maxillo facial radiology · 2025Article
- Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study.BMC oral health · 2024Article
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Improving resolution of panoramic radiographs: super-resolution concept.Dento maxillo facial radiology · 2024Article
- Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Periodontology: A Scoping Review.Dentistry journal · 2023Article
- Topology Optimization-Based Localized Bone Microstructure Reconstruction for Image Resolution Enhancement: Accuracy and Efficiency.Bioengineering (Basel, Switzerland) · 2022Article
- Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography.Sensors (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Dentistry-Narrative Review.International journal of environmental research and public health · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion difficult. Recently, the use of deep-learning methods for image resolution improvement has seen an increase in the literature. In this work, we performed two studies to evaluate the effects of using different resolution improvement methods (nearest, bilinear, bicubic, Lanczos, SRCNN, and SRGAN). In the first one, specialized dentists visually analyzed the quality of images treated with these techniques. In the second study, we used those methods as different pre-processing steps for inputs of convolutional neural network (CNN) classifiers (Inception and ResNet) and evaluated whether this process leads to better results. The deep-learning methods lead to a substantial improvement in the visual quality of images but do not necessarily promote better classifier performance.
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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.