ArticleSensors (Basel, Switzerland)2022
Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Applications and clinical translation of artificial intelligence in CBCT-based detection of endodontic lesions: a scoping review.Oral radiology · 2026Article
- Diagnostic Accuracy of Clinical Decision Support Systems ORADIII and ORAD DDx to Histopathological Diagnosis of Jaw Lesions.Mayo Clinic proceedings. Digital health · 2026Article
- Impacts of artificial intelligence on computed tomography in endodontics: an integrative review.BMC oral health · 2026Review
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 2026Article
- Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- The Leeuwenhoek paradox in dental artificial intelligence: A narrative review of artificial intelligence-detected subclinical periapical changes and human-labeled false positives.Journal of dental sciences · 2026Review
- Evaluation of a no-code AI model for detecting periapical radiolucencies: impact of anatomical region on diagnostic performance.BMC oral health · 2025Article
- Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification.Plant methods · 2025Article
- Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.Scientific reports · 2025Article
- Role of Artificial Intelligence and Machine Learning in Conservative Dentistry and Endodontics: A Review.Cureus · 2025Review
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
- Deep learning algorithms for detecting fractured instruments in root canals.BMC oral health · 2025Article
- A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.Journal of imaging informatics in medicine · 2025Article
- Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study.BMC oral health · 2024Article
- Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs.BDJ open · 2024Article
- Artificial Intelligence in Endodontics: A Scoping Review.Iranian endodontic journal · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Imaging examinations are of remarkable importance for diagnostic support in Dentistry. Imaging techniques allow analysis of dental and maxillofacial tissues (e.g., bone, dentine, and enamel) that are inaccessible through clinical examination, which aids in the diagnosis of diseases as well as treatment planning. The analysis of imaging exams is not trivial; so, it is usually performed by oral and maxillofacial radiologists. The increasing demand for imaging examinations motivates the development of an automatic classification system for diagnostic support, as proposed in this paper, in which we aim to classify teeth as healthy or with endodontic lesion. The classification system was developed based on a Siamese Network combined with the use of convolutional neural networks with transfer learning for VGG-16 and DenseNet-121 networks. For this purpose, a database with 1000 sagittal and coronal sections of cone-beam CT scans was used. The results in terms of accuracy, recall, precision, specificity, and F1-score show that the proposed system has a satisfactory classification performance. The innovative automatic classification system led to an accuracy of about 70%. The work is pioneer since, to the authors knowledge, no other previous work has used a Siamese Network for the purpose of classifying teeth as healthy or with endodontic lesion, based on cone-beam computed tomography images.
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.