ArticleMedicina (Kaunas, Lithuania)2023
Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks.
Article in Medicina (Kaunas, Lithuania), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed, 24 citations in OpenAlex.
- Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.Journal of imaging informatics in medicine · 2026Article
- Detection of Jaw Lesions on Panoramic Radiographs Using Deep Learning Method.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Evaluation of the effectiveness of artificial intelligence models in radiopaque and radiolucent lesions of the maxillofacial region on panoramic radiographs.Oral radiology · 2026Article
- A deep learning approach to predict the results of root coverage procedures based on intraoral photographs: a retrospective study.BMC oral health · 2025Article
- Deep learning for imaging diagnosis of jaw cystic lesions and maxillofacial tumors: A narrative review.The Journal of international medical research · 2025Review
- Radiographic features of radiolucent odontogenic lesions associated with impacted teeth: a cross-sectional study of 454 cases.Oral and maxillofacial surgery · 2025Article
- A deep learning approach based on YOLO v11 for automatic detection of jaw cysts.BMC oral health · 2025Article
- Artificial Intelligence Methods in the Detection of Oral Diseases on Pantomographic Images-A Systematic Narrative Review.Journal of clinical medicine · 2025Review
- Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Detection of Fractured Endodontic Instruments in Periapical Radiographs: A Comparative Study of YOLOv8 and Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach.Journal of imaging informatics in medicine · 2025Article
- Automated recognition and segmentation of lung cancer cytological images based on deep learning.PloS one · 2025Article
- LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography.Journal of translational medicine · 2024Article
- Utilizing Deep Learning for Diagnosing Radicular Cysts.Diagnostics (Basel, Switzerland) · 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
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.