ArticlePediatric reports2024
Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study.
Article in Pediatric reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06258798 (Comparison of the Dental X-ray Analysis Performed by an Artificial Intelligence Algorithm and the Analysis Performed by Dentists), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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.
Comparison of the Dental X-ray Analysis Performed by an Artificial Intelligence Algorithm and the Analysis Performed by Dentists
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Automated detection of periapical lesions in pediatric panoramic radiographs using YOLOv7: a retrospective internal validation study.BMC pediatrics · 2026Article
- Artificial intelligence in diagnostic imaging: collaborative asset or looming replacement?Oral radiology · 2026Review
- Teeth identification and numbering in mixed dentition: evaluating deep learning models for pediatric panoramic radiographs.BMC oral health · 2026Article
- Can artificial intelligence in orthopantomography advance dental diagnostics through automated image analysis?Frontiers in radiology · 2026Review
- Challenges and Limitations of Multimodal Large Language Models in Interpreting Pediatric Panoramic Radiographs.International journal of paediatric dentistry · 2026Article
- A six-level clinical autonomy framework for artificial intelligence in dentistry.Frontiers in oral health · 2026Article
- Detection accuracy of an AI platform for dental treatment features on panoramic radiographs - tooth- and patient-level analyses.Scientific reports · 2025Article
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This clinical study aimed to evaluate the sensitivity, specificity, accuracy, and precision of artificial intelligence (AI) in assessing permanent teeth in pediatric patients. Over one thousand consecutive DPRs taken in Kielce, Poland, with the Carestream CS9600 device were screened. In the study material, 35 dental panoramic radiographs (DPRs) of patients of developmental age were identified and included. They were automatically evaluated with an AI algorithm. The DPRs were then analyzed by researchers. The status of the following dichotomous variables was assessed: (1) decay, (2) missing tooth, (3) filled tooth, (4) root canal filling, and (5) endodontic lesion. The results showed high specificity and accuracy (all above 85%) in detecting caries, dental fillings, and missing teeth but low precision. This study provided a detailed assessment of AI performance in a previously neglected age group. In conclusion, the overall accuracy of AI algorithms for evaluating permanent dentition in dental panoramic radiographs is lower for pediatric patients than adults or the entire population. Hence, identifying primary teeth should be implemented in AI-driven software, at least so as to ignore them when assessing mixed dentition (ClinicalTrials.gov registration number: NCT06258798).
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