ReviewImaging science in dentistry2025
Application of artificial intelligence in the diagnosis and management of temporomandibular joint osteoarthritis using cone-beam computed tomography: An evidence-based systematic review.
Review in Imaging science in dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Temporomandibular Disorders Diagnosis: Current Challenges and the Promising Role of Artificial Intelligence.European journal of dentistry · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Temporomandibular joint osteoarthritis (TMJOA) is a significant subtype of temporomandibular joint disorders (TMDs). The purpose of this study was to comprehensively summarize the current literature on the use of artificial intelligence (AI) technologies in the diagnosis and management of TMJOA using cone-beam computed tomography (CBCT). Materials and Methods: This systematic review was pre-registered in the PROSPERO database (PROSPERO CRD42024509772). Up to December 2023, research was conducted using Google Scholar, Embase, MEDLINE, and Web of Science databases to identify studies evaluating the use of AI technologies in the management and diagnosis of TMJOA via CBCT. The search strategy included MeSH terms, keywords, and their combinations. Risk of bias was assessed using the ROBINS-I tool. Results: Out of 2,543 articles retrieved, a total of 9 studies were included in this systematic review. All included studies were observational and employed AI models based on convolutional neural networks, including SVA, SSD, LightGBM, XGBoost, and YOLO. The performance of these models varied, with accuracy ranging from 73.5% to 99% and F1-scores between 0.80 and 0.86. Among these, YOLO demonstrated the highest accuracy for the assessment and diagnosis of TMJOA using CBCT scans. Conclusion: AI algorithms developed for the automated diagnosis of TMJOA can be utilized by clinicians as decision-support tools. Incorporating diverse input data types, such as electronic medical records, radiomics features, and biomarkers, alongside diagnostic imaging may further increase the diagnostic accuracy for TMDs.
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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.