ArticleJournal of pharmacy & bioallied sciences2025
Article in Journal of pharmacy & bioallied sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Introduction: The use of artificial intelligence (AI) has the potential to improve both efficiency and accuracy in the process of automating orthodontic diagnosis and treatment planning operations. The purpose of this work is to evaluate the efficacy of a deep learning-based system for computer-aided diagnosis and treatment planning by using digital orthodontic models. Materials and Methods: The AI system was trained to recognize malocclusions and generate treatment recommendations by using a collection of one hundred digital models in three dimensions. It was determined whether the results generated by AI were comparable with the evaluations made by qualified orthodontists. Results: The technique obtained a high level of accuracy in malocclusion categorization, with sensitivity and specificity that were both greater than 90% in the majority of cases. Conclusion: Despite the fact that these findings lend credence to the potential of AI in orthodontic operations, more clinical validation requires further investigation.
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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.