ArticleJournal of pharmacy & bioallied sciences2024
Creating and Testing a New Computer Vision System for Detecting Dental Problems in Orthodontic Patients.
Article in Journal of pharmacy & bioallied sciences, 2024. 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
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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.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Utilizing Deep Learning for Diagnosing Radicular Cysts.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: The research project focuses on the creation and assessment of an innovative computer vision system designed to identify dental irregularities in individuals undergoing orthodontic treatment. Materials and Methods: To establish the computer vision system, a comprehensive dataset of dental images was collected, encompassing various orthodontic cases. The system's algorithm was trained to recognize patterns indicative of common dental anomalies, such as malocclusions, spacing issues, and misalignments. Rigorous testing and refinement of the algorithm were conducted to enhance its accuracy and reliability. Results: The validation of the system was carried out using the dental records and images of the 40 patients. The computer vision system's performance was evaluated against assessments made by experienced orthodontists. The results demonstrated a commendable level of concurrence between the system's automated detections and the orthodontists' evaluations, suggesting its potential as a valuable diagnostic tool. Conclusion: In conclusion, the development and validation of this novel computer vision system exhibit promising outcomes in its ability to automatically detect dental anomalies in orthodontic patients.
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Registered trials
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