Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study
Background: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate students in determining working length on intraoral periapic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-01-01
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| Series: | Contemporary Clinical Dentistry |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/ccd.ccd_274_24 |
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| author | R. S. Basavanna Ishaan Adhaulia N. M. Dhanyakumar Jyoti Joshi |
| author_facet | R. S. Basavanna Ishaan Adhaulia N. M. Dhanyakumar Jyoti Joshi |
| author_sort | R. S. Basavanna |
| collection | DOAJ |
| description | Background:
The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate students in determining working length on intraoral periapical radiographs.
Materials and Methods:
One hundred anonymized radiographs of single-rooted teeth with files at working length were obtained. The images were preprocessed and used to train a deep learning model. Five dental postgraduates visually estimated the working length after receiving training. Pixel counting in image processing software provided the gold standard measurement. Accuracy comparisons were performed using a t-test.
Results:
The deep learning model demonstrated significantly higher accuracy (85%) compared to human estimations (mean accuracy 75.4%). The t-test yielded P = 0.0374 (P < 0.05), rejecting the null hypothesis.
Conclusion:
Deep learning models show great potential in enhancing precision and reliability for working length determination in endodontics. With further refinement, these models can effectively complement human expertise in dental radiographic interpretation. |
| format | Article |
| id | doaj-art-00722f00a50d4204be785a55a76fc015 |
| institution | OA Journals |
| issn | 0976-237X 0976-2361 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Contemporary Clinical Dentistry |
| spelling | doaj-art-00722f00a50d4204be785a55a76fc0152025-08-20T02:08:43ZengWolters Kluwer Medknow PublicationsContemporary Clinical Dentistry0976-237X0976-23612025-01-01161151810.4103/ccd.ccd_274_24Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro StudyR. S. BasavannaIshaan AdhauliaN. M. DhanyakumarJyoti JoshiBackground: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate students in determining working length on intraoral periapical radiographs. Materials and Methods: One hundred anonymized radiographs of single-rooted teeth with files at working length were obtained. The images were preprocessed and used to train a deep learning model. Five dental postgraduates visually estimated the working length after receiving training. Pixel counting in image processing software provided the gold standard measurement. Accuracy comparisons were performed using a t-test. Results: The deep learning model demonstrated significantly higher accuracy (85%) compared to human estimations (mean accuracy 75.4%). The t-test yielded P = 0.0374 (P < 0.05), rejecting the null hypothesis. Conclusion: Deep learning models show great potential in enhancing precision and reliability for working length determination in endodontics. With further refinement, these models can effectively complement human expertise in dental radiographic interpretation.https://journals.lww.com/10.4103/ccd.ccd_274_24artificial intelligencedeep learningdental radiographyendodonticsworking length determination |
| spellingShingle | R. S. Basavanna Ishaan Adhaulia N. M. Dhanyakumar Jyoti Joshi Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study Contemporary Clinical Dentistry artificial intelligence deep learning dental radiography endodontics working length determination |
| title | Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study |
| title_full | Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study |
| title_fullStr | Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study |
| title_full_unstemmed | Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study |
| title_short | Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study |
| title_sort | evaluating the accuracy of deep learning models and dental postgraduate students in measuring working length on intraoral periapical x rays an in vitro study |
| topic | artificial intelligence deep learning dental radiography endodontics working length determination |
| url | https://journals.lww.com/10.4103/ccd.ccd_274_24 |
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