Academic Journal
Comparison of the Artificial Intelligence Versus Traditional Radiographic Interpretation in Detecting Periapical Periodontitis: A Diagnostic Accuracy Study
| Title: | Comparison of the Artificial Intelligence Versus Traditional Radiographic Interpretation in Detecting Periapical Periodontitis: A Diagnostic Accuracy Study |
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| Authors: | Bharath Nagareddy, Rohit Vadlamani, Nikhila Reddy Venkannagari, Sulabh Jain, Syed N. Basheer, Sabari Murugesan |
| Source: | Journal of Pharmacy and Bioallied Sciences, Vol 16, Iss Suppl 4, Pp S3676-S3678 (2024) |
| Publisher Information: | Wolters Kluwer Medknow Publications, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Pharmacy and materia medica LCC:Analytical chemistry |
| Subject Terms: | ai, artificial intelligence, diagnosis, diagnostic test accuracy, periapical periodontitis, Pharmacy and materia medica, RS1-441, Analytical chemistry, QD71-142 |
| More Details: | Objective: To compare the performance of an AI model with that of experienced radiologists in detecting periapical periodontitis using radiographic images. Methods: Thirty radiographic images (CBCT, panoramic, and periapical) were analyzed by an AI model and two experienced radiologists. Diagnostic accuracy, sensitivity, specificity, and confidence levels were evaluated. Statistical analyses included Chi-square tests, independent samples t-tests, and Pearson correlation analysis. Results: The AI model achieved 89.6% accuracy, 86.5% sensitivity, and 88.1% specificity. Radiologist 1 showed the highest performance (accuracy 98.5%, sensitivity 93.8%, specificity 96.7%), while Radiologist 2 performed slightly lower than the AI model (accuracy 81.7%, sensitivity 83.3%, specificity 80%). The AI model demonstrated the highest mean confidence level (86.5% ± 9.18). Moderate positive correlations were observed between the AI’s confidence and that of Radiologist 1 (0.383) and Radiologist 2 (0.347). Conclusions: The AI model demonstrated comparable performance to experienced radiologists in detecting periapical periodontitis. These findings suggest that AI could serve as a valuable tool in dental diagnostics, potentially improving efficiency and consistency. However, further research is needed to refine AI models and evaluate their performance across diverse clinical scenarios. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 0976-4879 0975-7406 |
| Relation: | https://journals.lww.com/10.4103/jpbs.jpbs_1096_24; https://doaj.org/toc/0976-4879; https://doaj.org/toc/0975-7406 |
| DOI: | 10.4103/jpbs.jpbs_1096_24 |
| Access URL: | https://doaj.org/article/bb9f771dab23449b875e3d8934369f36 |
| Accession Number: | edsdoj.bb9f771dab23449b875e3d8934369f36 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 09764879 09757406 |
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| DOI: | 10.4103/jpbs.jpbs_1096_24 |
| Published in: | Journal of Pharmacy and Bioallied Sciences |
| Language: | English |