Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study
Background: This study evaluated AI-based analysis of dental periapical lesions using CBCT scans, conducted at the Department of Oral Medicine and Radiology, New Horizon Dental College, Bilaspur, Chhattisgarh. Method: A total of 500 CBCT scans were analyzed, with 400 scans used to train AI software...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-06-01
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| Series: | Journal of Pharmacy and Bioallied Sciences |
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| Online Access: | https://journals.lww.com/10.4103/jpbs.jpbs_2002_24 |
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| author | Giridhar Naidu Ramanpal Singh Makkad Fiza Khan Pallavi Sinha Swatantra Shrivastava Narayan Prasad Tripathi Dasharathraj K. Shetty Sarthak Shastri Ankita Shrivastava |
| author_facet | Giridhar Naidu Ramanpal Singh Makkad Fiza Khan Pallavi Sinha Swatantra Shrivastava Narayan Prasad Tripathi Dasharathraj K. Shetty Sarthak Shastri Ankita Shrivastava |
| author_sort | Giridhar Naidu |
| collection | DOAJ |
| description | Background:
This study evaluated AI-based analysis of dental periapical lesions using CBCT scans, conducted at the Department of Oral Medicine and Radiology, New Horizon Dental College, Bilaspur, Chhattisgarh.
Method:
A total of 500 CBCT scans were analyzed, with 400 scans used to train AI software and 100 scans assessed by two radiologists to test the software’s performance. The AI classified lesions into periapical cysts, abscesses, or granulomas. Sensitivity, specificity, and accuracy were calculated.
Result:
Cysts were the largest lesions, with regular margins (99.09%) and significant cortical expansion (93.36%), causing teeth displacement (66.36%). Abscesses and granulomas predominantly affected the maxilla, showing moderate hypodensity (100%) with minimal structural changes. Radiologists achieved perfect agreement (0.98, P < .001) in 77% of scans. Manual machine learning AI achieved 100% accuracy, while deep learning AI demonstrated 84.62% accuracy, with moderate to substantial agreement for lesion dimensions.
Conclusion:
Manual machine learning AI showed superior accuracy compared to deep learning AI, demonstrating its potential for radiographic diagnosis of periapical lesions. |
| format | Article |
| id | doaj-art-a5235afafffa40579801bd7b844e6834 |
| institution | Kabale University |
| issn | 0976-4879 0975-7406 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Pharmacy and Bioallied Sciences |
| spelling | doaj-art-a5235afafffa40579801bd7b844e68342025-08-20T03:31:38ZengWolters Kluwer Medknow PublicationsJournal of Pharmacy and Bioallied Sciences0976-48790975-74062025-06-0117Suppl 2S1475S147710.4103/jpbs.jpbs_2002_24Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological StudyGiridhar NaiduRamanpal Singh MakkadFiza KhanPallavi SinhaSwatantra ShrivastavaNarayan Prasad TripathiDasharathraj K. ShettySarthak ShastriAnkita ShrivastavaBackground: This study evaluated AI-based analysis of dental periapical lesions using CBCT scans, conducted at the Department of Oral Medicine and Radiology, New Horizon Dental College, Bilaspur, Chhattisgarh. Method: A total of 500 CBCT scans were analyzed, with 400 scans used to train AI software and 100 scans assessed by two radiologists to test the software’s performance. The AI classified lesions into periapical cysts, abscesses, or granulomas. Sensitivity, specificity, and accuracy were calculated. Result: Cysts were the largest lesions, with regular margins (99.09%) and significant cortical expansion (93.36%), causing teeth displacement (66.36%). Abscesses and granulomas predominantly affected the maxilla, showing moderate hypodensity (100%) with minimal structural changes. Radiologists achieved perfect agreement (0.98, P < .001) in 77% of scans. Manual machine learning AI achieved 100% accuracy, while deep learning AI demonstrated 84.62% accuracy, with moderate to substantial agreement for lesion dimensions. Conclusion: Manual machine learning AI showed superior accuracy compared to deep learning AI, demonstrating its potential for radiographic diagnosis of periapical lesions.https://journals.lww.com/10.4103/jpbs.jpbs_2002_24artificial intelligencecbct scansdeep learningperiapical lesions |
| spellingShingle | Giridhar Naidu Ramanpal Singh Makkad Fiza Khan Pallavi Sinha Swatantra Shrivastava Narayan Prasad Tripathi Dasharathraj K. Shetty Sarthak Shastri Ankita Shrivastava Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study Journal of Pharmacy and Bioallied Sciences artificial intelligence cbct scans deep learning periapical lesions |
| title | Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study |
| title_full | Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study |
| title_fullStr | Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study |
| title_full_unstemmed | Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study |
| title_short | Evaluation of Artificial Intelligent Systems Based Analysis in Dental Periapical Lesions – A Radiological Study |
| title_sort | evaluation of artificial intelligent systems based analysis in dental periapical lesions a radiological study |
| topic | artificial intelligence cbct scans deep learning periapical lesions |
| url | https://journals.lww.com/10.4103/jpbs.jpbs_2002_24 |
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