Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs
Background: Artificial intelligence (AI) is revolutionizing diagnostic practices in dentistry by enhancing accuracy and efficiency. Accurate diagnosis of oral lesions from radiographs is critical for early intervention and treatment planning. This study evaluates the diagnostic accuracy of AI-based...
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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 |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/jpbs.jpbs_78_25 |
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| author | Hamzah Ali Babkair Mohammed Enamur Rashid Abedalla Abdelghani Tarek M. Ibrahim Mohammad Khursheed Alam |
| author_facet | Hamzah Ali Babkair Mohammed Enamur Rashid Abedalla Abdelghani Tarek M. Ibrahim Mohammad Khursheed Alam |
| author_sort | Hamzah Ali Babkair |
| collection | DOAJ |
| description | Background:
Artificial intelligence (AI) is revolutionizing diagnostic practices in dentistry by enhancing accuracy and efficiency. Accurate diagnosis of oral lesions from radiographs is critical for early intervention and treatment planning. This study evaluates the diagnostic accuracy of AI-based software compared to expert radiologists in identifying oral lesions.
Materials and Methods:
A total of 500 radiographic images were collected from a dental teaching hospital. The images included common oral lesions such as cysts, tumors, and infections. AI-based diagnostic software was used to analyze the images, and its performance was compared to that of three experienced radiologists. Sensitivity, specificity, and accuracy were calculated for both methods. Statistical analysis was performed using the Chi-square test, with a significance level set at P < 0.05.
Results:
The AI-based software demonstrated an overall sensitivity of 92%, specificity of 88%, and accuracy of 90%. In comparison, the expert radiologists showed an average sensitivity of 95%, specificity of 91%, and accuracy of 93%. The AI software performed better in detecting small lesions (accuracy: 88%) but was slightly less accurate for complex cases such as mixed radiolucent and radiopaque lesions (accuracy: 86%).
Conclusion:
AI-based diagnostic software is a promising tool for diagnosing oral lesions from radiographs, offering high sensitivity and accuracy. While it performs comparably to expert radiologists in most cases, further optimization is needed for complex lesion types. The integration of AI in routine diagnostic workflows could significantly enhance clinical efficiency and decision-making. |
| format | Article |
| id | doaj-art-9821d5a9f88e40148d45daabfddac5f9 |
| institution | DOAJ |
| 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-9821d5a9f88e40148d45daabfddac5f92025-08-20T03:15:19ZengWolters Kluwer Medknow PublicationsJournal of Pharmacy and Bioallied Sciences0976-48790975-74062025-06-0117Suppl 2S1255S125710.4103/jpbs.jpbs_78_25Assessing AI-Based Software’s Precision in Identifying Oral Lesions from RadiographsHamzah Ali BabkairMohammed Enamur RashidAbedalla AbdelghaniTarek M. IbrahimMohammad Khursheed AlamBackground: Artificial intelligence (AI) is revolutionizing diagnostic practices in dentistry by enhancing accuracy and efficiency. Accurate diagnosis of oral lesions from radiographs is critical for early intervention and treatment planning. This study evaluates the diagnostic accuracy of AI-based software compared to expert radiologists in identifying oral lesions. Materials and Methods: A total of 500 radiographic images were collected from a dental teaching hospital. The images included common oral lesions such as cysts, tumors, and infections. AI-based diagnostic software was used to analyze the images, and its performance was compared to that of three experienced radiologists. Sensitivity, specificity, and accuracy were calculated for both methods. Statistical analysis was performed using the Chi-square test, with a significance level set at P < 0.05. Results: The AI-based software demonstrated an overall sensitivity of 92%, specificity of 88%, and accuracy of 90%. In comparison, the expert radiologists showed an average sensitivity of 95%, specificity of 91%, and accuracy of 93%. The AI software performed better in detecting small lesions (accuracy: 88%) but was slightly less accurate for complex cases such as mixed radiolucent and radiopaque lesions (accuracy: 86%). Conclusion: AI-based diagnostic software is a promising tool for diagnosing oral lesions from radiographs, offering high sensitivity and accuracy. While it performs comparably to expert radiologists in most cases, further optimization is needed for complex lesion types. The integration of AI in routine diagnostic workflows could significantly enhance clinical efficiency and decision-making.https://journals.lww.com/10.4103/jpbs.jpbs_78_25ai in dentistryartificial intelligencediagnostic accuracyoral lesionsradiographic diagnosis |
| spellingShingle | Hamzah Ali Babkair Mohammed Enamur Rashid Abedalla Abdelghani Tarek M. Ibrahim Mohammad Khursheed Alam Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs Journal of Pharmacy and Bioallied Sciences ai in dentistry artificial intelligence diagnostic accuracy oral lesions radiographic diagnosis |
| title | Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs |
| title_full | Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs |
| title_fullStr | Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs |
| title_full_unstemmed | Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs |
| title_short | Assessing AI-Based Software’s Precision in Identifying Oral Lesions from Radiographs |
| title_sort | assessing ai based software s precision in identifying oral lesions from radiographs |
| topic | ai in dentistry artificial intelligence diagnostic accuracy oral lesions radiographic diagnosis |
| url | https://journals.lww.com/10.4103/jpbs.jpbs_78_25 |
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