Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis
ABSTRACT Background and Aims Odontogenic keratocyst (OKC) is a radiolucent jaw lesion often mistaken for similar conditions like ameloblastomas on panoramic radiographs. Accurate diagnosis is vital for effective management, but manual image interpretation can be inconsistent. While deep learning alg...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70614 |
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| author | Reyhaneh Shoorgashti Mohadeseh Alimohammadi Sana Baghizadeh Bahareh Radmard Hooman Ebrahimi Simin Lesan |
| author_facet | Reyhaneh Shoorgashti Mohadeseh Alimohammadi Sana Baghizadeh Bahareh Radmard Hooman Ebrahimi Simin Lesan |
| author_sort | Reyhaneh Shoorgashti |
| collection | DOAJ |
| description | ABSTRACT Background and Aims Odontogenic keratocyst (OKC) is a radiolucent jaw lesion often mistaken for similar conditions like ameloblastomas on panoramic radiographs. Accurate diagnosis is vital for effective management, but manual image interpretation can be inconsistent. While deep learning algorithms in AI have shown promise in improving diagnostic accuracy for OKCs, their performance across studies is still unclear. This systematic review and meta‐analysis aimed to evaluate the diagnostic accuracy of AI models in detecting OKC from panoramic radiographs. Methods A systematic search was performed across 5 databases. Studies were included if they examined the PICO question of whether AI models (I) could improve the diagnostic accuracy (O) of OKC in panoramic radiographs (P) compared to reference standards (C). Key performance metrics including sensitivity, specificity, accuracy, and area under the curve (AUC) were extracted and pooled using random‐effects models. Meta‐regression and subgroup analyses were conducted to identify sources of heterogeneity. Publication bias was evaluated through funnel plots and Egger's test. Results Eight studies were included in the meta‐analysis. The pooled sensitivity across all studies was 83.66% (95% CI:73.75%–93.57%) and specificity was 82.89% (95% CI:70.31%–95.47%). YOLO‐based models demonstrated superior diagnostic performance with a sensitivity of 96.4% and specificity of 96.0%, compared to other architectures. Meta‐regression analysis indicated that model architecture was a significant predictor of diagnostic performance, accounting for a significant portion of the observed heterogeneity. However, the analysis also revealed publication bias and high variability across studies (Egger's test, p = 0.042). Conclusion AI models, particularly YOLO‐based architectures, can improve the diagnostic accuracy of OKCs in panoramic radiographs. While AI shows strong capabilities in simple cases, it should complement, not replace, human expertise, especially in complex situations. |
| format | Article |
| id | doaj-art-d2c893eb6b414302b3e043c81943a11c |
| institution | OA Journals |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-d2c893eb6b414302b3e043c81943a11c2025-08-20T01:48:34ZengWileyHealth Science Reports2398-88352025-04-0184n/an/a10.1002/hsr2.70614Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐AnalysisReyhaneh Shoorgashti0Mohadeseh Alimohammadi1Sana Baghizadeh2Bahareh Radmard3Hooman Ebrahimi4Simin Lesan5Department of Oral and Maxillofacial Medicine, School of Dentistry Islamic Azad University of Medical Sciences Tehran IranFaculty of Dentistry, Tehran Medical Sciences Islamic Azad University Tehran IranFaculty of Dentistry, Tehran Medical Sciences Islamic Azad University Tehran IranSchool of Dentistry Shahid Beheshti University of Medical Sciences Tehran IranDepartment of Oral and Maxillofacial Medicine, School of Dentistry Islamic Azad University of Medical Sciences Tehran IranDepartment of Oral and Maxillofacial Medicine, School of Dentistry Islamic Azad University of Medical Sciences Tehran IranABSTRACT Background and Aims Odontogenic keratocyst (OKC) is a radiolucent jaw lesion often mistaken for similar conditions like ameloblastomas on panoramic radiographs. Accurate diagnosis is vital for effective management, but manual image interpretation can be inconsistent. While deep learning algorithms in AI have shown promise in improving diagnostic accuracy for OKCs, their performance across studies is still unclear. This systematic review and meta‐analysis aimed to evaluate the diagnostic accuracy of AI models in detecting OKC from panoramic radiographs. Methods A systematic search was performed across 5 databases. Studies were included if they examined the PICO question of whether AI models (I) could improve the diagnostic accuracy (O) of OKC in panoramic radiographs (P) compared to reference standards (C). Key performance metrics including sensitivity, specificity, accuracy, and area under the curve (AUC) were extracted and pooled using random‐effects models. Meta‐regression and subgroup analyses were conducted to identify sources of heterogeneity. Publication bias was evaluated through funnel plots and Egger's test. Results Eight studies were included in the meta‐analysis. The pooled sensitivity across all studies was 83.66% (95% CI:73.75%–93.57%) and specificity was 82.89% (95% CI:70.31%–95.47%). YOLO‐based models demonstrated superior diagnostic performance with a sensitivity of 96.4% and specificity of 96.0%, compared to other architectures. Meta‐regression analysis indicated that model architecture was a significant predictor of diagnostic performance, accounting for a significant portion of the observed heterogeneity. However, the analysis also revealed publication bias and high variability across studies (Egger's test, p = 0.042). Conclusion AI models, particularly YOLO‐based architectures, can improve the diagnostic accuracy of OKCs in panoramic radiographs. While AI shows strong capabilities in simple cases, it should complement, not replace, human expertise, especially in complex situations.https://doi.org/10.1002/hsr2.70614artificial intelligencedeep learningodontogenic cystsodontogenic keratocystsoral diagnosisoral health |
| spellingShingle | Reyhaneh Shoorgashti Mohadeseh Alimohammadi Sana Baghizadeh Bahareh Radmard Hooman Ebrahimi Simin Lesan Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis Health Science Reports artificial intelligence deep learning odontogenic cysts odontogenic keratocysts oral diagnosis oral health |
| title | Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis |
| title_full | Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis |
| title_fullStr | Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis |
| title_full_unstemmed | Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis |
| title_short | Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta‐Analysis |
| title_sort | artificial intelligence models accuracy for odontogenic keratocyst detection from panoramic view radiographs a systematic review and meta analysis |
| topic | artificial intelligence deep learning odontogenic cysts odontogenic keratocysts oral diagnosis oral health |
| url | https://doi.org/10.1002/hsr2.70614 |
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