Diagnostic and Prognostic Performance of Artificial Intelligence Models in Detecting Odontogenic Keratocysts from Histopathologic Images: A Systematic Review and Meta-Analysis
| Author | Reyhaneh Shoorgashti | en |
| Author | Farnaz Jafari | en |
| Author | Asma Yazdanfar | en |
| Author | Sarah Sadat Ehsani | en |
| Author | Sana Baghizadeh | en |
| Author | Mohadeseh Alimohammadi | en |
| Author | Simin Lesan | en |
| Author | Donia Sadri | en |
| Orcid | Reyhaneh Shoorgashti [0000-0002-1658-8983] | en |
| Orcid | Farnaz Jafari [0000-0003-0278-3048] | en |
| Orcid | Sarah Sadat Ehsani [0009-0007-7564-570X] | en |
| Orcid | Sana Baghizadeh [0000-0002-7983-4803] | en |
| Orcid | Mohadeseh Alimohammadi [0009-0008-7506-9957] | en |
| Orcid | Simin Lesan [0000-0003-3877-328X] | en |
| Orcid | Donia Sadri [0000-0001-7408-476X] | en |
| Issued Date | 2025-07-31 | en |
| Abstract | Context: Odontogenic keratocysts (OKCs) are aggressive jaw cysts characterized by a high recurrence rate, making accurate diagnosis critical for effective treatment. Recent advances in artificial intelligence (AI) have demonstrated potential for enhancing diagnostic accuracy in histopathology. However, the effectiveness of AI in diagnosing OKCs has not yet been systematically reviewed. Objectives: This study aims to evaluate the diagnostic and prognostic performance of AI models in detecting OKCs in histopathologic images. Methods: This systematic review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A comprehensive literature search was performed across PubMed, Scopus, Embase, Google Scholar, and ScienceDirect to identify studies that utilized AI models for diagnosing OKCs from histopathologic images. Studies were eligible for inclusion if they addressed the PICO (patient/population, intervention, comparison, and outcomes) framework, specifically investigating whether AI models (I) can enhance diagnostic and prognostic accuracy (O) for OKCs in histopathologic images (P). A meta-analysis was performed to pool the diagnostic performance of AI models across studies, and Egger’s test was conducted to assess publication bias. Results: A total of eight studies were included in the review. The risk of bias (ROB) across the included studies was generally low, with a few exceptions. The pooled area under the curve (AUC) for AI models in diagnosing OKCs was 0.967 (95% CI: 0.957 - 0.978). The pooled sensitivity ranged from 0.89 to 0.92, and the pooled specificity ranged from 0.88 to 0.94. The summary receiver operating characteristic (sROC) curve demonstrated an AUC of 0.93. Egger’s test for publication bias yielded a P-value of 0.522, indicating no significant evidence of publication bias. The review also highlighted several limitations, including small sample sizes, lack of external validation, and limited interpretability of the AI models. Conclusions: Artificial intelligence models, particularly deep learning architectures, demonstrate high diagnostic accuracy in detecting OKCs from histopathologic images. | en |
| DOI | https://doi.org/10.5812/mejrh-158082 | en |
| Keyword | Artificial Intelligence | en |
| Keyword | AI | en |
| Keyword | Deep Learning | en |
| Keyword | Machine Learning | en |
| Keyword | Odontogenic Cyst | en |
| Keyword | Odontogenic Keratocyst | en |
| Keyword | OKCs | en |
| Keyword | Pathology | en |
| Publisher | Brieflands | en |
| Title | Diagnostic and Prognostic Performance of Artificial Intelligence Models in Detecting Odontogenic Keratocysts from Histopathologic Images: A Systematic Review and Meta-Analysis | en |
| Type | Systematic Review | en |
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