โก Quick Summary
Recent advancements in artificial intelligence (AI) have shown promise in predicting oral cancer through the analysis of the oral microbiome. This innovative approach could significantly enhance early detection and personalized treatment strategies for oral squamous cell carcinoma (OSCC).
๐ Key Details
- ๐ Dataset: 3,382 records reviewed, 44 studies included
- ๐งฉ Focus: Role of oral microbiome in oral cancer prediction
- โ๏ธ Technologies: Machine learning algorithms including logistic regression, random forests, and artificial neural networks
- ๐ Findings: Unique microbial patterns associated with OSCC identified
๐ Key Takeaways
- ๐ฆ Oral microbiome composition may serve as a biomarker for cancer prediction.
- ๐ค AI techniques can analyze complex microbiome data to uncover associations with cancer.
- ๐ Machine learning algorithms have demonstrated effectiveness in identifying microbial patterns linked to OSCC.
- ๐ Further research is needed for standardization and validation in clinical settings.
- ๐ก AI-driven models offer a noninvasive and cost-effective tool for predicting disease progression.
- ๐ Potential impact on early detection, risk stratification, and personalized treatment strategies.
- ๐๏ธ Future studies should focus on large-scale, multi-centric, and longitudinal validation.
๐ Background
Oral cancer, particularly oral squamous cell carcinoma (OSCC), is a significant global health issue, ranking sixth in prevalence. Unfortunately, OSCC is often diagnosed at advanced stages, which highlights the urgent need for innovative early detection methods. The oral microbiome, a complex community of microorganisms in the oral cavity, has emerged as a potential biomarker for cancer prediction and progression.
๐๏ธ Study
This review analyzed a decade’s worth of research on the role of the oral microbiome in oral cancer prediction using AI-powered tools. A total of 3,382 records were identified, with 44 studies meeting the inclusion criteria. The focus was on how various machine learning algorithms can identify unique microbial patterns associated with OSCC and other malignancies.
๐ Results
The application of machine learning algorithms, such as logistic regression, random forests, and artificial neural networks, has revealed significant associations between oral microbiome composition and oral cancer. These findings underscore the transformative potential of AI in understanding the microbiome’s role in cancer studies.
๐ Impact and Implications
The integration of AI with oral microbiome analysis holds substantial promise for improving early detection and personalized treatment strategies for OSCC. By identifying unique microbial patterns associated with cancer, AI-driven models could provide a noninvasive and cost-effective tool for predicting disease progression and guiding clinical decision-making. However, translating these advancements into routine clinical practice will require standardized protocols and validation through extensive studies.
๐ฎ Conclusion
This review highlights the incredible potential of AI in the realm of oral cancer prediction through microbiome analysis. By leveraging advanced computational techniques, we can enhance early detection and improve patient outcomes. The future of oral cancer management looks promising, and further research is essential to fully realize these advancements in clinical settings.
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Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.
Abstract
Oral cancer is a major global health burden, ranking sixth in prevalence, with oral squamous cell carcinoma (OSCC) being the most common type. Importantly, OSCC is often diagnosed at late stages, underscoring the need for innovative methods for early detection. The oral microbiome, an active microbial community within the oral cavity, holds promise as a biomarker for the prediction and progression of cancer. Emerging computational techniques in the artificial intelligence (AI) field have enabled the analysis of complex microbiome data sets to unravel the association between oral microbiome composition and oral cancer. This review provides a comprehensive overview of learning-based algorithms applied to oral microbiome data for cancer prediction. In particular, this work discusses how typical machine learning (ML) algorithms, such as logistic regression, random forests, and artificial neural networks, identify the unique microbial patterns associated with oral cancer and other malignancies. A search was conducted in Pubmed covering a 10-year period. The goal was to identify previous studies focused on the role of the oral microbiome in oral cancer prediction using AI-powered tools. The search strategy identified 3382 records in total, of which 44 studies met the inclusion criteria. While AI has shown a transformative power in understanding and revealing the oral microbiome’s role in cancer studies, its application in clinical settings requires further efforts on standardization of protocols, curation of diverse cohorts, and validation through large-scale multi-centric and longitudinal studies. The integration of AI with oral microbiome analysis holds significant promise for improving early detection, risk stratification, and personalized treatment strategies for OSCC. By identifying unique microbial patterns associated with cancer, AI-driven models offer a noninvasive, cost-effective tool to predict disease progression and guide clinical decision-making. However, translating these advancements into routine clinical practice requires standardized protocols, diverse patient cohorts, and validation through large-scale, longitudinal studies. Once implemented, this approach could transform oral cancer management, enabling timely interventions and improving patient outcomes.
Author: [‘Soghli N’, ‘Khormali A’, ‘Mahboubi D’, ‘Peng A’, ‘Miguez PA’]
Journal: Periodontol 2000
Citation: Soghli N, et al. Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome. Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome. 2025; (unknown volume):(unknown pages). doi: 10.1111/prd.70000