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Related Experiment Video

Updated: Dec 21, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based

Chaminda Jayampath Seneviratne1, Preethi Balan1, Tanujaa Suriyanarayanan1

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Research on the oral microbiome

Keywords:
High-throughput DNA sequencingdeep learningmachine learningmetagenomicsoral healthsaliva

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Area of Science:

  • Oral microbiome research
  • Systemic disease links
  • Bioinformatics

Background:

  • Growing evidence links oral microbiome to systemic diseases.
  • Inconsistent findings due to study design and confounding variables.
  • Limitations in current oral microbiome-systemic disease studies.

Purpose of the Study:

  • Review influence of study design on oral microbiome-systemic disease links.
  • Discuss limitations in studies of type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer.
  • Propose AI/ML/DL for predicting systemic disease from oral microbiome data.

Main Methods:

  • Narrative review of existing literature.
  • Analysis of study design and confounding variables.
  • Exploration of AI/ML/DL applications in oral microbiome research.

Main Results:

  • Study design and confounding variables significantly impact oral microbiome-systemic disease findings.
  • Current studies on specific diseases show limitations.
  • AI offers potential for improved prediction accuracy.

Conclusions:

  • Standardized study designs and comprehensive datasets are crucial.
  • AI, particularly machine learning and deep learning, can enhance systemic disease prediction from oral microbiome data.
  • Global collaboration for data sharing is essential for AI model development and clinical application.