Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study Article Swipe
Ricardo Jorge Pais
,
João Botelho
,
Vanessa Machado
,
Gil Alcoforado
,
José João Mendes
,
Ricardo Alves
,
Lucinda J. Bessa
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0529.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0529.v1
Background: Machine-learning (ML) techniques have been recently proposed as a solution for aiding in the prevention and diagnosis of microbiome-related diseases. Here, we applied auto-ML approaches on real-case metagenomic datasets from saliva and subgingival peri-implant biofilm microbiomes to explore a wide range of ML algorithms to benchmark best-performing algorithms for predicting peri-implantitis (PI). Methods: A total of 100 metagenomes from the NCBI SRA database (PRJNA1163384) were used in this study to construct biofilm and saliva metagenomes datasets. Two AI-driven auto-ML approaches were used on constructed datasets to generate 100 ML-based models for the prediction of PI. These were compared with statistically significant single microorganism-based models. Results: Several ML algorithms were pinpointed as suitable bespoke predictive approaches to apply to metagenomic data, outperforming the single microorganism-based classification. Auto-ML approaches rendered high-performing models with AUC values, sensitivities and specificities between 80% and 100%. Among these, classifiers based on ML-driven scoring of combinations of 2-4 microorganisms presented top-ranked performances and can be suitable for clinical application. Moreover, models generated based on the saliva microbiome showed higher predictive performance than those from the biofilm microbiome. Conclusions: This feasibility study bridges complex AI research with practical dental applications by benchmarking ML algorithms and exploring oral microbiomes as foundations for developing intuitive, cost-effective, and clinically relevant diagnostic platforms.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202501.0529.v1
- OA Status
- green
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4406184956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202501.0529.v1Digital Object Identifier
- Title
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Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility StudyWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-07Full publication date if available
- Authors
-
Ricardo Jorge Pais, João Botelho, Vanessa Machado, Gil Alcoforado, José João Mendes, Ricardo Alves, Lucinda J. BessaList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202501.0529.v1Publisher landing page
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.20944/preprints202501.0529.v1Direct OA link when available
- Concepts
-
Peri-implantitis, Microbiome, Computer science, Artificial intelligence, Machine learning, Data science, Medicine, Biology, Bioinformatics, Surgery, ImplantTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.performances | 155 |
| abstract_inverted_index.peri-implant | 34 |
| abstract_inverted_index.outperforming | 121 |
| abstract_inverted_index.sensitivities | 134 |
| abstract_inverted_index.specificities | 136 |
| abstract_inverted_index.statistically | 100 |
| abstract_inverted_index.(PRJNA1163384) | 64 |
| abstract_inverted_index.microorganisms | 152 |
| abstract_inverted_index.best-performing | 47 |
| abstract_inverted_index.classification. | 125 |
| abstract_inverted_index.cost-effective, | 206 |
| abstract_inverted_index.high-performing | 129 |
| abstract_inverted_index.Machine-learning | 1 |
| abstract_inverted_index.peri-implantitis | 51 |
| abstract_inverted_index.microbiome-related | 19 |
| abstract_inverted_index.microorganism-based | 103, 124 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.00911541 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |