Publicação
Exploring AI-driven machine learning approaches for optimal classification of peri-implantitis based on oral microbiome data : a feasibility study
| datacite.subject.fos | Ciências Médicas::Ciências da Saúde | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Pais, Ricardo Jorge | |
| dc.contributor.author | Botelho, João | |
| dc.contributor.author | Machado, Vanessa | |
| dc.contributor.author | Alcoforado, Gil | |
| dc.contributor.author | Mendes, José João | |
| dc.contributor.author | Alves, Ricardo | |
| dc.contributor.author | Bessa, Lucinda J. | |
| dc.date.accessioned | 2026-05-07T14:06:03Z | |
| dc.date.available | 2026-05-07T14:06:03Z | |
| dc.date.issued | 2025-02 | |
| dc.description.abstract | 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 Receiver Operating Characteristic–Area Under the Curve, 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. | eng |
| dc.identifier.citation | Pais RJ, Botelho J, Machado V, Alcoforado G, Mendes JJ, Alves R, Bessa LJ. Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study. Diagnostics. 2025; 15(4):425. https://doi.org/10.3390/diagnostics15040425 | |
| dc.identifier.doi | 10.3390/diagnostics15040425 | |
| dc.identifier.issn | 2075-4418 | |
| dc.identifier.uri | http://hdl.handle.net/10400.26/63026 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.hasversion | https://doi.org/10.3390/diagnostics15040425 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | peri-implantitis | |
| dc.subject | machine learning | |
| dc.subject | metagenomic data | |
| dc.subject | dental implants | |
| dc.subject | biomarkers | |
| dc.subject | saliva | |
| dc.subject | peri-implant biofilms | |
| dc.title | Exploring AI-driven machine learning approaches for optimal classification of peri-implantitis based on oral microbiome data : a feasibility study | eng |
| dc.type | contribution to journal | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 4 | |
| oaire.citation.startPage | 425 | |
| oaire.citation.title | Diagnostics | |
| oaire.citation.volume | 15 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |