{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:02:38Z","timestamp":1778644958841,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Portuguese National Funds","doi-asserted-by":"publisher","award":["2022.01430.PTDC"],"award-info":[{"award-number":["2022.01430.PTDC"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2013Area Under the Curve, sensitivities and specificities between 80% and 100%. Among these, classifiers based on ML-driven scoring of combinations of 2\u20134 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.<\/jats:p>","DOI":"10.3390\/diagnostics15040425","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T09:29:26Z","timestamp":1739179766000},"page":"425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5489-8790","authenticated-orcid":false,"given":"Ricardo Jorge","family":"Pais","sequence":"first","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"},{"name":"Bioenhancer Systems Ltd., Stockport SK3 0GF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1019-8263","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Botelho","sequence":"additional","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2503-260X","authenticated-orcid":false,"given":"Vanessa","family":"Machado","sequence":"additional","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1545-2267","authenticated-orcid":false,"given":"Gil","family":"Alcoforado","sequence":"additional","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0167-4077","authenticated-orcid":false,"given":"Jos\u00e9 Jo\u00e3o","family":"Mendes","sequence":"additional","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7184-5965","authenticated-orcid":false,"given":"Ricardo","family":"Alves","sequence":"additional","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8339-1964","authenticated-orcid":false,"given":"Lucinda J.","family":"Bessa","sequence":"additional","affiliation":[{"name":"Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Caparica, Almada, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kormas, I., Pedercini, C., Pedercini, A., Raptopoulos, M., Alassy, H., and Wolff, L.F. (2020). Peri-Implant Diseases: Diagnosis, Clinical, Histological, Microbiological Characteristics and Treatment Strategies. A Narrative Review. Antibiotics, 9.","DOI":"10.3390\/antibiotics9110835"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S246","DOI":"10.1111\/jcpe.12954","article-title":"Peri-Implantitis","volume":"45","author":"Schwarz","year":"2018","journal-title":"J. Clin. Periodontol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Donelli, G. (2015). Peri-Implant Infections of Oral Biofilm Etiology. Biofilm-based Healthcare-Associated Infections, Springer. Advances in Experimental Medicine and Biology.","DOI":"10.1007\/978-3-319-09782-4"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/clr.12636","article-title":"Risk Indicators for Peri-Implantitis. A Narrative Review","volume":"26","author":"Renvert","year":"2015","journal-title":"Clin. Oral Implant. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.cden.2014.10.007","article-title":"Treatment of Peri-Implantitis and the Failing Implant","volume":"59","author":"Robertson","year":"2015","journal-title":"Dent. Clin. N. Am."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Diaz, P., Gonzalo, E., Villagra, L.J.G., Miegimolle, B., and Suarez, M.J. (2022). What Is the Prevalence of Peri-Implantitis? A Systematic Review and Meta-Analysis. BMC Oral Health, 22.","DOI":"10.1186\/s12903-022-02493-8"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1111\/clr.13416","article-title":"Peri-Implantitis Prevalence, Incidence Rate, and Risk Factors: A Study of Electronic Health Records at a U.S. Dental School","volume":"30","author":"Finkelstein","year":"2019","journal-title":"Clin. Oral Implant. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"576","DOI":"10.11607\/jomi.9852","article-title":"Machine Learning and Artificial Intelligence: A Web-Based Implant Failure and Peri-Implantitis Prediction Model for Clinicians","volume":"38","author":"Rekawek","year":"2023","journal-title":"Int. J. Oral Maxillofac. Implant."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mameno, T., Wada, M., Nozaki, K., Takahashi, T., Tsujioka, Y., Akema, S., Hasegawa, D., and Ikebe, K. (2021). Predictive Modeling for Peri-Implantitis by Using Machine Learning Techniques. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-90642-4"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"122","DOI":"10.4103\/jomfp.JOMFP_304_18","article-title":"Oral Microbiome: Unveiling the Fundamentals","volume":"23","author":"Deo","year":"2019","journal-title":"J. Oral Maxillofac. Pathol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bessa, L.J., Botelho, J., Machado, V., Alves, R., and Mendes, J.J. (2022). Managing Oral Health in the Context of Antimicrobial Resistance. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph192416448"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1038\/sj.bdj.2016.865","article-title":"The Oral Microbiome\u2014An Update for Oral Healthcare Professionals","volume":"221","author":"Kilian","year":"2016","journal-title":"Br. Dent. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"168S","DOI":"10.1177\/0022034513504950","article-title":"Patient-Specific Analysis of Periodontal and Peri-Implant Microbiomes","volume":"92","author":"Dabdoub","year":"2013","journal-title":"J. Dent. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Komatsu, K., Shiba, T., Takeuchi, Y., Watanabe, T., Koyanagi, T., Nemoto, T., Shimogishi, M., Shibasaki, M., Katagiri, S., and Kasugai, S. (2020). Discriminating Microbial Community Structure Between Peri-Implantitis and Periodontitis With Integrated Metagenomic, Metatranscriptomic, and Network Analysis. Front. Cell. Infect. Microbiol., 10.","DOI":"10.3389\/fcimb.2020.596490"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1111\/prd.12372","article-title":"Peri-Implantitis Is Not Periodontitis: Scientific Discoveries Shed Light on Microbiome-Biomaterial Interactions That May Determine Disease Phenotype","volume":"86","author":"Kotsakis","year":"2021","journal-title":"Periodontol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1111\/clr.13459","article-title":"Intra-Oral Single-Site Comparisons of Periodontal and Peri-Implant Microbiota in Health and Disease","volume":"30","author":"Yu","year":"2019","journal-title":"Clin. Oral Implant. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Giok, K.C., and Menon, R.K. (2023). The Microbiome of Peri-Implantitis: A Systematic Review of Next-Generation Sequencing Studies. Antibiotics, 12.","DOI":"10.3390\/antibiotics12111610"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Song, L., Feng, Z., Zhou, Q., Wu, X., Zhang, L., Sun, Y., Li, R., Chen, H., Yang, F., and Yu, Y. (2024). Metagenomic Analysis of Healthy and Diseased Peri-Implant Microbiome under Different Periodontal Conditions: A Cross-Sectional Study. BMC Oral Health, 24.","DOI":"10.1186\/s12903-023-03442-9"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.identj.2023.11.005","article-title":"Oral Fluid Biomarkers for Peri-Implantitis: A Scoping Review","volume":"74","author":"Lumbikananda","year":"2024","journal-title":"Int. Dent. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Roman-Naranjo, P., Parra-Perez, A.M., and Lopez-Escamez, J.A. (2023). A Systematic Review on Machine Learning Approaches in the Diagnosis and Prognosis of Rare Genetic Diseases. J. Biomed. Inform., 143.","DOI":"10.1016\/j.jbi.2023.104429"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3467477","article-title":"Evolutionary Machine Learning: A Survey","volume":"54","author":"Telikani","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pais, R.J. (2022). Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups. BioTech, 11.","DOI":"10.3390\/biotech11030035"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ghensi, P., Manghi, P., Zolfo, M., Armanini, F., Pasolli, E., Bolzan, M., Bertelle, A., Dell\u2019Acqua, F., Dellasega, E., and Waldner, R. (2020). Strong Oral Plaque Microbiome Signatures for Dental Implant Diseases Identified by Strain-Resolution Metagenomics. npj Biofilms Microbiomes, 6.","DOI":"10.1038\/s41522-020-00155-7"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1093\/bioinformatics\/btz470","article-title":"Scaling Tree-Based Automated Machine Learning to Biomedical Big Data with a Feature Set Selector","volume":"36","author":"Le","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.3390\/biomedinformatics3040064","article-title":"Facilitating \u201cOmics\u201d for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics","volume":"3","author":"Filho","year":"2023","journal-title":"BioMedInformatics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1093\/bioinformatics\/btu170","article-title":"Trimmomatic: A Flexible Trimmer for Illumina Sequence Data","volume":"30","author":"Bolger","year":"2014","journal-title":"Bioinformatics"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Langmead, B., Trapnell, C., Pop, M., and Salzberg, S.L. (2009). Ultrafast and Memory-Efficient Alignment of Short DNA Sequences to the Human Genome. Genome Biol., 10.","DOI":"10.1186\/gb-2009-10-3-r25"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1101\/gr.213959.116","article-title":"MetaSPAdes: A New Versatile Metagenomic Assembler","volume":"27","author":"Nurk","year":"2017","journal-title":"Genome Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1038\/s41587-023-01688-w","article-title":"Extending and Improving Metagenomic Taxonomic Profiling with Uncharacterized Species Using MetaPhlAn 4","volume":"41","author":"Beghini","year":"2023","journal-title":"Nat. Biotechnol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wood, D.E., Lu, J., and Langmead, B. (2019). Improved Metagenomic Analysis with Kraken 2. Genome Biol., 20.","DOI":"10.1186\/s13059-019-1891-0"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.7717\/peerj-cs.104","article-title":"Bracken: Estimating Species Abundance in Metagenomics Data","volume":"3","author":"Lu","year":"2017","journal-title":"PeerJ Comput. Sci."},{"key":"ref_32","first-page":"123","article-title":"Automating Biomedical Data Science Through Tree-Based Pipeline Optimization","volume":"Volume 9597","author":"Olson","year":"2016","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pais, R.J., Lopes, F., Parreira, I., Silva, M., Silva, M., and Moutinho, M.G. (2023). Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach. Med. Sci. Forum, 22.","DOI":"10.3390\/msf2023022006"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pais, R.J., Zmuidinaite, R., Lacey, J.C., Jardine, C.S., and Iles, R.K. (2022). A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum. Appl. Sci., 12.","DOI":"10.3390\/app12063030"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1007\/s10815-020-01890-8","article-title":"Bioinformatic Identification of Euploid and Aneuploid Embryo Secretome Signatures in IVF Culture Media Based on MALDI-ToF Mass Spectrometry","volume":"37","author":"Pais","year":"2020","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dankers, F.J.W.M., Traverso, A., Wee, L., and van Kuijk, S.M.J. (2019). Prediction Modeling Methodology. Fundamentals of Clinical Data Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-99713-1_15"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Feher, B., Tussie, C., and Giannobile, W. (2024). V Applied Artificial Intelligence in Dentistry: Emerging Data Modalities and Modeling Approaches. Front. Artif. Intell., 7.","DOI":"10.3389\/frai.2024.1427517"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/j.cels.2021.06.006","article-title":"Artificial Intelligence for Proteomics and Biomarker Discovery","volume":"12","author":"Mann","year":"2021","journal-title":"Cell Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lof, M., Janus, M.M., and Krom, B.P. (2017). Metabolic Interactions between Bacteria and Fungi in Commensal Oral Biofilms. J. Fungi, 3.","DOI":"10.3390\/jof3030040"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1099\/acmi.0.000575.v4","article-title":"Rhino-Orbital Cerebral Mycosis: A Case Series of Non-Mucorales in COVID Patients","volume":"5","author":"Patil","year":"2023","journal-title":"Access Microbiol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"K\u00f6n\u00f6nen, E., Fteita, D., Gursoy, U.K., and Gursoy, M. (2022). Prevotella Species as Oral Residents and Infectious Agents with Potential Impact on Systemic Conditions. J. Oral Microbiol., 14.","DOI":"10.1080\/20002297.2022.2079814"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8","DOI":"10.5357\/koubyou.91.1_8","article-title":"Characterizing Bacterial Communities among Healthy, Peri-Implant Mucositis, and Peri-Implantitis Statuses by 16S RRNA Gene Amplicon Sequencing","volume":"91","author":"Takahiko","year":"2024","journal-title":"J. Stomatol. Soc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s00784-014-1250-1","article-title":"Epstein\u2013Barr Virus Associated Peri-Implantitis: A Split-Mouth Study","volume":"19","author":"Verdugo","year":"2015","journal-title":"Clin. Oral Investig."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Koyanagi, T., Sakamoto, M., Takeuchi, Y., Ohkuma, M., and Izumi, Y. (2010). Analysis of Microbiota Associated with Peri-Implantitis Using 16S RRNA Gene Clone Library. J. Oral Microbiol., 2.","DOI":"10.3402\/jom.v2i0.5104"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/4\/425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:30:14Z","timestamp":1760027414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/4\/425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["diagnostics15040425"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15040425","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]}}}