ArticleOpen life sciences2023
Early diagnosis for the onset of peri-implantitis based on artificial neural network.
Article in Open life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Impact of OMICS Technologies in Our Understanding of the Pathogenesis of Peri-Implantitis.Clinical and experimental dental research · 2026Review
- Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.Annals of biomedical engineering · 2024Review
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach.Frontiers in oral health · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study is to construct an artificial neural network (ANN) based on bioinformatic analysis to enable early diagnosis of peri-implantitis (PI). PI-related datasets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and functional enrichment analyses were performed between PI and the control group. Furthermore, the infiltration of 22 immune cells in PI was analyzed using CIBERSORT. Hub genes were identified with random forest (RF) classification. The ANN model was then constructed for early diagnosis of PI. A total of 1,380 DEGs were identified. Enrichment analysis revealed the involvement of neutrophil-mediated immunity and the NF-kappa B signaling pathway in PI. Additionally, higher proportion of naive B cells, activated memory CD4 T cells, activated NK cells, M0 macrophages, M1 macrophages, and neutrophils were observed in the soft tissues surrounding PI. From the RF analysis, 13 hub genes (ST6GALNAC4, MTMR11, SKAP2, AKR1B1, PTGS2, CHP2, CPEB2, SYT17, GRIP1, IL10, RAB8B, ABHD5, and IGSF6) were selected. Subsequently, the ANN model for early diagnosis of PI was constructed with high performance. We identified 13 hub genes and developed an ANN model that accurately enables early diagnosis of PI.
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Registered trials
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