ArticleScientific reports2020
Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Diagnostic Accuracy of Deep Learning Models in Detecting Peri-Implant Marginal Bone Loss: A Systematic Review and Meta-Analysis.Clinical oral implants research · 2026Pooled it
- Prediction models for the complication incidence and survival rate of dental implants-a systematic review and critical appraisal.International journal of implant dentistry · 2025Pooled it
- The Use of Artificial Intelligence in Planning Dental Implant Procedures: A Systematic Review.Dentistry journal · 2026Review
- Accuracy of Artificial Intelligence Models in Detecting Peri-Implant Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Decoding core genes and intercellular communication in osteosarcoma: bioinformatic investigation and immune cell profiling for diagnostic and therapeutic insights.Discover oncology · 2024Article
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature.Journal of dentistry (Shiraz, Iran) · 2023Review
- Deep learning based dental implant failure prediction from periapical and panoramic films.Quantitative imaging in medicine and surgery · 2023Article
- Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review.BMC musculoskeletal disorders · 2023Article
- Review
- Photocrosslinkable Col/PCL/Mg composite membrane providing spatiotemporal maintenance and positive osteogenetic effects during guided bone regeneration.Bioactive materials · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Marginal bone loss (MBL) is one of the leading causes of dental implant failure. This study aimed to investigate the feasibility of machine learning (ML) algorithms based on trabeculae microstructure parameters to predict the occurrence of severe MBL. Eighty-one patients (41 severe MBL cases and 40 normal controls) were involved in the current study. Four ML models, including support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), and random forest (RF), were employed to predict severe MBL. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity were used to evaluate the performance of these models. At the early stage of functional loading, severe MBL cases showed a significant increase of structure model index and trabecular pattern factor in peri-implant alveolar bone. The SVM model exhibited the best outcome in predicting MBL (AUC = 0.967, sensitivity = 91.67%, specificity = 100.00%), followed by ANN (AUC = 0.928, sensitivity = 91.67%, specificity = 93.33%), LR (AUC = 0.906, sensitivity = 91.67%, specificity = 93.33%), RF (AUC = 0.842, sensitivity = 75.00%, specificity = 86.67%). Together, ML algorithms based on the morphological variation of trabecular bone can be used to predict severe MBL.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.