Temporal Deep Learning for Predicting Periodontitis Progression Using Longitudinal Gingival Crevicular Fluid Protein Profiles

BackgroundConventional clinical indicators of periodontitis progression detect disease after irreversible tissue destruction has occurred. Molecular biomarkers in gingival crevicular fluid (GCF) offer potential for earlier detection, but existing analytical approaches rely on cross-sectional snapshots that fail to capture the temporal dynamics of disease evolution. AimTo develop and validate a temporal deep learning framework leveraging longitudinal GCF protein profiles for (1) regression-based prediction of clinical attachment level (CAL) and probing depth (PD) changes, (2) current-visit classification of periodontitis progression, (3) next-visit prediction of progression with a 2-month clinical lead time, and (4) identification of the most informative biomarkers through systematic multi-method feature importance analysis. Materials and MethodsThis study utilized longitudinal GCF data from a prospective cohort of 413 participants (501 periodontal sites, 3,792 time-series observations) with 64 protein biomarkers measured at 2-month intervals over 12 months. A compact encoder-gated recurrent unit (GRU)-decoder architecture was developed through systematic experimentation across four phases, benchmarking temporal deep learning against cross-sectional machine learning baselines. Task-specific decoders addressed continuous regression (CAL and PD prediction) and binary classification (progression detection). Model development and reporting followed the TRIPOD+AI guidelines. ResultsThe temporal GRU achieved 47.7% CAL mean absolute error (MAE) reduction (1.139 to 0.596 mm) and 41.0% PD MAE reduction (0.902 to 0.532 mm) over linear regression baselines through the systematic model development progression. For binary classification, the model achieved AUC-ROC of 0.886 for current-visit classification and 0.867 for next-visit prediction with a 2-month lead time. Per-visit analysis revealed progressive improvement in both regression and classification accuracy as longitudinal data accumulated. Cross-method feature importance analysis identified Periostin, VEGF, MMP-2, IL-1RA, and MCP-4 as core predictive biomarkers, with divergent profiles between diagnostic and prognostic tasks suggesting distinct molecular signatures for concurrent versus incipient progression. ConclusionsTemporal deep learning applied to longitudinal GCF protein profiles enables both accurate regression prediction of clinical parameters and reliable classification of progression status, including 2-month-ahead forecasting suitable for clinical intervention planning. The compact architecture and non-invasive sampling approach make this framework suitable for integration into point-of-care periodontal monitoring workflows. Clinical RelevanceConventional clinical indicators of periodontitis progression, including probing depth changes, attachment loss, and radiographic bone loss, inherently detect disease after irreversible damage has occurred. This study shows that a compact deep learning model analyzing temporal GCF protein profiles can first accurately predict continuous changes in pocket depth and attachment loss, then classify progression status 2 months in advance, enabling proactive intervention before clinical manifestation of tissue destruction.