{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:07:45Z","timestamp":1789103265500,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Predicting individual periodontal treatment response remains clinically unresolved. Existing machine-learning models are predominantly cross-sectional, lack a longitudinal response-vector design, and are rarely evaluated within validation-aware digital-twin frameworks that capture multidimensional credibility rather than single-endpoint discrimination alone. This study aimed to develop and evaluate PerioTwin-DeltaCRED, a validation-aware periodontal digital twin that models treatment response as a longitudinal PPD and CAL response vector, and to benchmark classical and quantum machine-learning engines against that framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Five hundred patients with Stage II-IV periodontitis were retrospectively assembled from the Saveetha DIAS system; treatment was observational, involving either scaling and root planing alone or with low-level laser therapy. A stratified 60:20:20 split (training n=300, validation n=100, test n=100) was used for all models, using only baseline predictors\u2014follow-up measurements and outcome variables were excluded to prevent leakage. Classical learners (logistic regression, random forest, gradient boosting) were benchmarked for poor-response classification, PPD reduction, CAL gain, and residual deep-pocket risk. Three quantum architectures\u2014VQC-Pauli, Data Re-Uploading VQC (DRU-VQC), and QSVC\u2014along with a hybrid quantum\u2013classical stack, were evaluated as exploratory engines; six principal components (83.6% of variance) were angle-encoded into six-qubit circuits. Hyperparameters were optimized by Optuna on the validation set, with the test set withheld until final assessment. Uncertainty was estimated by bootstrap resampling, and the Delta-PTFI summarized credibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Classical logistic regression was the strongest poor-response model (test AUROC 0.69, 95% CI 0.59\u20130.80; sensitivity 0.54, specificity 0.75), with random forest comparable (AUROC 0.68) and providing the best residual deep-pocket discrimination (AUROC 0.76). Continuous responses were predicted with modest error (PPD-reduction mean absolute error 0.37\u00a0mm; CAL-gain 0.31\u00a0mm); against observed changes of 1.20 \u00b1 0.46\u00a0mm and 0.78 \u00b1 0.36\u00a0mm, these errors were equivalent to an intercept-only baseline. Baseline CAL and periodontal stage were the dominant and most stable predictors (mean top-5 stability 0.75), and a two-variable model using stage and baseline CAL alone matched the full model (AUROC 0.71 versus 0.69). An exploratory composite credibility index (Delta-PTFI) was 0.62, falling to 0.25 when its continuous-outcome components were anchored to an intercept-only reference. No quantum architecture surpassed classical learning: the data re-uploading classifier (DRU-VQC) was the best quantum model (test AUROC 0.63), followed by the hybrid stack (0.60), VQC-Pauli (0.46), and QSVC (0.40). Classical learners restricted to the same six principal components reached AUROC 0.58\u20130.66, and the best of them (random forest, 0.66) still exceeded every quantum model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PerioTwin-DeltaCRED provides a validation-aware digital twin for periodontal treatment response, assessing calibration, fidelity, response stability, and credibility index instead of just discrimination. Classical models outperformed, and quantum architectures like DRU-VQC showed no advantage in noiseless six-qubit tests. This is among the first studies embedding and benchmarking quantum machine learning in a periodontal digital twin; more validation and noise testing is needed before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1958029","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:33:14Z","timestamp":1789101194000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PerioTwin-DeltaCRED: a validation-aware physiological digital twin for longitudinal periodontal treatment response prediction"],"prefix":"10.3389","volume":"17","author":[{"given":"Pradeep Kumar","family":"Yadalam","sequence":"first","affiliation":[{"name":"Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS)","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.3390\/bioengineering11010084","article-title":"Toward digital twin development for implant placement planning using a parametric reduced-order model","volume":"11","author":"Ahn","year":"2024","journal-title":"Bioengineering"},{"key":"B2","doi-asserted-by":"publisher","first-page":"7453","DOI":"10.1038\/s41598-024-55991-w","article-title":"Revolutionizing heart disease prediction with quantum-enhanced machine learning","volume":"14","author":"Babu","year":"2024","journal-title":"Sci. Rep."},{"key":"B3","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1080\/17460441.2025.2507376","article-title":"Generative AI and digital twins: shaping a paradigm shift from precision to truly personalized medicine","volume":"20","author":"Bordukova","year":"2025","journal-title":"Expert Opin. Drug Discov."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.3390\/jcm10071488","article-title":"Predictive periodontitis: the most promising salivary biomarkers for early diagnosis of periodontitis","volume":"10","author":"Cafiero","year":"2021","journal-title":"J. Clin. Med."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1016\/j.jds.2023.03.020","article-title":"Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence","volume":"18","author":"Chen","year":"2023","journal-title":"J. Dent Sci."},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.2196\/53542","article-title":"Digital representation of patients as medical digital twins: data-centric viewpoint","volume":"13","author":"Demuth","year":"2025","journal-title":"JMIR Med. Inform"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1002\/JPER.24-0737","article-title":"Machine learning-assisted prediction of clinical responses to periodontal treatment","volume":"96","author":"Feher","year":"2025","journal-title":"J. Periodontol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1007\/s10278-023-00886-x","article-title":"An explainable MRI-radiomic quantum neural network to differentiate between large brain metastases and high-grade glioma using quantum annealing for feature selection","volume":"36","author":"Felefly","year":"2023","journal-title":"J.\u00a0Digit Imaging"},{"key":"B9","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1038\/s41746-025-01597-z","article-title":"A systematic review of quantum machine learning for digital health","volume":"8","author":"Gupta","year":"2025","journal-title":"NPJ Digit Med."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.47164\/ijngc.v13i3.614","article-title":"Digital decision making in dentistry: analysis and prediction of periodontitis using machine learning approach","volume":"13","author":"J","year":"2022","journal-title":"Int. J. Next-Generation Computing"},{"key":"B11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1093\/dmfr\/twae070","article-title":"Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis","volume":"54","author":"Khubrani","year":"2025","journal-title":"Dentomaxillofac Radiol."},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2020.571515","article-title":"Prediction of chronic periodontitis severity using machine learning models based on salivary bacterial copy number","volume":"10","author":"Kim","year":"2020","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"B13","doi-asserted-by":"publisher","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","article-title":"Deep learning for the radiographic detection of periodontal bone loss","volume":"9","author":"Krois","year":"2019","journal-title":"Sci. Rep."},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.5051\/jpis.2018.48.2.114","article-title":"Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm","volume":"48","author":"Lee","year":"2018","journal-title":"J. Periodontal Implant Sci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"10603","DOI":"10.1038\/s41598-023-37774-x","article-title":"Effectiveness of creating digital twins with different digital dentition models and cone-beam computed tomography","volume":"13","author":"Lee","year":"2023","journal-title":"Sci. Rep."},{"key":"B16","doi-asserted-by":"publisher","first-page":"3288","DOI":"10.1038\/s41598-023-29495-y","article-title":"Variational quantum approximate support vector machine with inference transfer","volume":"13","author":"Park","year":"2023","journal-title":"Sci. Rep."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105170","article-title":"Machine learning techniques for periodontitis and dental caries detection: a narrative review","volume":"178","author":"Radha","year":"2023","journal-title":"Int. J. Med. Inform"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1111\/joor.14045","article-title":"Applications of artificial intelligence (AI) for diagnosis of periodontal\/peri-implant diseases: a narrative review","volume":"52","author":"Roy","year":"2025","journal-title":"J. Oral. Rehabil"},{"key":"B19","doi-asserted-by":"publisher","first-page":"100864","DOI":"10.1016\/j.landig.2025.02.004","article-title":"Medical digital twins: enabling precision medicine and medical artificial intelligence","volume":"7","author":"Sad\u00e9e","year":"2025","journal-title":"Lancet Digit Health"},{"key":"B20","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/jcpe.13290","article-title":"Treatment of stage I-III periodontitis-the EFP S3 level clinical practice guideline","volume":"47","author":"Sanz","year":"2020","journal-title":"J. Clin. Periodontol."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3389\/froh.2025.1561128","article-title":"Implications of artificial intelligence in periodontal treatment maintenance: a scoping review","volume":"6","author":"Sarakbi","year":"2025","journal-title":"Front. Oral. Health"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.20944\/preprints202506.1518.v1","article-title":"Application of explainable AI (XAI) in periodontal disease risk assessment","author":"Sheza Abdul Subhan","year":"2025"},{"key":"B23","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1038\/s41746-025-01828-3","article-title":"Predictive modeling for step II therapy response in periodontitis - model development and validation","volume":"8","author":"Walter","year":"2025","journal-title":"NPJ Digit Med."},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2024.105373","article-title":"Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph","volume":"150","author":"Xue","year":"2024","journal-title":"J. Dent."},{"key":"B25","doi-asserted-by":"publisher","first-page":"287","DOI":"10.3390\/e25020287","article-title":"Quantum machine learning: a review and case studies","volume":"25","author":"Zeguendry","year":"2023","journal-title":"Entropy (Basel)"}],"container-title":["Frontiers in Physiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2026.1958029\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:33:16Z","timestamp":1789101196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2026.1958029\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":25,"alternative-id":["10.3389\/fphys.2026.1958029"],"URL":"https:\/\/doi.org\/10.3389\/fphys.2026.1958029","relation":{},"ISSN":["1664-042X"],"issn-type":[{"value":"1664-042X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]},"article-number":"1958029"}}