{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T23:25:25Z","timestamp":1789255525865,"version":"build-2803163510"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study aims to develop and validate a deep learning model based on Cone Beam Computed Tomography (CBCT) radiomic features to achieve early detection of potential carotid atherosclerosis in periodontitis patients. The study utilised data from 279 observations, each with 206 features, to distinguish between periodontitis patients with and without concomitant carotid atherosclerosis. To address class imbalance, Synthetic Minority Over-sampling Technique(SMOTE) oversampling was applied (dup_size\u2009=\u20091), increasing the sample size to 390 observations. A bootstrap method (n_bootstrap\u2009=\u20091000) was employed for feature selection. In each iteration, a dataset was created by resampling with replacement. Features were first filtered using Spearman\u2019s rank correlation to remove redundant variables (correlation coefficient\u2009&gt;\u20090.8), followed by Lasso regression with ten-fold cross-validation to select predictive variables based on non-zero coefficients. High-frequency features identified through 1000 iterations underwent a second round of bootstrap analysis, where Logistic Regression combined with the Akaike Information Criterion (AIC) was used to determine the final variable set. This rigorous process ensured optimal feature selection for developing an effective early detection model for carotid atherosclerosis in periodontitis patients. The study analyzed data from 279 observations, with each observation characterized by 206 features, to differentiate between periodontitis patients with concurrent carotid atherosclerosis and those without. After SMOTE oversampling, the dataset was increased to 390 observations. As stated in the Methods, SMOTE was applied after baseline analysis to augment the dataset for model development. Feature selection through bootstrap methods identified 26 high-frequency features (&gt;\u2009500 times), which were further refined to a final set of 20 features using Logistic Regression combined with AIC. Three machine learning models\u2014Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF)\u2014were developed and evaluated using five-fold cross-validation. The best-performing model was the RF model, achieving an Area Under the Curve(AUC) of 0.892, sensitivity of 0.957, specificity of 0.710, and accuracy of 0.859. Receiver Operating Characteristic(ROC) curves and calibration plots demonstrated good predictive performance and model calibration across all three models. Decision curve analysis showed that the RF model provided the highest net benefit across a range of risk thresholds, indicating its potential for clinical utility in early detection of carotid atherosclerosis in periodontitis patients. This study developed a random forest model using CBCT radiomics to detect carotid atherosclerosis in periodontitis patients early. After rigorous feature selection and five-fold cross-validation, it achieved an AUC of 0.892, with sensitivity of 0.957 and specificity of 0.710. The model shows high predictive performance and clinical utility, offering an effective tool for early detection.<\/jats:p>","DOI":"10.1038\/s41598-026-38463-1","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:45:19Z","timestamp":1770698719000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomic features and carotid stenosis in periodontitis a two stage bootstrap and multimodal machine learning study"],"prefix":"10.1038","volume":"16","author":[{"given":"Mengqiang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subinuer","family":"Maimaitiaili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoxun","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"38463_CR1","doi-asserted-by":"crossref","unstructured":"Kim, H. 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