To address the problem of data silos in dental specialties caused by equipment heterogeneity, this study developed an Intelligent Internet of Things (IoT)-enabled dental chair platform (hereinafter referred to as the intelligent platform) based on the concept of medical-engineering integration. The platform adopts a three-tier chair-domain interconnection architecture: the bottom tier integrates multi-source sensors and standardized interfaces for automated data acquisition and linkage with hospital information systems; the middle tier provides clinic-level management and remote teaching collaboration; and the top tier employs a blockchain-based secure cloud database for resource allocation and data management. Clinical validation at The Affiliated Stomatological Hospital of Nanjing Medical University demonstrated that, compared with a control group from the same period in 2023, the trial group achieved a 38.0% increase in average daily patient visits (80.6±6.8 vs. 58.4±5.2, t=15.16, P<0.001), a 24.6% reduction in average treatment time [(36.1±6.3) min vs. (47.9±8.5) min, t=7.72, P<0.001], a 39.2% reduction in waiting time [23.3 (16.5, 30.1) min vs. 38.3 (28.3, 48.3) min, U=32.00, P<0.001], a 30.4% reduction in equipment idle rate [8.7% (5.1%, 12.3%) vs. 12.5% (7.4%, 17.6%), U=251.00, P=0.003], and an increase in patient satisfaction from 88.2% (1 519/1 723) to 94.3% (2 186/2 318) ( t=7.26, P<0.001). User research confirmed that the functions most favored by clinicians and patients were "dental chair parameter updating and clinical data integration" [74.7% (80/107)] and "chairside display of diagnostic images" [76.8% (119/155)], respectively. Looking forward, the intelligent platform has the potential to integrate artificial intelligence-assisted diagnosis and 5G-enabled multicenter collaboration to further expand its clinical applications and accelerate the digital transformation of dental healthcare.

Objective To predict the invasion degree of subsolid nodule(SSN)lung adenocarcinoma using a combined model incorporating artificial intelligence(AI)quantitative parameters and imaging signs,and to validate the predictive efficacy of this model.Methods A total of 281 SSN lung adenocarcinoma CT images in 243 patients were retrospectively collected and randomly divided into training set(224 cases)and validation set(57 cases)in an 8∶2 ratio,with atypical adenomatous hyperplasia(A AH)+adenocarcinoma in situ(AIS)+minimally invasive adenocarcinoma(MIA)(191 cases)as the non-invasive adenocarcinoma(I AC)group and I AC(90 cases)as the IAC group.Multivariate logistic regression analysis was performed based on the AI quantitative parameters and CT signs in the training set to obtain independent predictors of IAC.A combined model and nomogram were then constructed and validated.The diagnostic efficacy and clinical applicability of the model were evaluated by the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA).Results Multivariate logistic regression analysis of the training set showed nodule type,spicule sign,vascular abnormality,long diameter＞11.5 mm,median CT value＞—426.25 HU,and mass＞391.5 mg were independent predictors of IAC(P＜0.05).The area under the curve(AUC)of the training set model,based on these independent predictive factors,was 0.915[95％confidence interval(CI)0.875-0.954],and the AUC of the validation set model was 0.903(95％CI 0.824-0.982),indicating both the training set and validation set models had high efficacy in distinguishing IAC.The nomogram model,which quantified these independent factors,demonstrated enhanced predictive power for IAC.The calibration curve indicated good fit of the prediction model,and the clinical DCA showed the model had good clinical applicability.Conclusion The model combining AI quantitative parameters and imaging signs has a higher ability to predict the risk of IAC,compared to a single indicator.It helps clinicians in determining the appropriate surgical timing,formulating surgical methods,and reducing overtreatment.

To address the problem of data silos in dental specialties caused by equipment heterogeneity, this study developed an Intelligent Internet of Things (IoT)-enabled dental chair platform (hereinafter referred to as the intelligent platform) based on the concept of medical-engineering integration. The platform adopts a three-tier chair-domain interconnection architecture: the bottom tier integrates multi-source sensors and standardized interfaces for automated data acquisition and linkage with hospital information systems; the middle tier provides clinic-level management and remote teaching collaboration; and the top tier employs a blockchain-based secure cloud database for resource allocation and data management. Clinical validation at The Affiliated Stomatological Hospital of Nanjing Medical University demonstrated that, compared with a control group from the same period in 2023, the trial group achieved a 38.0% increase in average daily patient visits (80.6±6.8 vs. 58.4±5.2, t=15.16, P<0.001), a 24.6% reduction in average treatment time [(36.1±6.3) min vs. (47.9±8.5) min, t=7.72, P<0.001], a 39.2% reduction in waiting time [23.3 (16.5, 30.1) min vs. 38.3 (28.3, 48.3) min, U=32.00, P<0.001], a 30.4% reduction in equipment idle rate [8.7% (5.1%, 12.3%) vs. 12.5% (7.4%, 17.6%), U=251.00, P=0.003], and an increase in patient satisfaction from 88.2% (1 519/1 723) to 94.3% (2 186/2 318) ( t=7.26, P<0.001). User research confirmed that the functions most favored by clinicians and patients were "dental chair parameter updating and clinical data integration" [74.7% (80/107)] and "chairside display of diagnostic images" [76.8% (119/155)], respectively. Looking forward, the intelligent platform has the potential to integrate artificial intelligence-assisted diagnosis and 5G-enabled multicenter collaboration to further expand its clinical applications and accelerate the digital transformation of dental healthcare.

Objective To predict the invasion degree of subsolid nodule(SSN)lung adenocarcinoma using a combined model incorporating artificial intelligence(AI)quantitative parameters and imaging signs,and to validate the predictive efficacy of this model.Methods A total of 281 SSN lung adenocarcinoma CT images in 243 patients were retrospectively collected and randomly divided into training set(224 cases)and validation set(57 cases)in an 8∶2 ratio,with atypical adenomatous hyperplasia(A AH)+adenocarcinoma in situ(AIS)+minimally invasive adenocarcinoma(MIA)(191 cases)as the non-invasive adenocarcinoma(I AC)group and I AC(90 cases)as the IAC group.Multivariate logistic regression analysis was performed based on the AI quantitative parameters and CT signs in the training set to obtain independent predictors of IAC.A combined model and nomogram were then constructed and validated.The diagnostic efficacy and clinical applicability of the model were evaluated by the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA).Results Multivariate logistic regression analysis of the training set showed nodule type,spicule sign,vascular abnormality,long diameter＞11.5 mm,median CT value＞—426.25 HU,and mass＞391.5 mg were independent predictors of IAC(P＜0.05).The area under the curve(AUC)of the training set model,based on these independent predictive factors,was 0.915[95％confidence interval(CI)0.875-0.954],and the AUC of the validation set model was 0.903(95％CI 0.824-0.982),indicating both the training set and validation set models had high efficacy in distinguishing IAC.The nomogram model,which quantified these independent factors,demonstrated enhanced predictive power for IAC.The calibration curve indicated good fit of the prediction model,and the clinical DCA showed the model had good clinical applicability.Conclusion The model combining AI quantitative parameters and imaging signs has a higher ability to predict the risk of IAC,compared to a single indicator.It helps clinicians in determining the appropriate surgical timing,formulating surgical methods,and reducing overtreatment.