Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.

BACKGROUND

With the increasing burden of dental diseases and the limited availability of healthcare resources, traditional triage methods are inadequate in efficiently utilizing healthcare resources and meeting patient needs. The aim of this study is to develop an advanced triage system that combines oral radiomics and biological multi-omics data, which enables accurate departmental referral of patients by automatically interpreting biological information in oral X-ray images.

METHODS

Using a multi-label learning algorithm, we analyzed multi-omics data from 3,942 patients with oral diseases from three cohorts between July 1, 2023 and August 18, 2023, and continuously monitored classification accuracy (ACC) metrics.

RESULTS

In the test cohort and external validation cohort, we used the DenseNet121 model to analyze the multi-omics data and achieved classification accuracies of 0.80 and 0.82, respectively.

CONCLUSIONS

The main contribution of this study is to propose a new treatment process that incorporates biological multi-omics data, which reduces the workload of physicians while providing timely and accurate medical care to patients. Through comparative experiments, we demonstrate that the process is more efficient than existing processes. In addition, this intelligent triage system demonstrates high prediction accuracy in practical applications, providing new ideas and methods for biological multi-omics research.