Wu Yanchan, Yu Tao, Zhang Meijia, Li Yichen, Wang Yijun, Yang Dongren, Yang Yun, Lou Hao, Ren Chufan, Cai Enna, Dai Chenyue, Sun Ruidian, Xu Qiang, Zhao Qi, Zhang Huanhuan, Liu Jiefan
Department of Oral Maxillofacial Surgery, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, P.R. China.
School of Electrical and Information Engineering, Quzhou University, Quzhou, 324000, P.R. China.
J Transl Med. 2024 Dec 20;22(1):1123. doi: 10.1186/s12967-024-05958-2.
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.
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.
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.
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.
随着牙科疾病负担的增加以及医疗资源的有限可用性，传统的分诊方法在有效利用医疗资源和满足患者需求方面存在不足。本研究的目的是开发一种先进的分诊系统，该系统结合口腔放射组学和生物多组学数据，通过自动解读口腔X光图像中的生物信息，实现患者的准确科室转诊。
我们使用多标签学习算法，分析了2023年7月1日至2023年8月18日期间来自三个队列的3942例口腔疾病患者的多组学数据，并持续监测分类准确率（ACC）指标。
在测试队列和外部验证队列中，我们使用DenseNet121模型分析多组学数据，分类准确率分别达到0.80和0.82。
本研究的主要贡献在于提出了一种纳入生物多组学数据的新治疗流程，该流程在为患者提供及时准确医疗服务的同时，减轻了医生的工作量。通过对比实验，我们证明该流程比现有流程更高效。此外，这种智能分诊系统在实际应用中表现出较高的预测准确率，为生物多组学研究提供了新的思路和方法。
