Nur Aqsha, Yumnanisha Defin, Tjandra Sydney, Bachtiar Adang, Harbuwono Dante Saksono
Faculty of Public Health, Universitas Indonesia, Depok, Indonesia.
Acta Med Indones. 2024 Oct;56(4):563-570.
The burden of undiagnosed diabetes mellitus (DM) is substantial, with approximately 240 million individuals globally unaware of their condition, disproportionately affecting low- and middle-income countries (LMICs), including Indonesia. Without screening, DM and its complications will impose significant pressure on healthcare systems. Current clinical practices for screening and diagnosing DM primarily involve blood or laboratory-based testing which possess limitations on access and cost. To address these challenges, researchers have developed risk-scoring tools to identify high-risk populations. However, considering generalizability, artificial intelligence (AI) technologies offer a promising approach, leveraging diverse data sources for improved accuracy. AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. This article underscores the potential of AI-driven approaches in reducing the burden of DM through accurate prediction of undiagnosed diabetes while highlighting the need for continued innovation and collaboration in healthcare delivery.
未诊断糖尿病（DM）的负担十分沉重，全球约有2.4亿人未意识到自己患有这种疾病，这对包括印度尼西亚在内的低收入和中等收入国家（LMICs）造成了不成比例的影响。若不进行筛查，糖尿病及其并发症将给医疗系统带来巨大压力。目前筛查和诊断糖尿病的临床实践主要涉及血液或基于实验室的检测，这些检测在可及性和成本方面存在局限性。为应对这些挑战，研究人员开发了风险评分工具来识别高危人群。然而，考虑到通用性，人工智能（AI）技术提供了一种很有前景的方法，它利用各种数据源来提高准确性。人工智能模型（即机器学习和深度学习）在各种疾病中的预测准确率高达98%。本文强调了人工智能驱动的方法在通过准确预测未诊断糖尿病来减轻糖尿病负担方面的潜力，同时突出了在医疗服务中持续创新与合作的必要性。
