Németh Ákos, Tóth Gábor, Fülöp Péter, Paragh György, Nádró Bíborka, Karányi Zsolt, Paragh György, Horváth Zsolt, Csernák Zsolt, Pintér Erzsébet, Sándor Dániel, Bagyó Gábor, Édes István, Kappelmayer János, Harangi Mariann, Daróczy Bálint
Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Aesculab Medical Solutions, Black Horse Group Ltd., Debrecen, Hungary.
Front Digit Health. 2024 Dec 5;6:1505483. doi: 10.3389/fdgth.2024.1505483. eCollection 2024.
The integration of AI into healthcare is widely anticipated to revolutionize medical diagnostics, enabling earlier, more accurate disease detection and personalized care.
In this study, we developed and validated an AI-assisted diagnostic support tool using only routinely ordered and broadly available blood tests to predict the presence of major chronic and acute diseases as well as rare disorders.
Our model was tested on both retrospective and prospective datasets comprising over one million patients. We evaluated the diagnostic performance by (1) implementing ensemble learning (mean ROC-AUC.9293 and mean DOR 63.96); (2) assessing the model's sensitivity via risk scores to simulate its screening effectiveness; (3) analyzing the potential for early disease detection (30-270 days before clinical diagnosis) through creating historical patient timelines and (4) conducting validation on real-world clinical data in collaboration with Synlab Hungary, to assess the tool's performance in clinical setting.
Uniquely, our model not only considers stable blood values but also tracks changes from baseline across 15 years of patient history. Our AI-driven automated diagnostic tool can significantly enhance clinical practice by recognizing patterns in common and rare diseases, including malignancies. The models' ability to detect diseases 1-9 months earlier than traditional clinical diagnosis could contribute to reduced healthcare costs and improved patient outcomes. The automated evaluation also reduces evaluation time of healthcare providers, which accelerates diagnostic processes. By utilizing only routine blood tests and ensemble methods, the tool demonstrates high efficacy across independent laboratories and hospitals, making it an exceptionally valuable screening resource for primary care physicians.
人们广泛预期人工智能融入医疗保健将彻底改变医学诊断，实现更早、更准确的疾病检测和个性化护理。
在本研究中，我们开发并验证了一种人工智能辅助诊断支持工具，该工具仅使用常规安排且广泛可用的血液检测来预测主要慢性和急性疾病以及罕见病症的存在。
我们的模型在包含超过100万患者的回顾性和前瞻性数据集上进行了测试。我们通过以下方式评估诊断性能：(1) 实施集成学习（平均ROC-AUC为0.9293，平均DOR为63.96）；(2) 通过风险评分评估模型的敏感性以模拟其筛查效果；(3) 通过创建患者历史时间线分析早期疾病检测（临床诊断前30 - 270天）的潜力；(4) 与匈牙利盛合联检公司合作对真实世界临床数据进行验证，以评估该工具在临床环境中的性能。
独特的是，我们的模型不仅考虑稳定的血液值，还跟踪患者15年病史中相对于基线的变化。我们的人工智能驱动的自动化诊断工具可以通过识别常见和罕见疾病（包括恶性肿瘤）的模式来显著增强临床实践。该模型比传统临床诊断提前1 - 9个月检测疾病的能力有助于降低医疗成本并改善患者预后。自动化评估还减少了医疗保健提供者的评估时间，从而加快了诊断过程。通过仅使用常规血液检测和集成方法，该工具在独立实验室和医院中均显示出高效能，使其成为初级保健医生非常有价值的筛查资源。
