Dholariya Sagar, Dutta Siddhartha, Sonagra Amit, Kaliya Mehul, Singh Ragini, Parchwani Deepak, Motiani Anita
Department of Biochemistry, All India Institute of Medical Sciences, Rajkot, India.
Department of Pharmacology, All India Institute of Medical Sciences, Rajkot, India.
Curr Med Res Opin. 2024 Dec;40(12):2025-2055. doi: 10.1080/03007995.2024.2423737. Epub 2024 Nov 13.
The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In addition, we compared the performance of non-logistic regression (LR) machine learning (ML) models to conventional LR prediction models.
Until January 30, 2024, a comprehensive literature review was conducted by investigating databases such as Medline ( PubMed) and Cochrane. Research that is inclusive of AI or ML models for the prediction, diagnosis, and progression of DKD was incorporated. The area under the Receiver Operating Characteristic Curve (AUROC) served as the principal outcome metric for assessing model performance. A meta-analysis was performed utilizing MedCalc statistical software to calculate pooled AUROC and assess the performance differences between LR and non-LR models.
A total of 57 studies were included in the meta-analysis. The pooled AUROC of AI or ML model was 0.84 (95% CI = 0.81-0.86, < 0.0001) for analyzing prediction of DKD, 0.88 (95%CI = 0.84-0.92, < 0.0001) for detecting diagnostic biomarkers, and 0.80 (95% CI = 0.77-0.82, < 0.0001) for analyzing progression of DKD. The pooled AUROC of LR and non-LR ML models exhibited no significant differences across all categories ( > 0.05), except for the random forest (RF) model, which displayed a statistically significant increase in predictive accuracy compared to LR for DKD occurrence ( < 0.04).
ML models showed solid DKD prediction effectiveness, with pooled AUROC values over 0.8, suggesting good performance. These data demonstrated that non-LR and LR models perform similarly in overall CKD management, but the RF model outperforms the LR model, particularly in predicting the occurrence of DKD. These findings highlight the promise of AI technologies for better DKD management. To improve model reliability, future study should include extended follow-up periods as well as external validation.
本研究旨在系统调查人工智能（AI）模型在预测、检测糖尿病肾病（DKD）诊断生物标志物及疾病进展方面的潜力。此外，我们还比较了非逻辑回归（LR）机器学习（ML）模型与传统LR预测模型的性能。
截至2024年1月30日，通过调查Medline（PubMed）和Cochrane等数据库进行了全面的文献综述。纳入了包含用于DKD预测、诊断和疾病进展的AI或ML模型的研究。受试者操作特征曲线下面积（AUROC）作为评估模型性能的主要结果指标。利用MedCalc统计软件进行荟萃分析，以计算合并AUROC并评估LR模型和非LR模型之间的性能差异。
荟萃分析共纳入57项研究。AI或ML模型分析DKD预测的合并AUROC为0.84（95%CI = 0.81 - 0.86，P < 0.0001），检测诊断生物标志物的合并AUROC为0.88（95%CI = 0.84 - 0.92，P < 0.0001），分析DKD进展的合并AUROC为0.80（95%CI = 0.77 - 0.82，P < 0.0001）。除随机森林（RF）模型外，LR和非LR ML模型的合并AUROC在所有类别中均无显著差异（P > 0.05），与LR相比，RF模型在预测DKD发生方面显示出统计学上显著提高的预测准确性（P < 0.04）。
ML模型显示出可靠的DKD预测效果，合并AUROC值超过0.8，表明性能良好。这些数据表明，在慢性肾脏病（CKD）的整体管理中，非LR模型和LR模型表现相似，但RF模型优于LR模型，尤其是在预测DKD的发生方面。这些发现凸显了AI技术在改善DKD管理方面的前景。为提高模型可靠性，未来研究应包括延长随访期以及外部验证。
