Value Analysis of Using Urinary Microalbumin in Artificial Intelligence Medical Institutions to Detect Early Renal Damage in Diabetes

Abstract

As the scale and depth of artificial intelligence network models continue to increase, their accuracy in albumin recognition tasks has increased rapidly. However, today’s small medical datasets are the main reason for the poor recognition of artificial intelligence techniques in this area. The sample size in this article is based on the data analysis and research on urine albumin detection of diabetes in the EI database. It is assumed that the observation group has at least 20 mg UAER difference from the control group, and the standard deviation of the UAER change from baseline to 12 weeks is 30 mg. Therefore, the sample size of the two groups is 77 cases. Assuming that the rate of loss to follow-up during the follow-up period is 20%, at least 92 patients are needed. The final enrollment in this study is 100 patients. Studies have shown that DR is used as an indicator to diagnose NDRD, and its OR value is as high as 28.198, indicating that non-DR can be used as an indicator to distinguish DN from NDRD. The meta-analysis found that DR has a sensitivity of 0.65 and a specificity of 0.75 in distinguishing DN from NDRD in patients with type 2 diabetes, and it is emphasized that PDR is highly specific in the diagnosis of DN. Using a meta-analysis to systematically analyze 45 studies, it was found that the sensitivity of DR to diagnose DN was 0.67, the specificity was 0.78, and the specificity of PDR to predict DN was 0.99, indicating that DR is a good indicator for predicting DN, and the team’s latest research has also verified this point of view. They have established a new model for diagnosing DN. In addition to including traditional proteinuria, glycosylated hemoglobin, FR, blood pressure, and other indicators into the diagnostic model, it will also include the presence or absence of DR. The final external verification accuracy rate of this model is 0.875.

Yishen Capsule Alleviated Symptoms of Diabetic Nephropathy via NOD-like Receptor Signaling Pathway

“…9 Urine microalbumin quantification can detect the albumin level in urine, which can reflect the early damage to DN. 26 In this study, Yishen capsule could reduce the level of urinary microalbumin in DN rats and improve the pathological changes in kidney tissue. The renal cortex tissues were further selected for transcriptome sequencing study.…”

Artificial Intelligence for the Diagnosis and Treatment of Diabetes Kidney Disease: a systematic review

“…The details of the selected articles are presented in Table -3 The scheme of this study is summarized into four different categories based on the AI framework in use. The categories include 1) predictive AI model for the early detection of DN (40,44,45,46,48,51,54,55,59,61), 2) AI Framework for the diagnosis of DN (42,50,57), 3) predictive model for the progression of DKD (39,41,49,58,62), 4) the management of DN for existing patients OR early management indication to DM patients without DKD with the help of AI (43,47,52,53,56,60). The studies included in this systematic review are from various parts of the world.…”

“…Meanwhile, 7 studies(41,50,51,52,54,55,57) included a cohort size/ sample size over 10,000 patients/sample size. In addition, a large sum of 14 studies(39,40,42,44,48,49,51,52,53,55,56,57,59,62) used ML to build the predictive model. On the other hand, 5 studies(47,50,54,58,60) used DL algorithms as the AI method in their study.…”

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

“…Advancements in proteomics technology such as protein separation, biological mass spectrometry, and bioinformatics have decreased the difficulty of examining proteome expression (8). Despite these advancements, there are still many drawbacks in the detection of urine albumin (8).…”

“…Advancements in proteomics technology such as protein separation, biological mass spectrometry, and bioinformatics have decreased the difficulty of examining proteome expression (8). Despite these advancements, there are still many drawbacks in the detection of urine albumin (8). The gold standard in chronic kidney disease (CKD) screening is the 24-h urine collection test; however, this method is difficult to implement on a large scale due to its inconvenience (2).…”