Role of Artificial Intelligence in Kidney Disease

Yuan, Qiongjing; Zhang, Haixia; Deng, Tianci; Tang, Shumei; Yuan, Xiangning; Tang, Wenbin; Xie, Yanyun; Ge, Huipeng; Wang, Xiufen; Zhou, Qiaoling; Xiao, Xiangcheng

doi:10.7150/ijms.42078

Cited by 51 publications

(35 citation statements)

References 105 publications

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“…With the increasing sophistication of artificial intelligence, deep learning has been increasingly applied in various fields, including medicine [ 25 ]. The use of artificial intelligence for management of kidney disease has been recently proposed, and its potential has been well recognized by physicians [ 13 ]. Kuo et al [ 26 ] developed a deep learning model for predicting renal function by using kidney ultrasound images.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Kang¹,

Hsieh²,

Li³

et al. 2020

JMIR Med Inform

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Background Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning–based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning–based method using retinal images for detecting early renal function impairment has not yet been well studied. Objective This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. Methods This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate <90 mL/min/1.73 m2. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). Results In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A1c (HbA1c) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA1c levels of ≤6.5%, >6.5%, >7.5%, and >10%, respectively. Conclusions The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA1c levels.

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Section: Discussionmentioning

confidence: 99%

“…Scholars have recommended applying artificial intelligence to the management and prevention of kidney disease [ 13 ]. However, few studies have developed deep learning–based methods for detecting early renal function impairment from retinal images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Kang¹,

Hsieh²,

Li³

et al. 2020

JMIR Med Inform

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“…The interest is increasing rapidly in the application of risk score systems and artificial intelligence (AI)-based decision tree assisted technologies to monitor the patient safety [ 14 , 15 ]. In recent years, the accessibility of electronic medical records for big data in the healthcare and development of machine learning to AI has grown tremendously.…”

Section: Discussionmentioning

confidence: 99%

“…In light of this, a recent study integrates age, GDF15 biomarker, and clinical parameters as the ABC death risk score for clinical evaluation of patient safety and decision tree [ 12 ]. There has been increasing interest in identifying the novel death risk score model to effectively predict poor prognosis in CKD patients [ 13 , 14 , 15 ]. Emerging evidence has shown that the prognostic questionnaires of mortality in CKD and specifically in MHD patients serve as useful risk prediction tools for the early detection of clinical events.…”

Section: Introductionmentioning

confidence: 99%

A Growth Differentiation Factor 15-Based Risk Score Model to Predict Mortality in Hemodialysis Patients

et al. 2021

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Background: The risk of cardiovascular (CV) and fatal events remains extremely high in patients with maintenance hemodialysis (MHD), and the growth differentiation factor 15 (GDF15) has emerged as a valid risk stratification biomarker. We aimed to develop a GDF15-based risk score as a death prediction model for MHD patients. Methods: Age, biomarker levels, and clinical parameters were evaluated at study entry. One hundred and seventy patients with complete information were finally included for data analysis. We performed the Cox regression analysis of various prognostic factors for mortality. Then, age, GDF15, and robust clinical predictors were included as a risk score model to assess the predictive accuracy for all-cause and CV death in the receiver operating characteristic (ROC) curve analysis. Results: Age, GDF15, and albumin were significantly associated with higher all-cause and CV mortality risk that were combined as a risk score model. The highest tertile of GDF-15 (>1707.1 pg/mL) was associated with all-cause mortality (adjusted hazard ratios (aHRs): 3.06 (95% confidence interval (CI): 1.20–7.82), p < 0.05) and CV mortality (aHRs: 3.11 (95% CI: 1.02–9.50), p < 0.05). The ROC analysis of GDF-15 tertiles for all-cause and CV mortality showed 0.68 (95% CI = 0.59 to 0.77) and 0.68 (95% CI = 0.58 to 0.79), respectively. By contrast, the GDF15-based prediction model for all-cause and CV mortality showed 0.75 (95% CI: 0.67–0.82) and 0.72 (95% CI: 0.63–0.81), respectively. Conclusion: Age, GDF15, and hypoalbuminemia predict all-cause and CV death in MHD patients, yet a combination scoring system provides more robust predictive powers. An elevated GDF15-based risk score warns clinicians to determine an appropriate intervention in advance. In light of this, the GDF15-based death prediction model could be developed in the artificial intelligence-based precision medicine.

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“…The rise of machine learning is driven by the ability to process “big data” and the need to deliver the best possible value- and evidence-based care. The utility of artificial intelligence (AI) coupled with machine learning, has generated much interest and many studies in clinical medicine [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. The machine learning approach has been developed recently for advantages in performance and extensibility and has become indispensable for solving complex problems in most sciences [ 80 , 81 , 82 ].…”

Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

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Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

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Role of Artificial Intelligence in Kidney Disease

Cited by 51 publications

References 105 publications

Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

A Growth Differentiation Factor 15-Based Risk Score Model to Predict Mortality in Hemodialysis Patients

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

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