Recent advances in medical image processing for the evaluation of chronic kidney disease

Alnazer, Israa; Bourdon, Pascal; Urruty, Thierry; Falou, Omar; Khalil, Mohamad; Shahin, Ahmad; Fernández-Maloigne, Christine

doi:10.1016/j.media.2021.101960

Cited by 58 publications

(37 citation statements)

References 273 publications

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“…Therefore, imaging techniques such as US, used in [26] with 82% accuracy in predicting the stage of CKD, and MRI are preferred. The latter has been shown to have the ability to allow assessment of both renal function and structure [120]. Major future developments may shift in this direction and focus on the development of methods that take advantage of MRI to be able to determine CKD.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Renal Pathologies: An Updated Survey

Magherini

Mussi

Volpe

et al. 2022

Sensors

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Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for Renal Pathologies: An Updated Survey

Magherini

Mussi

Volpe

et al. 2022

Sensors

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“…There have been a few studies employing radiomics models as computer assisted diagnostic tools for renal deficits using DWI scanning [3]. The detection of renal parenchymal lesions by MRI or DWI scanning in children with CAKUT is very time consuming.…”

Section: Discussionmentioning

confidence: 99%

“…Although the test accuracy reported for the best model was 80.9%, generalization is questionable due to the small cohort size and the lack of external validation. Despite the fact that we included 280 cases in our cohort, which is more than any prior machine learning works on CAKUT or CKD [3], algorithm development might benefit from a larger patient cohort, especially given the variability of image capture settings among institutions. Second, manual set for ROI was performed by students and radiologists reading the DWI images.…”

Section: Discussionmentioning

confidence: 99%

“…Advanced imaging, including ultrasound, magnetic resonance imaging (MRI), and scintigraphy, allow for the retrieval of structural, functional, and molecular information that may be used to identify changes in renal tissue properties and functionality [3]. Diffusion weighted imaging (DWI) is a non-invasive magnetic resonance modality, which uses the motion of water molecules as a contrast to measure in vivo movement of water diffusion or Brownian motion.…”

Section: Introductionmentioning

confidence: 99%

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A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI

Kang

Zhang

et al. 2022

Front. Phys.

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Background: Assessment of renal lesions and deficiency accurately remains critical in the diagnosis of congenital anomalies of the kidneys and urinary tracts (CAKUT) in children. Advanced imaging such as Magnetic resonance Imaging (MRI) and Diffusion weighted Imaging (DWI) allows structural and functional insufficiency to be detected. Currently, radiomics machine learning models are being explored as full-automated diagnostic tools. We aimed to develop a machine learning integrated radiomics model to predict renal anomalies and deficiency in children.Methods: A retrospective study of 280 children with MRI/DWI were enrolled between 2018 and 202 at a children’s hospital. A total of 1,037 radiomics features were extracted from the DWI images of each participant, which were divided into training set and test set (8:2 split). Using 5-fold cross-validated method, multiple machine learning algorithms were employed to predict renal lesions and deficiency when compared with the radiologist’s diagnosis based on DWI, 99mTc-labeled dimercaptosuccinic acid (DMSA) SPECT cortical renal scintigraphy or 99mTc-labeled diethylenetriamine pentaacetate (DTPA) renal scan.Results: For detecting the kidney lesions, the LASSO + Random Forest algorithm outperformed other classifiers with an accuracy of 0.750 (95% confidence interval, 0.734–0.766) and area under the curve (AUC) of 0.765 (95% confidence interval, 0.700–0.831). The performance of classifiers did not show a significant difference (p > 0.05) in detecting bilateral or unilateral kidney lesions by DWI scanning. The classifiers performed significantly better in bilateral kidney deficit than in unilateral kidney deficit (p < 0.05). We next built prediction models for renal deficiency using the radiomics signature and clinical features compared to renal scintigraphy. The ensemble model had a high-test accuracy of 80.9% ± 4.2% and a sensitivity of 91.7% ± 7.1% with a moderate calibration.Conclusion: An ensemble model integrated with DWI-radiomic and clinical features can be utilized to predict renal lesions and deficiency in children with CAKUT.

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“…If early diagnosis and treatment are ignored, it may lead to kidney cancer. In the latest study, Alnazer et al [123] evaluated the medical imaging methods used for CKD detection, including MRI, CT, PET, etc. These complex medical images can more comprehensively analyze kidney disease conditions and provide a data basis for DL's disease research.…”

Section: Kidney Diseasementioning

confidence: 99%

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie¹,

Yu²,

Lv³

2021

Computer Modeling in Engineering &Amp; Sciences

144

104

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In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research.

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Recent advances in medical image processing for the evaluation of chronic kidney disease

Cited by 58 publications

References 273 publications

Machine Learning for Renal Pathologies: An Updated Survey

Machine Learning for Renal Pathologies: An Updated Survey

A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI

Multi-Disease Prediction Based on Deep Learning: A Survey

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