Chronic kidney disease stage identification using texture analysis of ultrasound images

Ahmad, R. Badlishah; Mohanty, Basant Kumar

doi:10.1016/j.bspc.2021.102695

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Early and noninvasive detection is crucial to preventing or delaying the progression of CKD. In this systematic review, 12 studies were identified in this section, of which 10 referred to CKD and related diseases classification or screening (31)(32)(33)(34)(35)(36)(37)(38)(39)48) and two referred to complications after allograft renal transplantation (8,40). Iqbal et al showed that texture feature obtained from the cortex region in ultrasound images was more significant than those obtained from the entire kidney or renal medulla in distinguishing between normal and CKD patients (48).…”

Section: Ckdmentioning

confidence: 99%

Artificial intelligence-aided ultrasound in renal diseases: a systematic review

Liang¹,

Du²,

Chen³

2023

Quant Imaging Med Surg

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Background: The development of artificial intelligence (AI) techniques has provided a novel strategy for improving the performance of renal ultrasound. To reflect the development of AI methods in renal ultrasound, we aimed to clarify and analyze the state of AI-aided ultrasound research in renal diseases.Methods: PRISMA 2020 guidelines have been used to guide all processes and results. AI-aided renal ultrasound studies (for both image segmentation and disease diagnosis) published up to June 2022 were screened through the databases of PubMed and Web of Science. Accuracy/Dice similarity coefficient (DICE), the area under the curve (AUC), sensitivity/specificity, and other indications were applied as evaluation parameters. The PROBAST was used to assess the risk of bias in the studies screened.Results: Of 364 articles, 38 studies were analyzed, and could be divided into AI-aided diagnosis or prediction related studies (28/38) and image segmentation related studies (10/38). The output of these 28 studies involved differential diagnosis of local lesions, disease grading of, automatic diagnosis, and diseases prediction. The median values of accuracy and AUC were 0.88 and 0.96, respectively. Overall, 86% of the AI-aided diagnosis or prediction models were classified as high risk. An unclear source of data, inadequate sample size, inappropriate analysis methods, and lack of rigorous external validation were found to be the most frequent and critical risk factors in AI-aided renal ultrasound studies.Conclusions: AI is a potential technique in the ultrasound diagnosis of different types of renal diseases, but the reliability and availability need to be strengthened. The use of AI-aided ultrasound in chronic kidney disease and quantitative hydronephrosis diagnosis will be a promising possibility. The size and quality of sample data, rigorous external validation, and adherence to guidelines and standards should be considered in further studies.

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Section: Ckdmentioning

confidence: 99%

Artificial intelligence-aided ultrasound in renal diseases: a systematic review

Liang¹,

Du²,

Chen³

2023

Quant Imaging Med Surg

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“…Indeed, most of those techniques are using statistical texture analysis approaches based on the extraction of texture features that allow the kidneys to be classified as a normal or abnormal organ. Clinicians mainly use statistical texture analysis methods [1][2][3][4][5]. Generally, these research works have two disadvantages, namely that they not only remain, unfortunately, manual in the selection of the region of interest to be studied but also have difficulty carrying out a complete classification accounting for different pathologies in their several stages.…”

Section: Introductionmentioning

confidence: 99%

Contribution to the Characterization of the Kidney Ultrasound Image Using Singularity Levels

Alaoui,

Korchiyne

2023

The 3rd International Day on Computer Science and Applied Mathematics

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“…Gray level cooccurrence matrix [37] 98.38% Produced good accuracy. Only 300 samples were taken for testing.…”

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confidence: 99%

Improved ResNet Models for Chronic Kidney Disease Prediction

D. Vetrithangam,

2024

jes

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Chronic kidney disease (CKD), a consequential health issue that can deeply affect an individual's overall wellness, can be initiated by either kidney cancer or a gradual reduction in kidney function. As the chronic disease advances, it can reach a critical stage where only dialysis or surgery can save lives. Halting its progress becomes crucial. CKD patients also face a heightened risk of premature death. Early detection of associated conditions poses a challenging task for healthcare professionals aiming to prevent their onset. A unique deep learning model is presented in this work for the prediction of CKD. Many existing CKD prediction models have the drawbacks of producing less accuracy, mispredicting, utilizing more computation time, and using low-quality datasets or data with noise and missing values, leading to misprediction. So it is necessary to develop new techniques that give high predictions with less computation time. The objective of this research work is to build improved ResNet models for the prediction of chronic kidney disease and evaluate their performance in comparison to other cutting-edge machine learning and deep learning methods. This research work developed ResNet models such as improved ResNet 152v2 with inception, improved ResNet 101, and improved ResNet50 models that produced 99.90%, 96.53%, and 93.968% accuracy, respectively. The proposed ResNet models for CKD prediction will be useful to nephrologists and other medical professionals.

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Chronic kidney disease stage identification using texture analysis of ultrasound images

Cited by 9 publications

References 19 publications

Artificial intelligence-aided ultrasound in renal diseases: a systematic review

Artificial intelligence-aided ultrasound in renal diseases: a systematic review

Contribution to the Characterization of the Kidney Ultrasound Image Using Singularity Levels

Improved ResNet Models for Chronic Kidney Disease Prediction

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