Deep learning to classify arteriovenous access aneurysms in hemodialysis patients

Zhang, Hanjie; Preddie, Dean C.; Krackov, Warren; Sor, Murat; Waguespack, Peter; Kuang, Zuwen; Ye, Xiaoling; Kotanko, Peter

doi:10.1093/ckj/sfab278

Clinical Kidney Journal

2021

DOI: 10.1093/ckj/sfab278

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Deep learning to classify arteriovenous access aneurysms in hemodialysis patients

Hanjie Zhang

Dean C. Preddie²,

Warren Krackov³

et al.

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Cited by 7 publications

(1 citation statement)

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“…For example, Chan et al 1 applied natural language processing to EHRs to identify symptom burden in dialysis patients; their study revealed that natural language processing had higher sensitivity compared with International Classification of Diseases codes for identification of common hemodialysis-related symptoms. Zhang et al 2 successfully used cloud-based AI/ML-driven image analysis to classify vascular access aneurysms as nonadvanced or advanced with an area under the receiver operating characteristic curve of 0.96. Prognosis is the most widely used application of AI/ML in dialysis.…”

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confidence: 99%

Artificial Intelligence and Machine Learning in Dialysis

Kotanko

Zhang

Wang

2023

CJASN

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confidence: 99%

Artificial Intelligence and Machine Learning in Dialysis

Kotanko

Zhang

Wang

2023

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A new era in the science and care of kidney diseases

Zoccali,

Mallamaci,

Lightstone

et al. 2024

Nat Rev Nephrol

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Artificial intelligence: a new field of knowledge for nephrologists?

Arizon

Viera

Pilco

et al. 2023

Clinical Kidney Journal

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Artificial Intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment, and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging, and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration, and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis, and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

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Deep learning to classify arteriovenous access aneurysms in hemodialysis patients

Cited by 7 publications

References 7 publications

Artificial Intelligence and Machine Learning in Dialysis

Artificial Intelligence and Machine Learning in Dialysis

A new era in the science and care of kidney diseases

Artificial intelligence: a new field of knowledge for nephrologists?

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