Publications by authors named "Ryunosuke Noda"
Article Synopsis
- Minimal change disease (MCD) is a leading cause of nephrotic syndrome, making early and accurate diagnosis crucial, yet it typically requires a kidney biopsy for confirmation.
- This study explores the use of machine learning algorithms to create non-invasive models for diagnosing MCD by analyzing data from 248 patients with nephrotic syndrome.
- Among the algorithms tested, TabPFN performed the best, achieving high accuracy metrics and identifying key predictors like C3, total cholesterol, and urine red blood cells, suggesting that machine learning could effectively aid in MCD diagnosis.
IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning.
View Article and Find Full Text PDFBackground: Large language models (LLMs) have impacted advances in artificial intelligence. While LLMs have demonstrated high performance in general medical examinations, their performance in specialized areas such as nephrology is unclear. This study aimed to evaluate ChatGPT and Bard in their potential nephrology applications.
View Article and Find Full Text PDFPregnancy-onset thrombotic thrombocytopenic purpura (TTP) was reported by many obstetricians and hematologists, but less by nephrologists, and the detailed clinical course of its renal complication is not known. Here, we report a case of a 33-year-old pregnant woman who suffered from pregnancy-onset TTP with nephrotic syndrome which was controlled by the termination of pregnancy. On admission, she had periorbital and lower leg edema at 32 weeks of gestation.
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