AIMC Topic: Kidney

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Deep-learning model for evaluating histopathology of acute renal tubular injury.

Scientific reports
Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models...

UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning.

World journal of urology
BACKGROUND: Accurate estimation of the glomerular filtration rate (GFR) is clinically crucial for determining the status of obstruction, developing treatment strategies, and predicting prognosis in obstructive nephropathy (ON). We aimed to develop a ...

Assessing the perioperative outcomes of abdominal drain omission after robot-assisted partial nephrectomy.

Scientific reports
The study aimed to evaluate the impact of abdominal drain placement (vs. omission) on perioperative outcomes of robot-assisted partial nephrectomy (RAPN), focusing on complications, time to canalization, deambulation, and pain management. A prospecti...

[Artificial intelligence in kidney transplant pathology].

Pathologie (Heidelberg, Germany)
BACKGROUND: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology.

Predictive factors of renal function after robot-assisted partial nephrectomy in clinical T1b tumors.

Journal of robotic surgery
Robot-assisted partial nephrectomy (RAPN) has been shown to be a safe and effective method for treatment of small renal tumors, including clinical T1b renal cell carcinoma (RCC); however, the impact of RAPN for cT1b renal tumors on renal function is ...

Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intens...

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

Frontiers in endocrinology
BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features u...

Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset.

Interdisciplinary sciences, computational life sciences
Kidney ultrasound (US) images are primarily employed for diagnosing different renal diseases. Among them, one is renal localization and detection, which can be carried out by segmenting the kidney US images. However, kidney segmentation from US image...