Novel Systems Based on Artificial Intelligence and Numerical Algorithms for Predicting Laboratory Results: A Comparative Study of Original Automatic Prediction Model with Advances in the Field

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 113 - 131

Published: Jan. 1, 2025

Language: Английский

State of the art review of AI in renal imaging

Abdominal Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Abstract Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading higher detection incidental renal lesions. Differentiation between benign and malignant lesions is essential for effective treatment planning prognosis. tumors present numerous histological subtypes different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) deep (DL), shows promise in radiological analysis, providing advanced tools lesion detection, segmentation, classification improve diagnosis personalize treatment. Recent advancements AI have demonstrated effectiveness identifying predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, publication bias. In this review we explored the current role assessing kidney lesions, highlighting its potential preoperative addressing existing challenges clinical implementation.

Language: Английский

Enhanced Electronic Diagnosis System by CCNNViT Model for Kidney Cyst Detection

Published: Oct. 4, 2024

Language: Английский