Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning

The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, the...

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Bibliographic Details
Main Authors: Mikhail Petrov, Nadezhda Makarova, Amir Monemian, Jean Pham, Małgorzata Lekka, Igor Sokolov
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Cells
Subjects:
imaging
nanomedicine
cancer
atomic force microscopy
ringing mode
artificial intelligence
Online Access:https://www.mdpi.com/2073-4409/14/1/14
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