Diagnosing and monitoring multiple myeloma (MM), a type of blood cancer, requires identifying and quantifying specific cells called circulating plasma cells (CPCs) in the blood. The conventional method for detecting CPCs is manual microscopic examination, which is time-consuming and lacks sensitivity. This study introduces a highly sensitive CPC detection method using an artificial intelligence-based system, Morphogo. It demonstrated remarkable sensitivity and accuracy, surpassing conventional microscopy. This advanced approach suggests that early and accurate CPC detection is achievable by morphology examination, making efficient CPC screening more accessible for patients with MM. This innovative system has the potential to be used in the diagnosis and risk assessment of MM.
Keyphrases
- induced apoptosis
- artificial intelligence
- deep learning
- cell cycle arrest
- risk assessment
- peripheral blood
- label free
- multiple myeloma
- high resolution
- machine learning
- oxidative stress
- signaling pathway
- high throughput
- cell death
- human health
- heavy metals
- single molecule
- cell proliferation
- single cell
- climate change
- convolutional neural network
- squamous cell
- pi k akt
- solid phase extraction