A team of researchers at UCLA developed a cancer-detection mechanism based on deep learning technologies. The research team developed a device for detecting the presence of cancer cells in the blood. The project was accomplished in collaboration with NantWorks, and it spans into new areas of cancer research. The newly-developed device could diagnose the presence of blood cancer cells in milliseconds. This is the most distinct function of the device, and it could facilitate quick extraction of cancer cells. This device could also help in accentuating blood cancer treatments. Nature Scientific Journal Reports published the study’s findings.
Extracting Cancer Cells from the Blood
The ability to extract blood cancer cells immediately after diagnosis can help in preventing the disease from spreading. Furthermore, the use of deep-learning technologies ensures accuracy of results. Deep learning technology uses trained algorithms to gather insights from large sets of data. The device also uses photonic time stretch in conjunction with deep-learning technologies. Photonic time stretch is a measurement technique developed at UCLA.
Neural networks are an integral part of deep learning technologies. These networks help in understanding the functioning of the human brain. Henceforth, the use of deep learning technologies is extremely effective in generating speech, music, videos, and images.
Relevance of Cytometry
Cytometry techniques also helped the researchers in understanding the characteristics of the cells. The technique uses lasers to study the movement of cells and subsequently analyse their characteristics. A postdoctoral researcher from UCLA asserts that the technique can help in determining whether the cells are cancerous. Hence, the researchers have successfully eliminated the need for finding biophysical parameters of cells. Neural networks can easily analyse the characteristics of blood cells.
The research is a special case of neural network analysis in an oncological context. The next few years would be crucial for researchers to further build on the current findings.