Scientists modify 3D printed medical detectors through machine learning techniques

Machine learning technology is an important and broad field of computing where algorithms are predicted based on the data they process without being explicitly programmed. Therefore, the prospect of combining machine learning with 3D printing, as UCLA researchers can see in a new project, is definitely an exciting area. Using 3D printed prototype detectors with sensors that can be modified by machine learning techniques, researchers have demonstrated a new and more efficient way to detect tiny items such as cancer biomarkers, viruses and proteins. This can improve the treatment and diagnosis of serious infections and diseases.

Plasma sensing has been used for many years in medical research in order to gather information about the composition of matter at the submicroscopic level. The method irradiates light onto the metal nanostructures to amplify the local electric field. The interaction between the field and the particular molecule of interest to the investigator can be measured and this allows them to study valuable things about molecular concentration and kinetics.

However, the use of plasma sensing outside the laboratory environment has been limited due to the cost and bulk of the instruments involved. The UCLA team is led by Aydogan Ozcan, professor and associate director of electrical engineering and bioengineering at the California Institute of Nanosystems. They are now developing a prototype of a mobile, inexpensive plasmonic reader that is more accurate than traditional sensor designs. .

It is reported that the prototype utilizes machine learning techniques to determine what type of light source should be used in the plasma sensing process because it allows a particular algorithm to adapt to the data it presents and "trains" itself to make a decision. It has a wide range of applications in other areas, and one of the more notable is optical character recognition. Google's map software can accurately read numbers and letters on houses and streets, but for this task, pre-programmed algorithms will not be feasible.

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