The researchers combined machine learning techniques with accessible inkjet printing and microfluidics technology to develop low-cost, miniaturized biochip. (Credit: Gerd Altmann/Pixabay)

A new lab-on-a-chip can help study tumor heterogeneity to reduce resistance to cancer therapies. The researchers combined artificial intelligence, microfluidics, and nanoparticle inkjet printing in a device that enables the examination and differentiation of cancers and healthy tissues at the single-cell level.

Cancer cell and tumor heterogeneity can lead to increased therapeutic resistance and inconsistent outcomes for different patients. The team’s novel biochip addresses this problem by allowing precise characterization of a variety of cancer cells from a sample.

By combining machine learning techniques with accessible inkjet printing and microfluidics technology, they developed low-cost, miniaturized biochips that are simple to prototype and capable of classifying various cell types.

In the apparatus, samples travel through microfluidic channels with carefully placed electrodes that monitor differences in the electrical properties of diseased versus healthy cells in a single pass. The researchers’ innovation was to devise a way to prototype key parts of the biochip in about 20 minutes with an inkjet printer, allowing for easy manufacturing in diverse settings. Most of the materials involved are reusable or, if disposable, inexpensive.

The incorporation of machine learning helps manage the large amount of data the tiny system produces. This branch of AI accelerates the processing and analysis of large datasets, finding patterns and associations, predicting precise outcomes, and aiding in rapid and efficient decision-making.

By including machine learning in the biochip’s workflow, the team has improved the accuracy of analysis and reduced the dependency on skilled analysts, which can also make the technology appealing to medical professionals in the developing world.

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Topics:
Artificial Intelligence Medical Nanotechnology

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