Farewell to external software
What is the problem that Van Doremaele and her employees have solved? “For practical use in healthcare devices, neuromorphic technology must consume little power, interface with a sensor, and be easy to train for use. The first two can be solved with neuromorphic electronics. The central issue is the training part.”
Until now, a neuromorphic chip’s neural network has been trained using external software, a process that can be time-consuming and energy-inefficient. “Now our new chip can learn itself by processing real-time patient data. This really speeds up the training process and promotes the use of the chip in real interactive bioapplications,” said the researcher.
Search for chloride anions
To test the effectiveness of their brand new chip, the researchers used it to test for cystic fibrosis. Cystic fibrosis is a hereditary disease that can damage organs such as the lungs and digestive system.
An existing way to test for the disease is through a sweat test where a high level of chloride anions is an indicator of cystic fibrosis. Reliable sensors are already available to test for cystic fibrosis, so this test provided the researchers with an easy-to-verify command for their on-chip learning sensor.
“To simplify implementation, we did not work with real patient data. Instead, we used sweat samples from healthy donors,” says Van Doremaele. “One sample was a negative or healthy sample of donor sweat, while a second sample was prepared to have a very high concentration of chloride anions.”
The researchers’ neuromorphic biosensor consists of three main parts: the sensor module, the neural network hardware and the classification part. A drop of sweat is added to the sensor module, after which chloride and other ion concentrations in the sweat are detected with ion-selective electrodes. These signals are then processed by the neuromorphic chip itself. Finally, the result of the analysis is displayed as a green or red light indicating a negative or positive result respectively.
2023-09-14 21:22:30
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