Key result
An on-chip classifying biochemical multi-sensor integrated interface achieved classification model F1 scores of 0.79 for diabetes and 0.72 for liver disease.
Why the study?
Biochemical multi-sensors for disease diagnosis face critical problems of sensor drift over time, long settling time, and computational burdens on edge-computing devices.
A novel on-chip biochemical multi-sensor integrated interface with anti-drift normalization achieved F1 scores of 0.79 and 0.72 for diabetes and liver disease classification.
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Advances on-chip metabolic sensing; leaves open clinical validation before cardiovascular use.
An on-chip classifying biochemical multi-sensor integrated interface is presented for diseases diagnosis applications, including diabetes and liver diseases. Against critical problems of sensor drift over time and long settling time from analyte concentration variations, two proposed circuit-level schemes of anti-drift normalization and settling-period detection are designed by utilizing two different detectors of min-max and level-crossing. For disease classification without computation burden in edge-computing devices, an on-chip analog neural network circuit is included together, and the whole readout integrated circuit (ROIC) is fabricated in a 0.18-μm CMOS process. A system-level prototype with an in-house organic electrochemical transistor (OECT) sensor array was implemented and experimentally verified to provide biochemical sensing of four electrolyte ions and glucose. It achieved on-chip classification model F1 scores of 0.79 and 0.72 on diabetes and liver disease respectively.
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Yeom et al. (2025) studied Diabetes and liver diseases. On-chip classifying biochemical multi-sensor integrated interface was evaluated on Classification model F1 scores. An on-chip classifying biochemical multi-sensor integrated interface achieved classification model F1 scores of 0.79 for diabetes and 0.72 for liver disease.
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