Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.
|Publication status||Published - 2022 Jun 16|
Bibliographical noteFunding Information:
J.L. and S.K. contributed equally to this work. This work was supported by the National Research Foundation of Korea (NRF) (Nos. NRF‐2021R1A2C3011450, NRF‐2018M3A7B4071106, NRF‐2019M3F3A1A02072175, NRF‐2021M3F3A2A03017782, and NRF‐2021M3F3A2A01037738). This study was also supported by the Open Resource Research Program of the Korea Institute of Science and Technology (Nos. 2E31032 and 2E31550).
© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Mechanics of Materials
- Mechanical Engineering