LSTM Deep Learning Enhances Optical Sensing for Biochemical and Medical Applications

Researchers have developed an LSTM-driven interferometric sensing system that achieves both high sensitivity and wide measurement range, overcoming previous trade-offs in optical sensing.
Key Details
- 1The LSTM model allows for accurate refractive index measurement beyond the free spectral range (FSR) limitation in optical interferometers.
- 2System architecture includes broadband light source, specially fabricated single-mode fiber, and spectral analyzer.
- 3Deep learning enables direct mapping of complex spectra to target measurements despite spectral overlap, tripling the detection range while retaining sensitivity.
- 4Efficient down-sampling reduces data acquisition and processing, making rapid, practical deployment feasible.
- 5Application relevance spans physics, chemistry, biology, and medicine—enabling real-time, high-precision monitoring in complex environments.
Why It Matters

Source
EurekAlert
Related News

ML and Multimodal Imaging Power Cerebral Blood Flow Monitoring for Spaceflight
Researchers developed a machine learning model that uses ultrasound and MRI data to predict cerebral blood flow in simulated microgravity for astronaut health.

Deep Learning Model Predicts Language Outcomes After Cochlear Implants Using MRI
AI model using deep transfer learning accurately predicts spoken language outcomes in deaf children after cochlear implantation based on pre-implantation brain MRI scans.

AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.