Work

Machine learning for direct oxygen saturation and hemoglobin concentration assessment using diffuse reflectance spectroscopy

Year: 2020

Type: article

Abstract: SIGNIFICANCE: Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neural networks (ANN) determining the absorption coefficient one wavelength at a time. AIM: To present an ANN-based method that directly outputs the oxygen saturation and the hemoglobin concentration using the shape of the measured spectra as input. APPROACH: A probe-based DRS setup with dual source-detector separations in the visible wavelength range was used. ANNs were trained on spectra generated from a three-layer tissue model with oxygen saturation and hemoglobin concentration as target. RESULTS: Modeled evaluation data with realistic measurement noise showed an absolute root-mean-square (RMS) deviation of 5.1% units for oxygen saturation estimation. The relative RMS deviation for hemoglobin concentration was 13%. This accuracy is at least twice as good as our previous nonlinear optimization method. On blood-intralipid phantoms, the RMS deviation from the oxygen saturation derived from partial oxygen pressure measurements was 5.3% and 1.6% in two separate measurement series. Results during brachial occlusion showed expected patterns. CONCLUSIONS: The presented method, directly assessing oxygen saturation and hemoglobin concentration, is fast, accurate, and robust to noise.

Source: Journal of Biomedical Optics

DOI: 10.1117/1.jbo.25.11.112905

Authors: Ingemar Fredriksson, Marcus Larsson, Tomas Strömberg

Institution: Linköping University

Language: English

Topic: Optical Imaging and Spectroscopy Techniques

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,675