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13 March 2024 Near-infrared spectroscopy for assessing disease severity in ECMO patients
Kai-Hsiang Hou, Yu-Han Zheng, Hsiao-Huang Chang, Chia-Wei Sun
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Proceedings Volume PC12836, Optical Biopsy XXII: Toward Real-Time Spectroscopic Imaging and Diagnosis; PC128360A (2024) https://doi.org/10.1117/12.3001519
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
We present a novel approach utilizing non-invasive near-infrared spectroscopy (NIRS) to assess disease severity in Extracorporeal Membrane Oxygenation (ECMO) patients. By monitoring lower limb microcirculation, our real-time assessment enables informed adjustments to ECMO settings and cardiovascular drug dosages, potentially mitigating complications and improving patient outcomes. Through machine learning, we classified VV-ECMO and VA-ECMO patient populations into high and low disease severity groups with an accuracy of 80%. The NIRS and support vector machine(SVM) combination demonstrate promising potential for clinically distinguishing disease severity in ECMO patients, providing valuable treatment insights and predictive tools for patient conditions and prognoses.
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Kai-Hsiang Hou, Yu-Han Zheng, Hsiao-Huang Chang, and Chia-Wei Sun "Near-infrared spectroscopy for assessing disease severity in ECMO patients", Proc. SPIE PC12836, Optical Biopsy XXII: Toward Real-Time Spectroscopic Imaging and Diagnosis, PC128360A (13 March 2024); https://doi.org/10.1117/12.3001519
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KEYWORDS
Near infrared spectroscopy
Cardiovascular disorders
Diseases and disorders
Machine learning
Blood
Education and training
Oxygen
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