AI-assisted high-density near-infrared spectroscopy system for cerebral oxygen saturation measurement in a porcine ischemia model

Stroke is one of the leading causes of mortality and is characterized by a sudden interruption of cerebral perfusion, resulting in neurological impairment. Especially in large-vessel occlusion (LVO), the absence of timely reperfusion therapy can lead to severe ischemic injury, causing permanent disability or death. Near-infrared spectroscopy (NIRS) is a non-invasive technique that estimates regional cerebral oxygen saturation (\(\hbox _2\)) by analyzing near-infrared light transmitted through biological tissue.

Despite its portability and cost-effectiveness, commercial NIRS systems that employ spatially resolved spectroscopy (SRS) algorithms lack sufficient reliability to distinguish between normal and stroke-affected conditions because of oversimplified assumptions. In this study, we propose an artificial intelligence–assisted NIRS system that analyzes high-density optical measurements to estimate cerebral oxygenation beyond the simplified assumptions of conventional SRS algorithms. Trained on an MRI-based synthetic dataset using cortical \(\hbox _2\) as the target label, the proposed model leverages measurements obtained at multiple source–detector distances to exploit depth-dependent spatial information and improve sensitivity to cortical oxygenation signals.

The proposed system was validated through a porcine common carotid artery occlusion experiment and compared with a conventional SRS-based NIRS system. Both systems showed decreases during occlusion and recovery after reperfusion, but statistically significant baseline-relative changes after multiple-comparison correction were observed only for NIRSIT-X. Furthermore, we compared the measured oxygen saturation against jugular venous oxygen saturation (\(\hbox _2\)) as a physiological reference.

In conclusion, based on linear mixed-effects modeling, the proposed system demonstrated a substantially stronger association with \(\hbox _2\) than the conventional system, achieving a markedly higher marginal \(R^2\) (0.561 vs. 0.177), indicating stronger physiological relevance for cerebral oxygenation monitoring. This work was partly supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (Project Number: 2710082474, RS-2024-00444862), and the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, and the Ministry of Food and Drug Safety) (Project Number: 1711196547, RS-2023-00242284). These authors contributed equally: Seongkwon Yu, Tae Jung Kim and Hayoung Kim.

School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea Seongkwon Yu, Bumjun Koh, Jimin Lee & Hyeon-Min Bae Department of Neurology, Seoul National University, College of Medicine, Seoul, 03080, Republic of Korea Department of Critical Care Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea Department of Emergency Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea Hayoung Kim, Heesu Park & Woon Yong Kwon Department of Research and Development, Optics Brain Electronics Laboratory, OBELAB Inc, Seoul, 06211, Republic of Korea Correspondence to Hyeon-Min Bae or Sang-Bae Ko. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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