Over the past two years, near infrared spectroscopy (NIRS) and related NIR techniques have seen rapid adoption in biomedical research. These developments span non invasive diagnostics, functional monitoring, machine learning integration, point of care probes, and applications in complex clinical settings such as liver fibrosis, viral detection, neonatal care, brain injury, and neurodegenerative disorders. This article synthesizes 10 key publications, highlighting trends, methodologies, and clinical potential.
Abstract
Near‑infrared spectroscopy (NIRS) has emerged as a versatile optical modality in biomedical applications due to its non‑invasive nature, sensitivity to molecular and hemodynamic changes, and compatibility with advanced data analytics. Recent studies have demonstrated its application in liver fibrosis staging, neonatal hemodynamic monitoring, functional neuroimaging for consciousness detection, viral detection in serum, kidney disease diagnostics, and neurodegenerative disorder assessment. Integration with machine learning has significantly enhanced diagnostic performance. This review contextualizes progress from 2024 to 2025, summarizing principles, clinical findings, and future directions.
Introduction
Near‑infrared spectroscopy (NIRS) exploits the absorption and scattering of light in the ~700–2500 nm (extended to 680-2650 nm) range to probe tissue biochemical and physiological states. When combined with modern analytical tools, including machine learning and multivariate processing, NIRS can non‑invasively monitor oxygenation, structural biomarkers, molecular fingerprints, and functional brain hemodynamics. This has broad implications across clinical care, from intensive care units to point‑of‑care diagnostics.
Principles of NIR Spectroscopy in Biomedicine
NIR spectroscopy measures tissue composition and physiological changes by detecting how tissues absorb and scatter near‑infrared light. Hemoglobin oxygenation, water structures, lipids, proteins, and other chromophores produce characteristic NIR signatures. Coupling spectral features with machine learning enables high‑dimensional classification and biomarker discovery (general principle; see discussed papers).
Top 10 Influential Contributions
1. Fibrotic Liver Detection and Machine Learning
Addissouky et al. review how NIRS combined with machine learning (ML) can rapidly detect and stage liver fibrosis non‑invasively. They describe spectral features reflecting biochemical changes in fibrotic tissue and the application of neural networks and support vector machines for high‑accuracy classification (1).
This paper is influential because it demonstrates how near-infrared spectroscopy paired with machine learning can transform liver fibrosis assessment into a rapid, noninvasive, and clinically scalable diagnostic approach.
2. Neonatal Cerebral Oxygenation Monitoring
Pellicer et al. provide an in‑depth review of NIRS for cerebral oxygen saturation monitoring in preterm infants in the NICU. Continuous, cot‑side measurements of regional tissue oxygen saturation (rStO₂) offer end‑organ perfusion insight though require careful pathophysiological interpretation (2).
The authors significantly advance neonatal care by establishing NIRS as a continuous bedside window into cerebral oxygenation, reshaping how clinicians interpret perfusion and vulnerability in premature infants.
3. Lanthanide‑Doped Nanomaterials for Tumor Imaging
Advanced Optical Materials report on lanthanide‑doped nanomaterials designed for second near‑infrared window (NIR‑II) fluorescence imaging, highlighting benefits like deep tissue penetration, low autofluorescence, and utility for tumor diagnosis and therapy (3).
The Advanced Optical Materials study is impactful for pushing NIRS into the NIR-II regime, where lanthanide-doped nanomaterials enable deeper tissue imaging with exceptional contrast for tumor diagnosis and therapy.
4. Detecting Consciousness After Severe Brain Injury
Kazazian et al. demonstrate that functional NIRS (fNIRS) can identify neural signatures of consciousness in acutely brain‑injured, behaviorally unresponsive patients. fNIRS detected resting‑state networks and task‑driven brain activity indicative of preserved awareness (4).
This paper redefines neurological assessment by showing that fNIRS can reveal hidden consciousness in severely brain-injured patients, challenging reliance on behavioral observation alone.
5. Viral Detection in Serum Microsamples
Gómez and colleagues show that near‑infrared spectra of serum coupled with ML can discriminate hepatitis C virus (HCV) presence, revealing spectral patterns linked to water matrix changes and lipid profiles as potential biomarkers (5).
The authors make a pivotal contribution by demonstrating that subtle near-infrared spectral fingerprints in serum, when decoded with machine learning, can signal viral infection from microscale samples.
6. Hepatitis C Virus (HCV) Detection with Integrated Clinical Data
Pérez‑Gómez et al. further improve HCV detection by integrating NIRS spectral features with routine clinical data using ML models such as random forests, achieving robust diagnostic performance (accuracy 72.2%, AUC‑ROC 0.850) (6).
Pérez Gómez et al. extend virus detection by integrating spectral data with routine clinical variables, illustrating how hybrid data fusion markedly strengthens diagnostic reliability for hepatitis C virus.
7. NIR AIE Probes for Kidney Disease
Zhu et al. designed a near‑infrared aggregation‑induced emission (AIE) probe (QM‑N2) that activates with albumin to enable sensitive point‑of‑care (POC) detection of urinary microalbumin, critical for early kidney disease diagnosis and reducing autofluorescence interference (7).
Zhu et al. are influential for introducing a smart NIR aggregation-induced emission probe that converts molecular binding events into highly sensitive optical signals for early kidney disease screening.
8. fNIRS in Neurodegenerative Disorders
Liampas et al. review fNIRS studies on neurodegenerative diseases like Alzheimer’s, Parkinson’s, and ALS, finding altered prefrontal activation patterns and compensatory mechanisms that vary with disease severity (8).
This paper provides a unifying framework for understanding how fNIRS is capable of capturing altered cortical activation and compensatory neural strategies across multiple neurodegenerative disorders.
9. fNIRS to Identify MCI and Alzheimer’s Disease
Li, Yang, and Gong systematically review fNIRS studies differentiating mild cognitive impairment (MCI) from Alzheimer’s dementia, noting consistent reductions in tissue oxygenation, functional connectivity, and signal complexity, with ML models showing classification accuracies up to ~90% (9).
Li, Yang, and Gong elevate fNIRS from observation to prediction by demonstrating consistent physiological signatures of cognitive decline and showing that machine learning can distinguish MCI from Alzheimer’s disease with striking accuracy.
10. Applications Beyond Traditional NIRS
Emerging studies (for example, fNIRS in pain and neuropsychiatric symptoms of dementia) underscore novel physiological markers derived from NIR signals, broadening its relevance in clinical and research contexts (10).
Recent emerging studies are influential because they push NIRS beyond traditional boundaries, revealing novel biomarkers of pain, cognition, and neuropsychiatric symptoms that expand its clinical and translational relevance.
Final Summary
From liver fibrosis and neonatal oxygenation to tumor imaging, viral diagnostics, and cognitive decline, NIRS technologies exemplify versatility in biomedical science. Machine learning integration emerges as a recurring theme, enhancing classification accuracy and facilitating scalable clinical tools. Advanced probes and nanomaterials expand both molecular sensitivity and clinical applicability.
Conclusion
Near‑infrared spectroscopy and its functional extensions represent a maturing class of optical diagnostics and monitoring tools with broad biomedical impact. Continued development of computational models, probe chemistry, and multimodal integration is likely to drive adoption in precision medicine, point‑of‑care diagnostics, and real‑time physiological monitoring.
References
(1) Addissouky, T. A.; El Tantawy El Sayed, I.; Ali, M. M. A.; Alubiady, M. H. S. Optical Insights into Fibrotic Livers: Applications of Near Infrared Spectroscopy and Machine Learning. Arch. Gastroenterol. Res. 2024, 5 (1), 1–10. DOI:
(2) Pellicer, A.; de Boode, W.; Dempsey, E.; Greisen, G.; et al. Cerebral Near Infrared Spectroscopy Guided Neonatal Intensive Care Management for the Preterm Infant. Pediatr. Res. 2024. DOI:
(3) Tong, L.; Cao, J.; Wang, K.; Song, J.; Mu, J.; et al. Lanthanide Doped Nanomaterials for Tumor Diagnosis and Treatment by Second Near Infrared Fluorescence Imaging. Adv. Optical Mater. 2024. DOI:
(4) Kazazian, K.; Abdalmalak, A.; Novi, S. L.; Norton, L.; et al. Functional Near Infrared Spectroscopy: A Novel Tool for Detecting Consciousness after Acute Severe Brain Injury. Proc. Natl. Acad. Sci. U.S.A. 2024, 121, e2402723121. DOI:
(5) Gómez, J.; Barquero Pérez, O.; Gonzalo, J.; Salgüero, S.; et al. Near Infrared Spectroscopy (NIRS) and Machine Learning as a Promising Tandem for Fast Viral Detection in Serum Microsamples: A Preclinical Proof of Concept. Spectrochim. Acta Part A 2024, 322, 124819. DOI:
(6) Pérez Gómez, E.; Gómez, J.; Gonzalo, J.; Salgüero, S.; Riado, D.; Casas, M. L.; Gutiérrez, M. L.; Jaime, E.; Pérez‑Martínez, E.; García‑Carretero, R.; Ramos, J.; Fernández‑Rodríguez, C.; Catalá, M.; Martino, L.; Barquero‑Pérez, Ó. Exploratory Integration of Near Infrared Spectroscopy with Clinical Data: A Machine Learning Approach for HCV Detection in Serum Samples. Front. Med. 2025, 12, 1596476. DOI:
(7) Zhu, Z.; Chen, X.; Liao, H.; Li, L.; Yang, H.; Wang, Q.; Zhu, W. H. Microalbuminuria Sensitive Near Infrared AIE Probe for Point of Care Evaluating Kidney Diseases. Aggregate 2024, 5 (3), 526. DOI:
(8) Liampas, I.; Danga, F.; Kyriakoulopoulou, P.; Siokas, V.; et al. The Contribution of Functional Near Infrared Spectroscopy (fNIRS) to the Study of Neurodegenerative Disorders: A Narrative Review. Diagnostics 2024, 14, 663. DOI:
(9) Li, H.; Yang, X.; Gong, L. Functional Near Infrared Spectroscopy for Identifying Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review. Front. Neurol. 2025, 1578375. DOI:
(10) Huff, A. J.; Park, J.; Montero Hernandez, S.; Park, L.; Lee, C.; Pollonini, L.; Ahn, H. Functional Near Infrared Spectroscopy (fNIRS) Detects Brain Changes for Apathy and Pain in Patients with Alzheimer’s Disease and Related Dementias: An Exploratory Study. Neuroimage Rep. 2025, 5 (3), 100266. DOI:
