Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning - post hoc analysis

Aug 5, 2026

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Quantification of cognitive impairment identification through olfactory-stimulated functional near-infrared spectroscopy combined with machine learning was evaluated using post hoc analyses across a prior diagnostic trial and an external additional trial. Two independent, patient-level diagnostic interventional studies enrolled elderly participants (>60 years) with suspected cognitive decline. Oxygenation differences in the orbitofrontal cortex were assessed alongside clinical and biomarker measures including amyloid PET, MRI, and neuropsychological testing. Machine learning achieved higher diagnostic accuracy than statistical methods in both internal and external validations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

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Abstract

Background

Methods

Results

Interpretation

Introduction

Rationale for early detection

Methods

Study design and ethics statements

Kim etal. Alzheimer’s Research & Therapy (2023) 15:127 [https://doi.org/10.1186/s13195-023-01268-9](https://doi.org/10.1186/s13195-023-01268-9) Alzheimer’s Research & Therapy RESEARCH Open Access Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial Jaewon Kim1†, Hayeon Lee2†, Jinseok Lee2, SangYoul Rhee1, Jae Il Shin3, Seung Won Lee4, Wonyoung Cho1, Chanyang Min 1, Rosie Kwon 1,5, Jae Gwan Kim6* and Dong Keon Yon1,7* Abstract Background We aimed to quantify the identification of mild cognitive impairment and/or Alzheimer’s disease using olfactory-stimulated functional near-infrared spectroscopy using machine learning through a post hoc analysis of a previous diagnostic trial and an external additional trial. Methods We conducted two independent, patient-level, single-group, diagnostic interventional trials (original and additional trials) involving elderly volunteers (aged > 60 years) with suspected declining cognitive function. All volunteers were assessed by measuring the oxygenation difference in the orbitofrontal cortex using an open-label olfactory-stimulated functional near-infrared spectroscopy approach, medical interview, amyloid positron emission tomography, brain magnetic resonance imaging, Mini-Mental State Examination, and Seoul Neuropsychological Screening Battery. Results In total, 97 (original trial) and 36 (additional trial) elderly volunteers with suspected decline in cognitive function met the eligibility criteria. The statistical model reported classification accuracies of 87 . 3% in patients with mild cognitive impairment and Alzheimer’s disease in internal validation (original trial) but 63. 9% in external validation (additional trial) . The machine learning algorithm achieved 92 . 5% accuracy with the internal validation data and 82. 5% accuracy with the external validation data. For the diagnosis of mild cognitive impairment, machine learning performed better than statistical methods with internal (86 .0% versus 85. 2%) and external validation data (85 .4% versus 68.8%) . Interpretation In two independent trials, machine learning models using olfactory-stimulated oxygenation differences in the orbitofrontal cortex were superior in diagnosing mild cognitive impairment and Alzheimer’s disease †Jaewon Kim and Hayeon Lee are the first authors. *Correspondence: Jae Gwan Kim [jaekim@gist.ac.kr](jaekim@gist.ac.kr)[ ](jaekim@gist.ac.kr)Dong Keon Yon[yonkkang@gmail.com](yonkkang@gmail.com) Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativeco](http://creativeco)[mmons.org/publicdomain/zero/1.0/](mmons.org/publicdomain/zero/1.0/)) applies to the data made available in t

FAQs

What is the main goal of the study?

To quantify how well olfactory-stimulated functional near-infrared spectroscopy (fNIRS) combined with machine learning can identify mild cognitive impairment and/or Alzheimer’s disease using post hoc analyses of clinical trials.

How were participants assessed in the trials?

Participants over 60 with suspected cognitive decline underwent open-label olfactory-stimulated fNIRS measurement of oxygenation differences in the orbitofrontal cortex, along with medical interview, amyloid PET, brain MRI, MMSE, and Seoul Neuropsychological Screening Battery.

How did machine learning performance compare with statistical methods?

For mild cognitive impairment, machine learning outperformed statistical approaches in both internal validation (86.0% vs 85.2%) and external validation (85.4% vs 68.8%).

Aug 5, 2026

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