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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 Kim  ;  Hayeon Lee  ;  Jinseok Lee  ;  Sang Youl Rhee  ;  Jae Il Shin  ;  Seung Won Lee  ;  Wonyoung Cho  ;  Chanyang Min  ;  Rosie Kwon  ;  Jae Gwan Kim  ;  Dong Keon Yon 
 ALZHEIMERS RESEARCH & THERAPY, Vol.15(1) : 127, 2023-07 
Journal Title
Issue Date
Aged ; Alzheimer Disease* / diagnostic imaging ; Cognitive Dysfunction* / diagnostic imaging ; Humans ; Machine Learning ; Middle Aged ; Reproducibility of Results ; Spectroscopy, Near-Infrared
Alzheimer’s disease ; Cognitive impairment ; Machine learning ; Mild cognitive impairment ; fNIRS
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 compared to classic statistical models. Our results suggest that the machine learning algorithm is stable across different patient groups and increases generalization and reproducibility. Trial registration: Clinical Research Information Service (CRiS) of Republic of Korea; CRIS numbers, KCT0006197 and KCT0007589. Graphical Abstract: [Figure not available: see fulltext.] © 2023, The Author(s).
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1. College of Medicine (의과대학) > Dept. of Pediatrics (소아과학교실) > 1. Journal Papers
Yonsei Authors
Shin, Jae Il(신재일) ORCID logo https://orcid.org/0000-0003-2326-1820
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