Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.
Ingyu ParkSang-Kyu LeeHui-Chul ChoiMoo-Eob AhnOhk-Hyun RyuDaehun JangUnjoo LeeYeo Jin KimPublished in: Brain sciences (2024)
In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.
Keyphrases
- mild cognitive impairment
- cognitive decline
- early stage
- machine learning
- white matter
- convolutional neural network
- contrast enhanced
- end stage renal disease
- deep learning
- magnetic resonance imaging
- cerebral palsy
- big data
- newly diagnosed
- chronic kidney disease
- ejection fraction
- artificial intelligence
- computed tomography
- multiple sclerosis
- diffusion weighted imaging
- magnetic resonance
- sentinel lymph node
- peritoneal dialysis
- optical coherence tomography
- electronic health record
- radiation therapy
- neoadjuvant chemotherapy
- high intensity
- rectal cancer
- pain management