A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS)
| Autor: | Maryam Sadeghi, Seyed Massood Nabavi, Mahdiyeh Khanbagi, Chris Kalafatis, Seyed-Mahdi Khaligh-Razavi |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male medicine.medical_specialty Neurology Adolescent Neurofilament light medicine.medical_treatment Neuropsychological Tests Audiology Artificial intelligence (AI) lcsh:RC346-429 BICAMS Correlation Multiple sclerosis Young Adult 03 medical and health sciences 0302 clinical medicine Artificial Intelligence medicine Humans Learning Cognitive Dysfunction Neurochemistry Effects of sleep deprivation on cognitive performance Cognitive rehabilitation therapy Integrated cognitive assessment (ICA) Language-independent lcsh:Neurology. Diseases of the nervous system 030304 developmental biology 0303 health sciences Rehabilitation business.industry Reproducibility of Results Cognition General Medicine Middle Aged medicine.disease Convergent validity Case-Control Studies Biomarker (medicine) Female Artificial intelligence Cognitive Assessment System Neurology (clinical) Digital biomarkers business 030217 neurology & neurosurgery Research Article |
| Zdroj: | BMC Neurology, Vol 20, Iss 1, Pp 1-13 (2020) BMC Neurology |
| DOI: | 10.21203/rs.2.10768/v3 |
| Popis: | BackgroundCognitive impairment is common in patients with MS. Accurate and repeatable measures of cognition have the potential to be used as a marker of disease activity. We developed a 5-minute computerized test to measure cognitive dysfunction in patients with MS. The proposed test –named Integrated Cognitive Assessment (ICA)– is self-administered and language-independent.ObjectiveTo determine ICA’s validity as a digital biomarker for assessing cognitive performance in MS.Methods91 MS patients and 83 healthy controls (HC) took part in substudy 1, in which each participant took the ICA test and the Brief International Cognitive Assessment for MS (BICAMS). We assessed ICA’s test-retest reliability, its correlation with BICAMS, its sensitivity to discriminate patients with MS from the HC group, and its accuracy in detecting cognitive dysfunction. In substudy 2, we recruited 48 MS patients, and examined the association between the level of serum neurofilament light (NfL) in these patients and their ICA scores.ResultsICA demonstrated excellent test-retest reliability (r=0.94), with no learning bias (i.e. no significant practice effect); and had high level of convergent validity with BICAMS. ICA was sensitive in discriminating the MS patients from the HC group, and demonstrated a high accuracy (AUC = 95%) in discriminating cognitively normal from cognitively impaired participants. Additionally, we found a strong association (r=-0.79) between ICA score and the level of NfL in MS patients.ConclusionsICA can be used as a digital biomarker for assessment and monitoring of cognitive performance in MS patients. In comparison to standard cognitive tools for MS (e.g. BICAMS), ICA is shorter in duration, does not show a learning bias, is independent of language, and takes advantage of artificial intelligence (AI) to identify cognitive status of patients more accurately. Being a digital test, it further has the potential for easier electronic health record or research database integration. |
| Databáze: | OpenAIRE |
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