Clinical Sciences/Health Conditions
Huanping Wang, MS
Master Degree Candidate
School of Rehabilitation Medicine, Nanjing Medical University
Nanjing, Jiangsu, China (People's Republic)
Mingmei Ding, MS
Master Degree Candidate
南京医科大学
Nanjing, Jiangsu, China (People's Republic)
Ya Shen, MS
Master Degree Candidate
南京医科大学
Nanjing, Jiangsu, China (People's Republic)
Haiting Wei, MS
Master Degree Candidate
School of Linguistic Sciences And Arts, Jiangsu Normal University
Xuzhou, Jiangsu, China (People's Republic)
Zhixiang Huang, BS
offices director
Department of Rehabilitation Therapy, Wuxi Huishan District Rehabilitation Hospital
Wuxi, Jiangsu, China (People's Republic)
Jing Teng, MS
Master Degree Candidate
南京医科大学
Nanjing, Jiangsu, China (People's Republic)
Jie Song, MS
Master Degree Candidate
Department of Rehabilitation Medicine, The First Affiliated Hospital with Nanjing Medical University
Nanjing\, Jiangsu, China (People's Republic)
Qian Lu, MS
Master Degree Candidate
Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University
Suzhou, Jiangsu, China (People's Republic)
Taicheng Huang, PhD
PhD student
Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai, China (People's Republic)
Hanjun Liu, PhD
professor
Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University
Guang Zhou, Guangdong, China (People's Republic)
Zude Zhu, PhD
professor
School of Linguistic Sciences And Arts, Jiangsu Normal University
Xuzhou, Jiangsu, China (People's Republic)
Ying Shen, PhD
professor
Department of Rehabilitation Medicine, The First Affiliated Hospital with Nanjing Medical University
Nanjing, Jiangsu, China (People's Republic)
Early detection of mild cognitive impairment (MCI) is essential for initiating timely intervention and delaying progression to dementia. However, current diagnostic approaches often lack scalability and efficiency, limiting their utility in primary care and community settings.
This study developed and validated the Integrated Cognitive Screening Platform (ICSP), a self-administered, tablet-based tool designed for rapid, multidomain screening of MCI.
Design:
ICSP comprises five cognitive tasks involving immediate and delayed memory, attention, sensory perception, and executive function. Both accuracy scores and reaction times (RTs) were recorded and processed within five minutes. A total of 126 participants (76 with MCI, 50 cognitively normal controls) completed standard neuropsychological assessments and the ICSP battery. A multivariate logistic regression model was developed using 60% of the data as a training set and evaluated on the remaining 40% as a validation set.
Results:
RTs and accuracy scores in sensory perception and executive function tasks, along with educational attainment, were identified as significant predictors of MCI. The model achieved high classification performance (AUC: 0.915; P < .001), with robust validation performance (AUC: 0.821; P < .001).
Conclusion:
ICSP is an accurate and scalable digital screening tool capable of identifying MCI with high specificity. Its multimodal design and automated analysis make it well-suited for clinical and community-level early detection of cognitive impairments.
