NIE 2024 | Brain-Autonomous Aurora King Xiao Yi: Practical Sharing on Digital Therapies in Cognitive Disorder Diagnosis

The following are the key points from Wang Xiaoyi's speech:

Wang Xiaoyi pointed out that cognitive digital therapy is a non-drug treatment technology. It is aimed at cognitive decline, especially in diseases such as neurological disorders, mental illnesses, cardiovascular and cerebrovascular diseases, and child development. Based on the scientific research results of brain cognition, it takes into account the scientific evidence provided by evidence-based medicine and integrates algorithms such as artificial intelligence and big data analysis. It collects patients' behavioral information through multi-dimensional sensors and is driven by a software system to provide real-time feedback on the patient's cognitive function status, thereby achieving digital cognitive assessment and intervention.

Current medical research indicates that mild cognitive impairment (MCI) is a golden period for cognitive function intervention. MCI represents an important stage in transition from subjective cognitive decline (SCD) to Alzheimer's disease (AD) or vascular dementia (VD). The incidence rate during this stage typically increases rapidly within 3 to 5 years, while the slower onset phase may last for 10 to 15 years. In this irreversible phase, traditional drug therapies and device-based treatments are often ineffective. Digital therapy interventions at this stage are expected to significantly delay disease progression and even improve the quality of life for patients. Cognitive impairment diseases affect a wide range of people across different age groups, including developmental, vascular, mental, and neurodegenerative types, with about 350 million people affected.

The National Cognitive Center project has made extensive arrangements nationwide, and through a three-tier system comprising core, advanced cognitive centers, and memory prevention and treatment centers, it has effectively improved the diagnosis and prevention level of cognitive disorders. Core and advanced cognitive centers are mainly located in top-tier medical institutions and regional medical centers, undertaking tasks such as formulating cognitive disorder standards, diagnostic quality control, and technical support. Memory prevention and treatment centers serve more grassroots communities, helping to identify at-risk populations for cognitive disorders earlier through extensive public education and preventive screenings. As of now, a total of 602 cognitive centers have been established or are under construction nationwide, forming a cognitive disorder diagnosis and treatment network covering the entire country, providing important support for improving the prevention and treatment level of cognitive disorders nationwide.

The application of AI technology in cognitive impairment assessment and intervention has formed a complete set of digital cognitive evaluation and training systems, providing patients with personalized and precise assessment and intervention methods. This system combines digital human evaluation, AI intelligent recommendation algorithms, and multi-dimensional cognitive training systems. It can focus on a comprehensive assessment of patients' cognition, speech, and mental state, and provide efficient and accurate cognitive intervention plans. The design covers evaluations in multiple key areas from attention, memory to executive function. Through a precise, dynamic, and targeted digital evaluation system, it can provide a good tool for short-term effect evaluation of different types of patients. By comprehensively collecting and stratifying patient information, it quantifies the optimal personalized training plan.

The Multiple Cognitive Stimulus Engine encompasses multiple cognitive domains such as attention, memory, executive function, thinking, language, and perception. Through dynamic training tasks, it helps patients achieve significant improvements in these critical cognitive functions. To ensure the effectiveness of training tasks, the system is personalized based on an AI intelligent recommendation system that can adaptively adjust the difficulty of training to ensure that each patient receives an intervention plan that matches their cognitive state. The system contains more than 300 effective cognitive training tasks, combined with multi-dimensional precise AI adaptive recommendations, maximizing the effectiveness and personalization of interventions.

The AI intelligent recommendation algorithm plays a key role in this system. It can dynamically adjust based on the patient's real-time data, push adaptive training tasks, and ensure that each patient's intervention process aligns with their current cognitive state. This personalized training method has helped patients achieve more significant improvements in cognitive function.

In practical applications, intelligent cognitive scales can efficiently complete assessment tasks. This assessment system relies on multimodal intent automatic recognition technology and real-time interaction with virtual doctors, capable of recording the entire assessment process to ensure data integrity and traceability. Through voice automation error correction functions, patient engagement is enhanced, and the system can improve diagnostic accuracy through intelligent assisted interpretation. Cognitive assessment systems designed based on brain neural networks can comprehensively evaluate patients' brain functions, meticulously depict their specific cognitive abilities, and provide more precise functional assessments.