LABORATORIES OF BRAIN HEALTH AND BRAIN AI | School of Biomedical Engineering, International University

LOCATION

Room LA1.513, International University, Vietnam National University

RESEARCH INTERESTS

Brain Health Lab

DEVELOPMENT OF SYSTEMS AND METHODS FOR NEURODEGENERATIVE AND NEUROLOGICAL DISEASES DIAGNOSIS AND MONITORING

Alzheimer’s disease (AD) is the prevalent form of neurodegeneration, lacking a cure and significantly affecting patients’ quality of life. Diagnosing AD and other neurodegenerative diseases remains suboptimal, posing a challenge, especially in the context of Vietnam’s aging population.

Research goal:

Develop simple, efficient, low-cost systems and methods to diagnose neurodegenerative diseases (AD, Vascular Dementia, Stroke or Brain tumor).
Investigate factors influencing disease progression (including computational and molecular approach). Create an intervention app to prevent Dementia
Developing systems and methods for Alzheimer’s disease early diagnosis using EEG, fNIRS, OCTA, etc.

DEVELOPMENT OF SYSTEMS AND METHODS FOR EVALUATING AND IMPROVING MENTAL HEALTH

Mental disorders like depression, anxiety, and mania are common across various age groups, including teenagers, college students, working-age individuals, and the elderly. In Vietnam, awareness is limited due to a lack of affordable and accurate diagnostic methods. Moreover, even after diagnosis, patients often receive ineffective medications without access to supportive therapies

Research goal:

Investigate and design novel systems and methods to diagnose stress and common mental disorders.
Explore potential methods such as meditation and alpha music in improving these disorders and related physiological biometric measurements.

BRAIN AI LAB

SLEEP DISORDER DIAGNOSIS USING WEARABLE DEVICES

Sleep apnea is a serious sleep disorder where breathing stops and starts, causing loud snoring and persistent fatigue. It has three main types: Obstructive sleep apnea, Central sleep apnea, and Complex sleep apnea syndrome.
Research goal: Develop low-cost polysomnography/wearable systems to recognize sleep stages and diagnose related diseases.

PRECISION PSYCHIATRY

Mental disorders are the illnesses that affect your mood, thinking, and behavior, leading to distress, impaired functioning, and low life quality.
Research goal: Develop a precision psychiatry system to diagnose mental disorders applying artificial intelligence and physiological signals.

AI APPLIED IN MEDICAL IMAGING

Eyes, responsible for 80% of human sensory impressions, rely on the crucial role of the retina. There are biomarkers for detecting mental and neurodegeneration illnesses existing in the retina
Research goal: Develop AI algorithms for automatic analysis of retinal images to detect a wide range of diseases including mental and neurodegeneration diseases.

BRAIN – COMPUTER INTERFACE

Brain-Computer Interface (BCI) is a direct pathway between an augmented brain and an external device, enabling communication or control through brain signals instead of traditional pathways such as nerves and muscles.
Research goal: Develop BCIs system to repair human cognitive or sensory-motor functions

RESEARCH PRODUCTS

BRAIN ANALYTICS

Brain Analytics is a website platform implemented with artificial intelligence (AI) to calculate the probability of Alzheimer’s from an inputted MRI image. It supports diagnostic imaging doctors in detecting Alzheimer’s disease at early stages. By using our platform, the doctor can receive a highly accurate result within 10 minutes, and the doctors required was reduced from four to one.

BRAIN TRAIN

The BrainTrain application develops cognitive training games based on neuroscience to target Mild Cognitive Impairment (MCI) patients. BrainTrain aspires to provide MCI patients with a scientific training environment for the body’s most important organ, to improve cognitive functions and prevent dementia.

SKIN DETECTIVE

Skin Detective – a mobile application integrated with Artificial Intelligence (AI) by Medical AI Company, capable of automatically analyzing skin conditions and dermatological diseases, connecting users with dermatologists for remote diagnosis.

PUBLICATIONS & AWARD

Phan, T. N. M., Nguyen, H. T., Huynh, T. N. M., Nguyen, T. H., Tran, T. N. Y., & Ha, H. T. T. (2022, December). Development and Commercialization of a Brain Training App Targeting the Vietnamese Elderly. In International Conference on the Development of Biomedical Engineering in Vietnam (pp. 719-737). Cham: Springer Nature Switzerland.
Nguyen, T. T. Q., Hoang, C. B. D., Hoang Le, M. D., Anh Vo, N. T., Quang, H., Nguyen, C. M., … & Thanh Huong Ha, T. (2023). Assessing cognitive decline in Vietnamese older adults using the Montreal Cognitive Assessment-Basic (MoCA-B) and Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) during the COVID-19 pandemic: a feasibility study. The Clinical Neuropsychologist, 1-19.
Nguyen, M. T. D., Xuan, N. Y. P., Pham, B. M., Do, H. T. M., Phan, T. N. M., Nguyen, Q. T. T., … & Ha, H. T. T. (2023). Optimize temporal configuration for motor imagery-based multiclass performance and its relationship with subject-specific frequency. Informatics in Medicine Unlocked, 36, 101141.
Nguyen, D., Nguyen, H., Ong, H., Le, H., Ha, H., Duc, N. T., & Ngo, H. T. (2022). Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer’s disease. IBRO Neuroscience Reports, 13, 255-263.
L. Ngo, J. Cha and J. -H. Han, “Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images,” in IEEE Transactions on Image Processing, vol. 29, pp. 303-312, 2020, doi: 10.1109/TIP.2019.2931461.
Ngo, L. and Han, J.-.-H. (2017), Multi-level deep neural network for efficient segmentation of blood vessels in fundus images. Electron. Lett., 53: 1096-1098. https://doi.org/10.1049/el.2017.2066
Le, D. N. T., Le, H. X., Ngo, L. T., & Ngo, H. T. (2020). Transfer learning with class-weighted and focal loss function for automatic skin cancer classification. CoRR, abs/2009.05977. Retrieved from https://arxiv.org/abs/2009.05977

BRAIN ANALYTICS