Telemedicine & telehealth

Telemedicine and telehealth group mainly researches on advanced, interdisciplinary technologies with the aim to transfer these technologies into clinical practice to aid the clinicians in remote diagnostic and treatment in medical centers and hospitals. The group consists of experts in the fields of biomedical image processing, signal processing, machine learning, artificial intelligence, under collaboration with medical experts in telemedicine and telehealth in several hospitals and medical universities as well as technological companies in researching and developing new technologies in the fields.

Core members

Dr. Luu Manh Ha, Group leader

Prof. Theo van Walsum, International advisor

Dr. Tran Trong Hieu

Mr. Le Quoc Anh

Biomedical image processing

In this research direction, we mainly focus on advanced methods and algorithm in biomedical image processing, with the aim to apply these methods and algorithms into clinical practice, using biomedical data from hospital and medical centers, in order to aids clinicians in diagnosis and treatment of human diseases.

CT image analysis for liver cancer intervention using convolutional neural networks

Improving karyotyping of human chromosomes using deep learning

Smart PACS for telemedicine

In this research direction, we build a picture archiving and communication system for medical images, integrated with smart medical image processing methods and algorithms (Smart PACS) which are developed in the first research direction. This system is build based on an open-source PACS, which is compatible to typical connection protocols and extended smart modules.

Extended picture archiving and communication systems

IT-based management system for remotely monitoring patent’s health

Monitoring vital signs of patients such as heart rate, respiratory rate, blood pressure is regular activity is hospitals and medical centers. Typically, clinicians have to directly interact the patient body for each measurement.  However the number of clinical staffs per patients is often very low, resulting in overload to the clinicians.  In this research direction, we research on a system which allows to measure the vital signs using non-contact methods such as Doppler radars for measuring the heart rate and respiratory rate, IR sensor for body temperature, combined with AI technology to automatically recognize patient ID via a webcam. These measurements are transferred to the central server, enabling the clinicians to monitor the patient’s health remotely.

Signal and data processing of vital sign data