We develop AI-enabled applications to improve medical diagnosis and treatment.

We aim to

Medical Imaging

MRI_denoising

Denoising

art_removal_mri

Artifact removal and image registration

mri_tumor_quantification

Quantification of brain tumours

mri_seizure quantification

Quantification of epileptic lessions

fMRI

Mapping of the brain activation

functionalconnectivity

Functional and effective connectivity

tractography

Effective connectivity and tractography

ct_denoising and artifact removal

Denoising, artifact removal

ct_stroke

Quantification and prediction of stroke

ultrasound_contrastenh

Contrast enhancement

Biomedical Signal Processing

eeg_epilepsydetect

Seizure detection, seizure prediction

eeg_sleepdetect

Sleep stages classification

EEG_bci 2

Brain-Computer Interface (BCI)

ecg_arrhythmia

Arrhythmia detection

ecg_seizure

Seizure detection, stages of anxiety detection

emg_seizure

Seizure detection, examination of electrical activity of muscles, determination of muscle fatigue

Infectious Diseases

In supervised machine learning, you use data to teach automated systems how to make accurate decisions. ML algorithms are designed to discover patterns and associations in data; they learn from that data and encode that learning into a model to accurately predict a data attribute of importance for new data. Training data, therefore, is fundamental in the pursuit of machine learning. With high-quality data, subtle nuances and correlations can be accurately captured and high-fidelity predictive systems can be built. But if training data is of poor quality or inadequate, the efforts of even the best ML algorithms may be rendered useless. Real World Data (RWD) denotes patient level data which is collected from different sources as part of routine healthcare practice. RWD originally comprised non-omics clinical data typically derived from Electronic Health Records (EHRs) containing a broad range of data (e.g. medical history, imaging data, etc.). Hence, RWD is seen as a potentially rich source to enhance the understanding of a patient’s disease trajectory and to generate insights as to how clinical care affects patient outcome under real world conditions, with the aid of machine learning tools.

Use of real world data for medication optimization

Sepsis is a major global health problem and the World Health Organization (WHO) is calling for multidisciplinary actions to improve the morbidity and mortality due to sepsis. It is estimated that sepsis affects about 49 million people annually and causes about 11 million related deaths. The incidence of sepsis shows heterogenicity among the global regions, which tends to differentiate depending on their respective development status. Apart from the serious death toll, the burden of sepsis is further increased by its various social and economic implications, since sepsis is a major cause of increased length of in-hospital stay, readmission and disability. Different algorithms can be trained by processing real-world clinical and laboratory data and their variations in all-cause hospital admissions (excluding the admissions where sepsis is the initial diagnosis). We aim to develop a sensitive tool that will predict the risk of sepsis in admitted patients and detect sepsis incidents early during the disease, hence raising the awareness of healthcare providers, to intervene as soon as possible.

Sepsis prediction

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Prediction of the spread , outcome of an infectious disease

Technologies we use:

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