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

We aim to

Services and tools to harness healthcare data

Our team specializes in data harmonization of medical data and mapping it to the OMOP Common Data Model, enabling standardized analysis and characterization of healthcare data. With our extensive experience in this domain, we have successfully worked with a wide range of medical datasets from diverse sources, including electronic medical records (EMR) and claims data. One of our key strengths lies in the comprehensive mapping of medical codes to OMOP Standard Concepts. We meticulously transform diverse code systems, including standard terminologies, proprietary codes, and local coding systems, into the standardized OMOP vocabulary. Our meticulous approach ensures accurate and consistent representation of medical concepts, enabling seamless integration and interoperability across different data sources. Once the data is mapped to the OMOP Common Data Model, we seamlessly integrate it into your existing analytics toolset. Whether you utilize OHDSI tools or commercial applications, our team ensures smooth incorporation of the standardized data.


Medical Imaging

Medical image processing refers to a set of procedures performed in order to extract clinically meaningful information from various imaging modalities (MRI, CT, etc.) mainly for diagnosis or prognosis. The modalities are typically in vivo types.

Machine Learning models are used in order to compute relevant features. After being trained with the prior knowledge provided by medical experts, Machine Learning guides image registration, fusion, segmentation, and other computations towards accurate descriptions of the initial data and extraction of reliable diagnostic cues.


Denoising and artifact removal

Image registration

Quantification of brain tumours

Quantification of epileptic lesions


Mapping of the brain activation

Functional and effective connectivity

Diffusion MRI

Effective connectivity and tractography


Automated Cardiac Ultrasound Assistance Tool


Denoising, artifact removal

Quantification and prediction of stroke

Biomedical Signal Processing

Biomedical signal processing involves the treatment and analysis of bio-signal measurements. By using machine learning tools, the signals can be analyzed in order to help physicians with greater insights to make better decisions in clinical assessments.

The combination of novel machine learning models with signal processing methods has assisted in revealing information which entirely altered the diagnosis and prognosis of several diseases.

The application-oriented and data-driven bio-signal analysis and processing applications, not only benefit from the machine learning algorithms, but also encourage the development of novel (bio)medical treatments.


Seizure detection, seizure prediction

Sleep stages classification

Brain-Computer Interface (BCI)


Arrhythmia detection

Seizure detection, stages of anxiety detection


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

Infectious Diseases

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries.

With the advent of Machine Learning tools, we can provide more accurate and in-time prediction of epidemics, understand the specific features of each pathogen, and identify potential targets for drug development. Machine Learning tools and methods are able to identify patterns in data, provided that adequate data are available.

Patient medical data are increasingly being stored as electronic health records (EHRs) at healthcare institutions worldwide. However, EHRs can be inconsistent and noisy, may contain many missing values, and frequently include unstructured text fields. Nevertheless, the very fact that these data are electronically available in large volumes provides the potential for applying Machine Learning, including in the field of infectious disease management.

Use of real world data for medication optimization

Sepsis prediction

Prediction of the spread, outcome of an infectious disease

Technologies we use: