We develop AI-enabled applications to improve medical diagnosis and treatment.
We aim to
Our team is always open into co-designing novel solutions for your problem. We will invite your clinical or research fellows to exchange ideas and work collaboratively on the specific requirements of the problem in order to provide the solution that best fits your needs.
Take advantage of our experience in sophisticated Machine Learning models and transform primary data into useful insights and boosted intelligence. We offer robust, high-quality analysis and research oriented novel solutions optimized for the problem in hand.
Gain market advantage from our holistic approach towards the solution of the problem. Biomedical engineers together with medical doctors and business consultants will provide a tailored solution to the needs of the client.
Connect and gain access to our services from everywhere in the world exploiting our secure cloud infrastructure. Trust our cloud protocols for storing your sensitive data.
Utilize our services through a user-friendly dashboard co-designed by the front-end developers, the medical doctors and the clients and customized based on client’s needs.
Comply with national and organizational GDPR regulations. We offer federated ML approaches to avoid redundant exchange of data.
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
Quantification of brain tumours
Quantification of epileptic lesions
Mapping of the brain activation
Functional and effective connectivity
Effective connectivity and tractography
Denoising, artifact removal
|Stroke is the leading cause of serious long-term disability and the fifth leading cause of death worldwide. Non contrast Computed Tomography (CT) of the brain remains the mainstay of medical imaging in the setting of an acute stroke since it is the gold standard for detecting hemorrhage. Machine learning tools along CT images can be used both for quantifying the extent of the stroke (with similar methods used in brain tumor quantification in MRI), while also for predicting which patients will develop Symptomatic Intracranial Haemorrhage (SICH).|
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)
Seizure detection, stages of anxiety detection
Seizure detection, examination of electrical activity of muscles, determination of muscle fatigue
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.
|Machine learning 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. With high-quality data, subtle nuances and correlations can be accurately captured and high-fidelity predictive systems can be built. Real World Data (RWD) denotes patient level data which is collected from different sources as part of routine healthcare practice, 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
Prediction of the spread, outcome of an infectious disease