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.
MRI
Denoising and artifact removal
Image registration
Quantification of brain tumours
Quantification of epileptic lesions
fMRI
Mapping of the brain activation
Functional and effective connectivity
Diffusion MRI
Effective connectivity and tractography
Ultrasound
Automated Cardiac Ultrasound Assistance Tool
CT
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.
EEG
Seizure detection, seizure prediction
Sleep stages classification
Brain-Computer Interface (BCI)
ECG
Arrhythmia detection
Seizure detection, stages of anxiety detection
EMG
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.
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
Sepsis prediction
Prediction of the spread, outcome of an infectious disease