YULCOM is developing SANKOF AI, an artificial intelligence system to aid diagnosis by multivariate analysis of medical IoT equipment data

The problem that SANKOF AI will solve

In the medical field, medical imaging (MI) is widely used for the diagnosis and monitoring of numerous diseases (many types of cancers, certain gastrointestinal diseases, etc.), pregnancy monitoring, etc. However, MI equipment is expensive and expertise in clinical analysis of these images is quite rare. This has an impact on the entire health system in terms of duration, cost and effectiveness of care. The problem is general but more pronounced in vulnerable areas, remote areas and developing countries.
According to the World Health Organization, around 830 women die every day from preventable causes linked to pregnancy and childbirth. Most of these deaths (99%) occur in developing countries (Maternal mortality report – World Health Organization – WHO).

At YULCOM Technologies we are convinced that SANKOF AI which draws on medical imaging by linking it to AI contributes to solving some of the most pressing challenges facing the Canadian health system as well as other systems of global health. Indeed, the need for medical imaging diagnosis is significant in certain regions of Canada, particularly in remote or underserved regions in terms of specialized medical personnel.

Our solution

SANKOF AI is a PACS-integrated software designed to assist radiologists in their clinical workflow.

SANKOF.AI offers a paradigm that combines:
▪ Proven portable IoT equipment from IM (less expensive and very efficient) in multimodal mode
▪ AI models (Edge & Cloud) for image processing, optimization and analysis.
▪ Applications (Edge & Cloud) is a decision support system usable by clinicians and will be equipped with innovative AI models for the treatment of MI to make it possible to establish precise and/or earlier diagnoses and take related clinical decisions. Also, to better monitor and manage/treat already diagnosed diseases, and to identify patients at risk for specific diseases/conditions – which allows proactive prevention.

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