Detect diseases early and accurately

Tapping into the potential of medical data may save patients’ lives without altering traditional care paths.

The healthcare sector continues to favour two fundamental concepts: prevention and efficiency. To that end, accurate early detection of medical conditions represents one of its major goals, as conditions detected early can be treated with much greater efficiency.

Non-Invasive Procedure

A vast amount of physiological data is generated (typically by the regular panoply of medical tests such as blood examination) and stored by medical institutions every day. Standard clinical interpretation of this data to diagnose conditions has only begun to benefit from the knowledge and abilities derived from a more systematic analytical approach.
The unique opportunity presented to the sector of a systematic analysis of medical data may lead to the addition a key non-invasive tools in the successful care of patients.

Utilising today’s unused potential

The PIT-M3D System, boasting a machine learning architecture, may provide clinical solutions to healthcare practitioners: by helping improve medical workflows and cutting healthcare expenditure.
The System’s medical conditions diagnosis and diseases early detection capabilities offer valuable insights on patients’ conditions and highlight patterns, which may often remain attentionless and unflagged as no direct and subject-oriented medical hints may be observed.

Striving to ensure clinical efficiency

PIT-M3D’s vision is to help promote a clinical environment, which focusses on early detection and allows for the implementation of various strategies to maximise health benefits for patients, whilst reducing costs for healthcare providers.
Our system ensures to detect all insignificant medical hints for its calculations and ultimately, flag unforeseen pathological arrangements.
More importantly, we present vital, far-reaching insights to physiological patient profiles which helps pushing clinical efficiency and success rates to the max.