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Blood tests usually tell doctors what is happening in the body at the moment the sample is taken. A new diagnostics venture wants to take a different approach: instead of relying on occasional tests, it aims to track how a person’s blood changes over time.
Algocyte Proxima, launched by computational biology researcher Dr. Hector Zenil, is being developed around the idea that repeated measurements could reveal patterns that a single blood test may miss. The goal is to move blood testing from a one-time “snapshot” toward ongoing monitoring that could eventually help doctors notice concerning changes earlier.
Looking at change, not just a single result
Traditional blood tests provide valuable information, but each test represents only one point in time. Algocyte’s approach focuses on comparing results taken at different times and examining the direction in which those measurements are moving.
Blood carries information about many processes in the body, including immune activity, inflammation and infection. The company believes combining repeated blood measurements with other information could eventually create a more detailed picture of how a person moves between health and disease.
The idea does not mean that a blood test can currently predict exactly which disease someone will develop. Rather, the proposed system is intended to identify changes that might give healthcare professionals a reason to investigate further.
What does the device measure?
According to Forbes, Algocyte Proxima is designed to measure 13 blood markers associated with a complete blood count. These include information about red blood cells, different types of white blood cells, platelets and haemoglobin. The device connects to a phone application through Bluetooth.
The company also plans to add an AI layer that could combine blood measurements with information from wearable devices, medical records and lifestyle data.
Repeated measurements could be particularly useful in situations where blood counts change during treatment or illness. The company points to cancer treatment and chronic or autoimmune diseases as possible applications. Changes in certain blood cells, for example, could potentially alert clinicians that further testing or a treatment adjustment may be needed.
The longer-term idea: a digital blood twin
Algocyte’s larger ambition is what Zenil calls a “digital blood twin.” This would be a continuously updated computer model representing how an individual’s blood measurements change over time.
The proposed model could eventually combine laboratory results with patient-reported information and other health data to simulate a person’s health trajectory. The aim would be to help identify possible problems before serious complications develop.
There are still major hurdles. AI models would need to prove that their predictions are accurate and that acting on those predictions actually improves patient outcomes. Frequent testing would also require people to participate consistently.
For now, Algocyte Proxima represents a different way of thinking about blood tests: not simply as occasional checks, but as a stream of information that might one day help healthcare become more proactive rather than waiting for illness to become obvious.
