I raised a couple of questions re competition in another thread,...

I raised a couple of questions re competition in another thread, and have read through this thread and would appreciate comments if anyone feels inclined.

I'll list two parts from different recent papers. One specifically on a study using Strokefinder, and another which also mentions EMTensor. As Discowarrior pointed, inability to image was mentioned. Strangely perhaps, Dr Guo and Abbosh's work is mentioned, but not the device by name (June, 2023 paper).
Came across a couple of NSW jurisdictions mentioned on other websites re trialling Strokefinder in ambulances - going back to 2017 if I remember correctly. A fair time back - how entrenched is it and still regularly being used?

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928921/

Among other high-risk situations cerebral stroke needs to be diagnosed as soon and as accurately as possible to prioritize the patients correct, allocate timely the necessary resources and expedite treatment. Point-of-care devices aid clinical diagnosis by performing tests and measurements at the patient's bedside. This can be challenging in a crowded emergency department where time and space are rare commodities. The Strokefinder MD 100 by Medfield Diagnostics AB fulfills all the prerequisites to be considered a point-of-care device even in the busiest emergency departments. It is easy to use, portable and durable.

https://onlinelibrary.wiley.com/doi/full/10.1002/adbi.202300174

the Strokefinder can quickly distinguish between ischemia and hemorrhage in the brain. The Strokefinder has undergone some initial clinical trials in hospitals.[20] However, there is no direct imaging can be formed after detection. The EMTensor BrainScanner, on the other hand, provides more detailed brain imaging that helps diagnose stroke in the early stages of patient presentation. However, this system is characterized by a larger size, lengthy elaboration, complex structures, and a higher cost due to its extensive use of radiating elements. In 2018, Dr. Guo and Dr. Abbosh from the University of Queensland reported a microwave-based stroke detection framework.[51] The findings demonstrate an impressive classification accuracy of 88%, accompanied by a sensitivity of 91% and a specificity of 87%. Later in 2022, their team developed a new algorithm for microwave brain tomography in stroke detection. They designed an adaptive clustering DBIM (AC-DBIM), which obtains advancements in the reconstruction of size and shape and results in significant reductions in errors compared to other conventional algorithms.[52]


Very interesting tech and seems to be ticking off all the boxes in the way to commercialisation.