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Medicine

Electronic nose identifies brain tumors in real time by "sniffing" the tissues

The device is able to "smell" the surgical smoke produced by the cuts of the electric scalpel, distinguishing the type of tumor and tissue (and helping the neurosurgeon in real time)

June 15 2019
Gianluca RiccioGianluca Riccio
⚪ 2 minutes
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In neurosurgery, the use of electronic scalpels has now become a norm: burning tissue means dispersing its molecules, literally sending them up in smoke.

The method developed by researchers at the University of Tampere in Finland includes a device capable of "sniffing" the surgical smoke produced by cuts in real time, thus analyzing the composition of the tissues being operated on.

The study was published in the Journal of Neurosurgery.

"In current clinical practice the process is much more complex: a very small sample of the tumor is analyzed after having it taken, frozen and sent to a pathologist during the operation," says the researcher Ilkka Haapala.

Today the pathologist produces an analysis after observation under the microscope and telephones the operating room to provide the response. It already looks like something a hundred years ago.

The article continues after the related links

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"Our new method gives the possibility to analyze the tissues in real time and on many points of the tumor, with the advantage of being able to adopt a device that connects with the instrumentation already present," explains Haapala.

How the "electronic nose" works

The fumes produced by the electronic scalpel pass through an electric field produced by the device: each type of smoke (and therefore of fabric) has a precise distribution of ions in an electric field. In other words, it has its own olfactory imprint.

A machine learning system is connected to the "nose" which helps it to refine the analyzes as it collects data: the accuracy of the system in classifying benign and malignant tumors was already 83% after the first uses, and in a short time (after about 700 analyzes carried out) it is now 94%.

When we say "having an infallible nose".

Tags: diagnosesMachine learningelectronic noseneurosurgerytumors

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