PR: Pathology 4.0 – inveox automates laboratory processes


Mix-ups, contamination and sample loss – most errors in pathology happen when specimen are received. Countless samples arrive daily at the laboratory, while the registration process is very monotonous. As a result, the work is inefficient. The start-up company inveox has now developed a system that automates the processes in the pathology laboratory, thus making them more efficient.

In this interview, Maria Driesel describes the challenges of pathology laboratory processes, explains how an automated system can simplify these procedures and defines the role machine learning should play in all this.

Ms. Driesel, what is the current pathology laboratory setup?

Maria Driesel: The majority of the processes in a pathology laboratory are handled manually. Typically, a simple container with a screw lid accompanied by a handwritten test request is used to transport the biopsies. That means, the ordering physician first collects the tissue sample, places it in the screw cap container that contains formalin – a stabilizing solution – and identifies the patient and analysis request on a handwritten form. All this is then sent to the lab. The problem is that labs receive hundreds or even thousands of samples each day. All of them must be registered and prepared. To do this, the data must first be collected and typed up. Subsequently, the samples are sorted, assigned and repackaged into a so-called biopsy cassette, which in turn requires another label. The samples cannot be processed in the laboratory until these steps have been completed. This is a very monotonous and time-consuming process due to the vast number of samples. Oftentimes, this can result in mix-ups, contamination or even the loss of samples. According to experts, these types of errors occur in one to fifteen percent of cases and – in the worst-case scenario – can lead to misdiagnosis or the wrong medical treatment. ...

Read the complete interview with Maria Driesel at!


Image copyright: Astrid Eckert

Article last time updated on 22.08.2018.

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