Cost Efficient Individual Blood Analysis Thanks to Industry 4.0 Concepts

The German company GLP Systems Ltd., based in Hamburg, is an innovative specialist in the area of information and automation systems for clinical laboratories. GLP Systems has revolutionized the sample transfer using a new approach: Similar to a Carrera race track, the sample vessels are individually moved through the laboratories and cold-storage rooms in intelligent CARs along lanes. Worldwide, lots of those systems are already being used, also in the Center of Laboratory Medicine at the Insel Hospital in Bern.

Figure 1: The pool of empty CARs allows for an efficient and automatic filling on the right lane. In the Tube Assessment Center (TAC), at the right rear of the picture, the samples are classified reliably by the SCS computer vision system and start their individual route through the laboratory.
Source: Center of Laboratory Medicine – Insel Hospital, Bern
















Incorrect analysis results are being avoided thanks to the SCS computer vision solution: The samples are being classified in purely visual terms using their shape and color only. Practically, the world wide range of vessel types is a challenge, since their characteristics oftentimes only differ slightly. Using a statistical evaluation; those samples can be reliably identified despite their variations in production batches. Since the system automatically withdraws samples with unsure classifications, confusions are hence avoided. In those rare cases, employees check on the samples manually and guarantee for the mandatory safety.

SCS Service Tool

Figure 2: The SCS Service Tool allows a detailed status analysis. Thanks to rapid prototyping using MATLAB, the agile development took place quickly in a cost efficient manner and was soon ready for use.














A service technician can quickly detect causes of uncertainties thanks to the SCS Service Tool: It analyzes and visualizes diagnostic images along with other data of the TAC. If required, a new series of images can be acquired and thus, the data pool used for the machine learning can be continually expanded. Applying this method, new types of vessels can easily be learned by the system. Moreover, new variations of known vessels can be understood better, therefore the recognition rate is steadily optimized.

Picture 3: Thanks to swarm intelligence, the CARs drive autonomously through a variety of modules (TAC, centrifuges, cap removers, analysis devices etc.) This allows for an individual and cost efficient analysis of the blood samples according to the Industry 4.0 idea.  Source: Center of Laboratory Medicine – Insel Hospital, Bern

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