Philips, and the PAMM foundation, the largest pathology laboratory in the South-East Brabant region of the Netherlands, today announced plans to jointly transform all of PAMM’s tissue analysis operations to fully digital diagnostics. The PAMM foundation will also contribute to the development of smart image-analysis software to help identify and quantify cancer cells in tissue samples. Through their collaboration, PAMM and Philips aim to further improve the efficiency and quality of diagnostics, ultimately leading to better patient care.
Pathologists play a crucial role in the detection and diagnosis of a broad range of diseases, including cancer. The increasing number of cancer cases, the aging population, and rapid advances in personalized medicine have resulted in significant increases in the complexity of pathological diagnostics and the workload imposed on pathologists. Digitally imaging tissue samples that a pathologist would normally need to view under a microscope makes it possible for pathologist to view and assess tissue samples without having the physical samples in front of them. For example, it enables samples to be viewed in any location and by more than one person at a time, allowing pathologists to quickly consult a (sub) specialist in a different part of the world, or conveniently share images during multi-disciplinary team meetings without the need to physically transport tissue samples. Such benefits allow pathologists to work more efficiently and effectively.
In addition to improving its operational efficiency, PAMM also expects to achieve a significant improvement in the quality of diagnoses via the use of image-recognition software. PAMM works with Philips for this. This software aims to help pathologists to better assess biomarkers that highlight changes in tissue proteins and DNA.
As part of its transformation to digital pathology, PAMM plans to set up a digital archive, using the Philips IntelliSite Pathology Solution Ultra Fast Scanner to digitalize the 400,000 tissue samples it processes each year. This digital archive will become an important source of information for the development of future image-recognition software.