What is computational pathology?

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Computational pathology uses the power of deep machine learning, image analytics and big data integration to enhance diagnostic precision and transform the next generation of pathologists.

Philips is leading the way in computational pathology research, delivering novel technologies for image analytics, which are fully embedded within their digital pathology scanning, storage and display solution.

Computational Pathology Empowering Pathologists to Support Improved Patient Care

We're in a really exciting time in pathology

Need for computational pathology ¹

We are moving rapidly into an era where next-generation pathology is becoming a reality with the advent of digital pathology. Paradigm shifts are being witnessed in cancer care with precision medicine and personalized treatments increasing by the day.
Pathologists are absolutely central to this dream of personalized medicine. It is at the desk of the pathologist that first clinical decisions regarding the patient are made and that will continue to be the case.

Increasing workload ²

70%

projected increase in new cancer cases within the next two decades

20%

of pathologists work overtime weekly or have to outsource services

Shortage of pathologists ³

60%

of active physicians in pathology are age 55 or older

10%

decrease in active physicians in pathology in 2008 - 2013

Expert opinions

A transformation in computational pathology ⁴

The evolution of deep learning and the improvement in accuracy for image pattern recognition has been staggering in the last few years. Everything from bio-metric and security, voice recognition and intelligent advertising, to driver-less cars is powered by deep learning technologies.

Next up is pathology. We believe that digital pathology and machine learning combined, could empower pathologists with new tools, and help drive improvements in workflow and diagnostic precision in both discovery and diagnosis.

Recent convergence of technologies, together with Philips innovation in computational pathology will help us to transform pathology together.

View our Computational Pathology products below

Clinical algorithms⁵

We have an extensive roadmap for the delivery of CE-IVD approved algorithms to support clinical decision making in multiple tissues. Initially we are providing digital image analysis applications in support of the pathologist for ER, PR, HER2 and Ki-67 clinical assessments.

Related products

Philips IntelliSite Pathology Solutions

The digital pathology solution for primary diagnosis with proven technology. A richer experience in your daily work.

Learn more

Resources

Related articles

Publication: Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

Publication: Deep learning algorithm diagnoses skin cancer as well as seasoned dermatologists

Publication: Artificial Intelligence Achieves Near-Human Performance in Diagnosing Breast Cancer

Louis DN et al, Computational pathology: an emerging definition, Arch Pathol Lab Med, Volume 138, Issue 9, 2014
Cancer Facts & Figures – US figures – AAMC 2014
Sara Bainbridge et al. (2016). Available: http://www.cancerresearchuk.org/sites/default/files/testing_times_to_come_nov_16_cruk.pdf.
Louis DN et al, Computational Pathology. A Path Ahead, Arch Pathol Lab Med, Vol 140, 2016
Visiopharm is the legal manufacturer of the breast IHC applications (HER2 APP, Breast Cancer; ER APP, Breast Cancer; PR APP, Breast Cancer; Ki67 APP, Breast Cancer). The applications are CE-IVD for Europe and Research Use Only for United States and Rest of the world.
The Philips IntelliSite Pathology Solution has obtained market access clearance as IVD for primary diagnosis in approx. 50 countries, such as EEA (European Economic Area), USA, Canada, Japan, South Korea, and other countries in Asia, Middle East and South America.

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