Posted 21st November 2018 by Jane Williams
Digital pathology is based on creating a digital replica of the glass slide, called whole slide image (WSI). This image is then viewed in a computer screen, which eliminates the need for using a microscope. Can you imagine working in a glassless environment, with better ergonomics and being able to immediately find the slide you need?
Posted 5th November 2018 by Jane Williams
The histological assessment of human tissue has emerged as a key challenge for the detection and treatment of cancer. Many tissue sections have to be processed in order to find those that contain cancer, which can be a timely and costly process. Similarly, procedures such as immunohistochemical scoring can be problematic for cases such as ER, PR, and HER2.
Posted 10th September 2018 by Jane Williams
At the recent Digital Pathology and AI Congress, Rebecca Calder and Daniel Stevens presented their research in the poster entitled: Preliminary Studies in the Use of the Foldscope Paper Microscope for Diagnostic Analysis of Crystals in Urine: Issues in the Analysis of Liquid Samples and Potential Applications in Low Budget/Low Tech Regions of the World. You can view the poster here.
Dr. Zev Leifer, who oversaw the project, describes the rationale.
Posted 31st August 2018 by Jane Williams
“There’s nothing wrong with the microscope.”
“Our Information Systems won’t integrate with other technology.”
“Change is hard.”
These are a couple of the concerns pathologists have about digital pathology. But as they learn more, pathologists are finding that their apprehension about going digital is holding them back from reaping significant benefits. They are also discovering that old methods can’t compete against today’s digital solutions.
Posted 15th August 2018 by Jane Williams
At the Digital Pathology and AI Conference in New York City, it was interesting to consider the different beliefs represented about Artificial Intelligence.
Posted 6th August 2018 by Jane Williams
Machine learning is already prevalent in many industries and most pathologists are unaware how accessible machine learning is and how it can be used to augment their work or research. Applications include decision support, image analytics, process improvement, disease diagnosis and prognosis.
Posted 29th June 2018 by Jane Williams
To use a baseball analogy – there have been more games won with singles than with home runs. Home runs, when they happen, are spectacular, but they are few and far between. The journeyman single, while not the stuff of legend, gets the job done, and happens frequently. When it comes to the practical application of artificial intelligence in digital pathology, the analogy holds. The home run is machine diagnosis – replacement of the pathologist – the holy grail. The single represents baby steps which improve pathology practice, right up until the technology reaches its inevitable maturity.
Posted 1st June 2018 by Jane Williams
Pathologists identify and interpret the changes that characterise diseases in cells and tissues, both for the studying/understanding disease processes in general and obtaining clinically relevant information for individual patients. Historically, by examining biopsy specimens, pathologists identified whether a lesion was neoplastic, inflammatory, or some other broad category. As medicine evolved, the task evolved into identifying more specific classifications. For example, if it was not sufficient to make the diagnosis of cancer; it was necessary to identify the specific subtype and grade of cancer in order to inform treatment decisions that were becoming increasingly sophisticated.