At the NIH Pi Day celebration I gave a lightning talk on applying deep learning to histology images. A video of the event is now available at NIH Videocast.
During the one hour event there were presentations on ten different projects. I was the second speaker and began at 8:48.
I am using deep learning to identify glomeruli in kidney biopsies. When we are unsure about the specific type of kidney disease a patient has we take a small biopsy to look at the kidney. It is often differences in the glomeruli that define the type of disease. Pathologists study the biopsy to define the type of kidney disease. These skilled pathologists spend significant time locating the glomeruli. A machine can do this simple step. The pathologist can then focus on the harder disease identification task.
Over the past several months I have been working on a method for measuring fibrosis. I published an article based on this work in Physiological Reports. The journal has started a podcast series and this article was in the second episode. I discussed the article with Physiological Reports editor Tom Kleyman. I embedded the full podcast below and the article is available on the journal website
Fibrosis is an important step in healing an injury. The scar that might form after a cut is an example of normal physiological fibrosis. Unfortunately fibrosis is not always benign. Pathological fibrosis is the deposition of excessive fibrous tissue. This interferes with healing and the function of the organ. Fibrosis is a dominant feature in the histological damage seen in many diseases. Examples include idiopathic pulmonary fibrosis, liver cirrhosis, and Crohn's disease. My interest is in chronic kidney disease.
The advanced stages of kidney disease requires treatment by dialysis or kidney transplantation. Both of these options have many negative consequences. Treatments to slow the development of fibrosis would help many patients.
Accurate measurements of fibrosis are vital in treatment development. The sirius red method in this article is more reproducible and precise. I hope it will contribute to getting better treatment options to the patients that need them.