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James Zou, PhD, assistant professor of biomedical data science at Stanford University, discusses machine learning and the different ways oncologists are utilizing it for the management, treatment, and diagnosis of cancer.

Machine learning is being applied in both early- and late-stage disease, and aids clinicians in providing the best treatment plans and options for their patients with cancer. In this video, Zou further discusses some of the specific methods the algorithm is trained to look at.

Transcription:

0:09 | Machine learning and artificial intelligence are seeing a lot of applications in oncology. For example, in diagnosis, often the clinicians are working with different kinds of imaging data could be mammography images or CT scans. Machine learning AI algorithms can be very helpful in helping clinicians to analyze those kinds of images for them to identify or to segment relevant regions.

0:39 | There are different stages where machine learning is being applied. They will go all the way from early stages in diagnosis to later stages in terms of treatment planning and treatment recommendations. [On the] diagnosis side, we are seeing a lot of these computer vision algorithms, which is a type of AI or machine learning models that are trained to really understand and analyze different images. For example, now there are algorithms that are looking at histopathology images and slides, and then try to diagnose and predict patient outcomes based on those histology images.

1:18 | There are also algorithms that are trained to look at mammography images and try to detect tumors, legions from these mammography images as other diagnosis sites and other treatment planning sites. People also develop machine learning models that look at, for example, mutation profiles of patients, right from their somatic mutations, and then try to predict based on these mutation profiles if immunotherapy or some other treatments are likely to be a good treatment for this particular patient.

See original here:
Using Machine Learning and AI in Oncology - Targeted Oncology