A collaboration across 29 research and healthcare agencies is putting together the world’s largest brain tumor dataset to answer the question: could artificial intelligence (AI) detect brain tumors better than doctors?
The project, led by Intel and Penn Medicine has received $1.2 million in grant funding over 3 years from the National Institutes of Health (NIH). Diagnostic technologies driven by AI and machine learning have already proven to themselves clinically in the detection of skin, breast and lung cancers. The key to establishing a robust diagnostic platform with minimal false positives or false negatives is to feed AI enough validated data for it to teach itself the difference between malignant and benign tissues.
Patient data, however, is extremely sensitive and subject to stringent privacy and security measures — making it a challenge for developers to access sufficiently large and robust datasets. To address this, the team is using a method known as federated learning, which uses encrypted data transfer and decentralized servers to securely channell patient data from participating centers.
Over 23,000 adults and 3,500 children in the United States will be diagnosed with a brain tumor this year. These masses of abnormal cells can be either cancerous or benign in nature, but both can cause severe neurological conditions due to the build up of pressure on sensitive brain tissue. Headaches, blurred vision and memory loss are among some of the early symptoms of this cancer of the nervous system.
High-tech imaging is allowing physicians to scan the brain with unprecedented clarity and the early detection of tumors is associated with better patient outcomes. In the not so distant future, AI-assisted diagnostics could be a central tool for healthcare practitioners, allowing them to draw insights from big datasets to catch tumors in their patients earlier.
Article originally published on LabRoots on 14 May 2020.