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AI Case Study

Researchers at UCLA develop a convolutional neural networks system that detects prostate cancer as well as experienced radiologists in detecting prostate cancer

Researchers at UCLA used MRi scans to train a convolutional neural network (CNN) to identify prostate cancer. When tested, FocalNet was able to read MRIs with a 80.5% accuracy, compared to the 83.9% accuracy of radiologists with at least 10 years of experience.



Healthcare Providers And Services

Project Overview

"UCLA researchers have developed a new artificial intelligence system to help radiologists improve their ability to diagnose prostate cancer. The system, called FocalNet, helps identify and predict the aggressiveness of the disease evaluating magnetic resonance imaging, or MRI, scans, and it does so with nearly the same level of accuracy as experienced radiologists.

FocalNet is an artificial neural network that uses an algorithm that comprises more than a million trainable variables; it was developed by the UCLA researchers. The team trained the system by having it analyze MRI scans of 417 men with prostate cancer; scans were fed into the system so that it could learn to assess and classify tumors in a consistent way and have it compare the results to the actual pathology specimen. Researchers compared the artificial intelligence system’s results with readings by UCLA radiologists who had more than 10 years of experience.

The research suggests that an artificial intelligence system could save time and potentially provide diagnostic guidance to less-experienced radiologists."

Reported Results

"In tests, FocalNet was 80.5 percent accurate in reading MRIs, while radiologists with at least 10 years of experience were 83.9 percent accurate."



R And D

Core Research And Development


"Radiologists use MRI to detect and assess the aggressiveness of malignant prostate tumors. However, it typically takes practicing on thousands of scans to learn how to accurately determine whether a tumor is cancerous or benign and to accurately estimate the grade of the cancer. In addition, many hospitals do not have the resources to implement the highly specialized training required for detecting cancer from MRIs."



MRI scans

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