Artificial Intelligence may help in diagnosing Tuberoculosis

The best performing artificial intelligence model was a combination of the AlexNet and GoogLeNet, with a net accuracy of 96 percent. "The relatively high accuracy of the deep learning models is exciting. The applicability for TB is important because it's a condition for which we have treatment options. It's a problem that can be solved," Dr. Lakhani shared. The two DCNN models had disagreement in 13 of the 150 test cases. For these 13 cases, the scientists evaluated a workflow where an expert radiologist was able to interpret the images, accurately diagnosing 100 percent of the cases.