Tuberculosis is an infectious disease that kills almost two million people worldwide each year, even though the disease can be identified on a simple chest X-ray and treated with antibiotics. One major challenge is that TB-prevalent areas typically lack the radiologists needed to screen and diagnose the disease.
New artificial intelligence models may help. Researchers from the Thomas Jefferson University Hospital in Pennsylvania have developed and tested an artificial intelligence model to accurately identify tuberculosis from chest X-rays, such as the TB-positive scan shown at right.
Lakhani performed the retrospective study with his colleague Baskaran Sundaram, MD, a TJUH cardiothoracic radiologist. They obtained 1007 chest X-rays of patients with and without active TB from publically available datasets. The data were split into three categories: training (685 patients), validation (172 patients) and test (150 patients).
The training dataset was used to teach two artificial intelligence models — AlexNet and GoogLeNet — to analyze the chest X-ray data and classify the patients as having TB or being healthy. These existing deep learning models had already been pre-trained with everyday nonmedical images on ImageNet. Once the models were trained, the validation dataset was used to select the best-performing model and then the test dataset was used to assess its accuracy.
The researchers got the best performance using an ensemble of AlexNet and GoogLeNet that statistically combined the probability scores for both artificial intelligence models — with a net accuracy of 96 percent.
The authors explain that the workflow of combining artificial intelligence and human diagnosis could work well in TB-prevalent regions, where an automated method could interpret most cases and only the ambiguous cases would be sent to a radiologist.
The researchers plan to further improve their artificial intelligence models with more training cases and other artificial intelligence algorithms, and then they hope to apply it in community
“The relatively high accuracy of the deep learning models is exciting,” Lakhani said in the release. “The applicability for TB is important because it’s a condition for which we have treatment options. It’s a problem that we can solve.”
This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.