TB can be detected on smartphone using new model: Study

Po-Chih Kuo, from Massachusetts Institute of Technology, and colleagues described how TBShoNet provides a method to develop an algorithm that can be deployed on phones to assist healthcare providers in areas where radiologists and high-resolution digital images are unavailable. 

The researchers used three public datasets for model pre-training, transferring and evaluation. They pretrained the neural network on a database containing 250,044 chest X-rays with 14 pulmonary labels, which did not include TB. The model was then recalibrated for chest X-ray photographs by using simulation methods to augment the dataset. Finally, the team built TBShoNet by connecting the pretrained model to an additional 2-layer neural network trained on augmented chest X-ray images (50 TB; 80 normal). 

To test the model's performance, the researcher used 662 chest X-ray photographs (336 TB; 326 normal) taken by five different phones. TBShoNet demonstrated an AUC of 0.89 for TB detection. With optimal cut-off, its sensitivity and specificity for TB classification were 81% and 84%, respectively.

"We need to extend the opportunities around medical artificial intelligence to resource limited settings," says Kuo.

Reference:

AI Model Aids in TB Detection via Smartphone: Research news from the Radiological Society of North America 106th Scientific Assembly and AnnualMeeting (RSNA 2020) – Nov. 29, 2020