A deep learning-based tuberculosis (TB) detection model called TBShoNet can detect TB on phone-captured chest X-ray photographs, according to research presented at the virtual Radiological Society of North America 106th Scientific Assembly and Annual Meeting (RSNA 2020).
An early diagnosis of TB is crucial but challenging for resource-poor countries. 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.
“We need to extend the opportunities around medical artificial intelligence to resource-limited settings,” said lead author Po-Chih Kuo, PhD, assistant professor of computer science at National Tsing Hua University in Taiwan.
This is the first study applying transfer deep learning to smartphone-captured chest X-ray photos for TB diagnosis.
Three publicly available datasets were used for model pre-training, transferring and evaluation. The neural network was pretrained 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 Xray photographs by using simulation methods to augment the dataset.
The TBShoNet model was built by connecting the pretrained model to an additional 2-layer neural network trained on augmented chest X-ray images. Then 662 chest X-ray photographs taken by five different phones (TB: 336; normal: 326) were used to test the model performance. Sensitivity and specificity for TB classification were 81% and 84%, respectively.
Along with Dr. Kuo, authors of the study are Cheng-Che Tsai, M.D., and Leo Anthony Celi, M.D.Back To Top
RSNA 2020: AI Model Uses Smartphone for TB Detection. Appl Radiol.