Machine Learning Model Accurately Predicts Interstitial Lung Abnormalities on CT Scans, claims study
Researchers have found that machine learning models can correctly predict the probability of interstitial lung abnormalities (ILAs) from computed tomography scans, a breakthrough that could help in devising early detection and treatment strategies for lung diseases. Despite the clinical importance of ILAs, there has been a difficulty in their automated identification. This study was a development and performance test of machine learning models in predicting the probabilities of ILA based on CT images from a large dataset provided by the Boston Lung Cancer Study. The study was recently published in the journal Radiology by Akinori H. and colleagues. Interstitial lung abnormalities, often incidentally found in computed tomography scans, emerge due to their significant clinical implications, including a relation to higher susceptibility for pulmonary fibrosis and other diseases affecting the lungs. However, fully automated detection of ILAs has not yet been realized, and therefore the...