Mass effect and Midline shift are most critical and timeÂ sensitive abnormalities that can be readily detected on head-CT scan. We describe development and validation of deep learning algorithms to automatically detect the mentioned abnormalities.
We labeled slices from 699 anonymized nonÂcontrast head-CT scans for the presence or absence of mass effect and midline shift in that slice. Number of scans(slices) with mass effect were 320(3143) and midline shift were 249(2074). We used these labels to train a modified ResNet18, a popular convolutional neural network to predict softmax based confidences for the presence of mass effect and midline shift in a slice. We modified the network by using two parallel fully connected(FC) layers in place of a single FC layer. The confidences at the slice-level were combined using random forest to predict the scan-level confidence for the presence of mass effect and midline shift. A separate dataset(CQ500 dataset) was collected for the validation of the algorithm. Three senior radiologists independently read each scan in this dataset. Consensus of the readers’ opinion was used as the gold standard. We used areas under receiver operating characteristics curves(AUC) to evaluate the algorithm.
CQ500 dataset contained 491 scans of which number of scans with mass effect and midline shift were 99 and 47 respectively. AUC for detecting mass effect was 0.92(95%CI 0.89-0.95) and for detecting midline shift was 0.97(95%CI 0.94-0.99).
We show that a deep learning algorithm can be trained to accurately detect mass effect and midline shift from head CT scans.