Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

BackgroundStroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. MethodsWe developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series. It was trained on 6,657 MRI studies. All studies were labelled positive or negative for infarct (classification annotation) with 377 having the region of interest outlined (segmentation annotation). The different annotation types facilitated training on more studies while not requiring the extensive time to manually segment every study. We initially validated the model on studies sequestered from the training set. We then tested the model on studies from three clinical scenarios: consecutive stroke team activations for 6-months at the hospital that provided training data, consecutive stroke team activations for 6-months at a hospital that did not provide training data, and an international site. The model results were compared to radiologist ground truth interpretations.ResultsThe model performed better when trained on classification and segmentation annotations (area under the receiver operating curve [AUROC] 0.995 [95% CI, 0.992-0.998] and median Dice coefficient for segmentation overlap of 0.797 [IQR, 0.642-0.861]) compared to segmentation annotations alone (AUROC 0.982 [95% CI, 0.972-0.990] and Dice coefficient 0.776 [IQR, 0.584-0.857]). The model accurately identified infarcts for training hospital stroke team activations (AUROC 0.964 [95% CI, 0.943-0.982], 381 studies), non-training hospital stroke team activations (AUROC 0.981 [95% CI, 0.966-0.993], 247 studies), and at the international site (AUROC 0.998 [95% CI, 0.993-1.000], 171 studies). The model accurately segmented infarcts with Pearson correlation comparing model output and ground truth volumes between 0.968-0.986 for the three scenarios.ConclusionsAcute infarct can be accurately detected and segmented on MRI in real-world clinical scenarios using a machine learning model.