Purpose:
To validate a Machine Learning algorithm able to identify LVO on NCCT.
Methods:
Patients with suspected acute stroke who underwent NCCT+CT Angiography (CTA) from two comprehensive stroke centers were included. Patients with intracranial haemorrhage were excluded. Two experienced radiologists identified the presence of LVO on CTA (NR-CTA) tagging the clot location and manually segmenting the clot. Acute ischemia and clot signs on NCCT were also depicted with assistance of the CTA clot location. With this information a deep learning system was used to create an algorithm (Deepstroke) to identify and locate the presence/absence of acute ischaemia and clot signs in NCCT. Deepstroke image output was used to train a binary classifier to determine LVO on NCCT. Cross-validation was performed in a stratified 5-fold of the data, including deep learning training. We also studied the effect on Deepstroke accuracy when adding the patients NIHSS and time from onset to the model (Deepstroke+).
Results:
The data cohort included 1354 patients, 724 (53%) with LVO by NR-CTA. The accuracy of Deepstroke to identify LVO had an AUC of 0.81 (sensitivity 0.85; specificity 0.49, PPV 0.66, NPV 0.74), and improved combined with NIHSS and time from symptom onset to AUC 0.88 (sensitivity 0.87, specificity 0.68, PPV 0.76, NPV 0.82). Deepstroke performed better on larger occlusions (Table). Among patients identified as LVO by Deepstroke+ only 19% showed no findings on NR-CTA. The agreement in LVO detection between NR-CTA and Deepstroke+ was 0.78 (Deepstroke was 0.68). Process time per patient was below 120s.
Conclusions:
In patients with suspected acute stroke, Deepstroke identified LVO in NCCT with a high correlation with radiologist readings of CTAs. Deepstroke could reduce the need to perform CTA, generate alarms and increase the efficiency of patients transfers in the acute management in stroke networks. Deepstroke accuracy will improve as more cases are added to the training set.
