To train, validate and test their model, the researchers used a dataset of 540 subjects with multiphase CT angiography exams. Of these, 270 patients had confirmed presence of an LVO, while the other 270 were LVO-negative. Each CT scan underwent a series of pre-processing steps.
First, the researchers standardized their scans through isotropic resampling (voxel resolution of 1 mm3), image resizing (500 x 500 pixels) and intensity normalizing (between 0 and 1). Then, they employed a vessel segmentation algorithm to increase the images’ signal-to-noise ratio. Finally, to further enhance the blood vessels, they selected 40 most-cranial axial slices from each subject and produced a single 2D image through a technique called maximal intensity projection.
To evaluate the diagnostic performance of the proposed deep-learning model, the group decided to experiment with seven training strategies. In each strategy, the team used a different subset of the multiphase CT angiography data: each phase alone, or various combinations (phases 1 and 2, phases 2 and 3, phases 1 and 3, and all three phases together).
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The group used a dataset of 62 patients (31 LVO-positive and 31 LVO-negative) to test their seven strategies. The model performed best when using all three phases as the input, where it achieved a sensitivity of 100% (31 out of 31) and a specificity of 77% (24 out of 31). Moreover, combining the peak arterial (phase 1) and late venous (phase 3), or the peak venous (phase 2) and late venous (phase 3), resulted in significantly better models than using just single-phase CT angiography.
The model gave good predictions across patient demographics, multiple institutions and scanners. Also, it detected both anterior and posterior circulation occlusions. “[Our model] could function as a tool to prioritize the review of patients with potential LVO by radiologists and clinicians in the emergency setting,” the researchers conclude. For future work, the group aims to evaluate the model’s clinical utility by testing it in a real-time emergency setting.