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Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making
Beom Joon Kim1,2, Kairan Zhu3, Wu Qiu4
1Department of Neurology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
Background:
The presence of diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch was used to determine eligibility for intravenous thrombolysis in clinical trials. However, due to the restricted availability of MRI and the ambiguity of image assessment, it is not widely implemented in clinical practice.
Methods:
A total of 222 acute ischemic stroke patients underwent non-contrast computed tomography (NCCT), DWI, and FLAIR within 1 h of one another. Human experts manually segmented ischemic lesions on DWI and FLAIR images and independently graded the presence of DWI-FLAIR mismatch. Deep learning (DL) models based on the nnU-net architecture were developed to predict ischemic lesions visible on DWI and FLAIR images using NCCT images. Inexperienced neurologists evaluated the DWI-FLAIR mismatch on NCCT images without and with the model's results.
Results:
The mean age of included subjects was 71.8 ± 12.8 years, 123 (55%) were male, and the baseline NIHSS score was a median of 11 [IQR, 6-18]. All images were taken in the following order: NCCT - DWI - FLAIR, starting after a median of 139 [81-326] min after the time of the last known well. Intravenous thrombolysis was administered in 120 patients (54%) after NCCT. The DL model's prediction on NCCT images revealed a Dice coefficient and volume correlation of 39.1% and 0.76 for DWI lesions and 18.9% and 0.61 for FLAIR lesions. In the subgroup with 15 mL or greater lesion volume, the evaluation of DWI-FLAIR mismatch from NCCT by inexperienced neurologists improved in accuracy (from 0.537 to 0.610) and AUC-ROC (from 0.493 to 0.613).
Conclusion:
The DWI-FLAIR mismatch may be reckoned using NCCT images through advanced artificial intelligence techniques.
Insights
Artificial intelligence can predict diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch using non-contrast computed tomography (NCCT) scans. This deep learning approach aids in assessing eligibility for acute ischemic stroke treatment.
Area of Science:
- Radiology
- Artificial Intelligence
- Neurology
Background:
- Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch is crucial for determining eligibility for intravenous thrombolysis in acute ischemic stroke.
- Current clinical practice is limited by MRI availability and subjective image interpretation.
Purpose of the Study:
- To develop and evaluate deep learning (DL) models for predicting DWI-FLAIR mismatch using non-contrast computed tomography (NCCT) images.
- To assess the impact of DL-assisted DWI-FLAIR mismatch evaluation on the diagnostic accuracy of inexperienced neurologists.
Main Methods:
- 222 acute ischemic stroke patients underwent NCCT, DWI, and FLAIR imaging.
- Deep learning models (nnU-net architecture) were trained to predict DWI and FLAIR lesions from NCCT images.
- Inexperienced neurologists assessed DWI-FLAIR mismatch on NCCT, with and without DL model assistance.
Main Results:
- The DL model achieved a Dice coefficient of 39.1% and volume correlation of 0.76 for DWI lesions, and 18.9% and 0.61 for FLAIR lesions.
- Assistance from the DL model improved DWI-FLAIR mismatch evaluation accuracy (AUC-ROC from 0.493 to 0.613) by inexperienced neurologists, particularly for larger lesions (≥15 mL).
Conclusions:
- Advanced artificial intelligence techniques can effectively estimate DWI-FLAIR mismatch using NCCT images.
- DL-based prediction holds promise for improving the accessibility and accuracy of stroke treatment eligibility assessment.
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