  Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment
decisions, and prognosis. However, image and disease variability hinder the
development of generalizable AI algorithms with clinical value. We address this
gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic
Stroke Lesion Segmentation (ISLES) challenge. ISLES'22 provided 400 patient
scans with ischemic stroke from various medical centers, facilitating the
development of a wide range of cutting-edge segmentation algorithms by the
research community. Through collaboration with leading teams, we combined
top-performing algorithms into an ensemble model that overcomes the limitations
of individual solutions. Our ensemble model achieved superior ischemic lesion
detection and segmentation accuracy on our internal test set compared to
individual algorithms. This accuracy generalized well across diverse image and
disease variables. Furthermore, the model excelled in extracting clinical
biomarkers. Notably, in a Turing-like test, neuroradiologists consistently
preferred the algorithm's segmentations over manual expert efforts,
highlighting increased comprehensiveness and precision. Validation using a
real-world external dataset (N=1686) confirmed the model's generalizability.
The algorithm's outputs also demonstrated strong correlations with clinical
scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived
results, underlining its clinical relevance. This study offers two key
findings. First, we present an ensemble algorithm
(https://github.com/Tabrisrei/ISLES22_Ensemble) that detects and segments
ischemic stroke lesions on DWI across diverse scenarios on par with expert
(neuro)radiologists. Second, we show the potential for biomedical challenge
outputs to extend beyond the challenge's initial objectives, demonstrating
their real-world clinical applicability.
