First-of-Its-Kind COVID-19 Lung CT Lesion Segmentation Grand Challenge Unveils Top 10 Results

The competition's AI models utilized a multi-institutional, multi-national data set provided by various public datasets that originated from patients of different ages, genders and with variable disease severity. NVIDIA provided GPUs to the top five winners as prizes, as well as supported the selection and judging process.

The top 10 AI algorithms were identified from a highly competitive field of participants who tested the data in November and December 2020. In addition to an award for the top five AI models, these winning algorithms are now available to partner with clinical institutions across the globe to further evaluate how these quantitative imaging and machine learning methods may potentially impact global public health.

"Improving COVID-19 treatment starts with a clearer understanding of the patient's disease state. However, a prior lack of global data collaboration limited clinicians in their ability to quickly and effectively understand disease severity across both adult and pediatric patients," said Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children's National, who led the Grand Challenge initiative. "By harnessing the power of AI through quantitative imaging and machine learning, these discoveries are helping clinicians better understand COVID-19 disease severity and potentially stratify and triage into appropriate treatment protocols at different stages of the disease."

"Quality annotations are a limiting factor in the development of useful AI models," said Mona Flores, M.D., Global Head of Medical AI, NVIDIA. "Using the NVIDIA COVID lesion segmentation model available on our NGC software hub, we were able to quickly label the NIH dataset, allowing radiologists to do precise annotations in record time."

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Children's National Hospital
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