Hypoxic Ischemic Encephalopathy (HIE) is a brain injury caused by a lack of blood and oxygen supply to the brain. HIE affects 4,000,000 term-born neonates per year worldwide, with an estimated 2 billion/year cost in the US, let alone family burdens. Therefore, reducing mortality and morbidity for HIE patients remains an important public health concern. Therapeutic hypothermia (TH) was established in 2005 as a standard therapy by cooling patients to 33-34°C in the first six postnatal hours for 72 hours. However, 35–50% of the patients still experience adverse outcomes, defined as death or cognitive Bayley Scales of Infant Development by age two years. Ongoing HIE-related trials worldwide are testing whether new therapies can supplement TH and further reduce adverse outcomes. However, therapeutic innovation is slow and inconclusive, for 1) before therapy, patients at high risk of developing adverse outcomes cannot be identified; 2) after therapy, outcomes cannot be measured until age two years. Besides, public MRI data exists for hundreds of patients with brain tumors, Alzheimer’s Disease, and other diseases, fueling AI’s success in MRI-based diagnosis and prognosis of brain tumor, Alzheimer’s Disease, and other disorders. In contrast, annotated MRIs with linked clinical and bio-marker data do not exist publicly for HIE. Our previous work has collected multi-site HIE MRI data. Therefore, to fill the gap in HIE diagnosis with MRI data, target high-risk patients, increase efficiency, evaluate therapeutic effects early, and expedite therapeutic innovations, in this work, we propose to predict 2-year neurocognitive outcomes in neonates using brain MRIs by deep learning methods. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Bao Rina, Grant Ellen, Ou Yangming

Keywords: Brain MRIs, Brain injury, Hypoxic Ischemic Encephalopathy

Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ∼ 5/1000 term-born neonates. HIE lesion detection is a crucial step in clinical care of HIE. It could lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy in this population. In addition, the rise of Artificial Intelligence (AI) brings hope to objectively and accurately finding HIE lesions. With public MRI data for brain tumors, Alzheimer’s Disease, and other diseases, AI has achieved significant success in MRI-based diagnosis and prognosis of these diseases. To facilitate the early prognosis and diagnosis of HIE, in this work, we focus on HIE lesion detection with MRI data using deep learning methods. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Bao Rina, Grant Ellen, Ou Yangming

Keywords: Brain MRIs, Brain injury, Hypoxic Ischemic Encephalopathy

Hypoxic Ischemic Encephalopathy (HIE) occurs in 1-6/1000 livebirths often leading to death or neurological disability. Neonatal brain magnetic resonance imaging (MRI) is used to assess severity of brain injury and provide prognostic information in the newborn period. However, 20-50% HIE patients do not have early abnormal MRI findings detected by qualitative neuroradiologic evaluation and yet have adverse outcomes evident by 2 years of age. Our aim was to investigate whether quantitative MRI analysis can find subtle abnormalities that help improve prognostication and reduce the incidence of falsely reassuring neonatal MRI among infants with HIE. Read More

Meeting name: SPR 2020 Annual Meeting & Postgraduate Course , 2020

Authors: Vyas Rutvi, Morton Sarah, Nunes Deivid, Song Yanan, Grant Ellen, Ou Yangming

Keywords: HIE, neonates, Quantitative MRI