BONBID-HIE: An Open-Source MRI and Clinical Dataset for Predicting Neurocognitive Outcomes in Neonatal Hypoxic Ischemic Encephalopathy

Core Concepts

This paper introduces BONBID-HIE, an open-source dataset of MRI and clinical data from 237 neonates with hypoxic-ischemic encephalopathy (HIE), designed to facilitate the development of biomarkers for predicting neurocognitive outcomes at 2 years of age.

Abstract

• Bibliographic Information: Bao, R., & Ou, Y. (2024). BOSTON NEONATAL BRAIN INJURY DATA FOR HYPOXIC ISCHEMIC ENCEPHALOPATHY (BONBID-HIE): II. 2-YEAR NEUROCOGNITIVE OUTCOME AND NICU OUTCOME. arXiv preprint arXiv:2411.03456.
• Research Objective: To present BONBID-HIE, an open-source dataset of brain MRI and clinical data from neonates with HIE, to facilitate the development of biomarkers for predicting neurocognitive outcomes at 2 years of age.
• Methodology: This paper describes the second release of BONBID-HIE, a retrospectively collected dataset from two hospitals. The dataset includes raw and derived diffusion parameter maps from MRI scans, NICU outcomes, and 2-year neurocognitive outcomes for 237 patients. The paper details the inclusion and exclusion criteria for patient selection, data collection methods for clinical and demographic information, and MRI acquisition protocols.
• Key Findings: The BONBID-HIE dataset offers a comprehensive resource for HIE research, including detailed clinical information, MRI data, and neurodevelopmental outcomes. The dataset's diversity in terms of MRI scanners, protocols, patient demographics, and outcomes makes it valuable for developing and validating predictive biomarkers.
• Main Conclusions: BONBID-HIE provides a valuable resource for researchers to develop and validate biomarkers for predicting long-term outcomes in neonates with HIE, potentially leading to earlier interventions and improved care.
• Significance: This work directly addresses the need for reliable early biomarkers in HIE, aiming to improve prognostic accuracy and guide the development of novel therapies.
• Limitations and Future Research: The study acknowledges limitations related to retrospective data collection and the potential for missing data. Future research could focus on expanding the dataset with additional biomarkers and long-term follow-up data.

Stats

HIE affects approximately 1-5/1000 newborns globally.
30% to 50% of HIE cases result in adverse neurocognitive outcomes by two years of age.
63 ongoing HIE-related trials worldwide are investigating new therapies to supplement therapeutic hypothermia.
BONBID-HIE includes data from 237 patients.
The dataset includes MRIs from different scanners (Siemens 3T and GE 1.5T), different MRI protocols, and from patients of different races/ethnicities and ages (0-14 days postnatal age).

Quotes

"HIE is a clinical syndrome due to a lack of blood flow and oxygen to the brain."
"Despite advancements in Therapeutic Hypothermia (TH), the prognosis for many infants remains challenging, with 35%–50% suffering adverse neurocognitive outcomes by 2 years of age."
"Both issues point to a lack of a neonatal biomarker that can predict adverse 2-year outcomes."

Key Insights Distilled From

BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): II. 2-year Neurocognitive Outcome and NICU Outcome

by Rina Bao, Ya... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03456.pdf

Deeper Inquiries

How might the development of reliable biomarkers for HIE impact clinical decision-making and family counseling in the NICU setting?

The development of reliable biomarkers for Hypoxic-Ischemic Encephalopathy (HIE) holds the potential to revolutionize clinical decision-making and family counseling within the NICU setting in several ways:

Early and Accurate Prognosis: Currently, prognostication for HIE relies heavily on clinical presentation, brain imaging (MRI), and electroencephalography (EEG). However, these methods have limitations in predicting long-term neurodevelopmental outcomes. Biomarkers could provide an earlier and more accurate assessment of the severity of brain injury, allowing clinicians to tailor interventions and provide more realistic expectations to families.
Targeted Treatment Strategies: Identifying specific biomarkers associated with different HIE subtypes or responses to treatment could pave the way for personalized medicine. This means that therapies like Therapeutic Hypothermia (TH) could be optimized for individual infants, potentially improving outcomes and reducing the risk of adverse effects.
Improved Family Communication and Support: Receiving an HIE diagnosis is incredibly stressful for families. Having access to objective biomarker data can facilitate more transparent and empathetic communication between healthcare providers and parents. This can empower families to make informed decisions about their child's care and access appropriate support services earlier.
Resource Allocation and Triaging: In resource-constrained settings, reliable biomarkers could help prioritize infants who would benefit most from specialized interventions or closer monitoring. This could lead to more efficient allocation of NICU resources and potentially improve overall outcomes.
However, it's crucial to consider the ethical implications of using biomarkers, ensuring equitable access, and avoiding premature conclusions based solely on biomarker data.

Could the reliance on MRI as a primary diagnostic and prognostic tool for HIE create disparities in access to early intervention, particularly in resource-limited settings?

Yes, the reliance on MRI as a primary diagnostic and prognostic tool for HIE could exacerbate existing disparities in access to early intervention, especially in resource-limited settings. This is due to several factors:

Availability and Cost: MRI machines are expensive to acquire, maintain, and operate. In many low- and middle-income countries, access to MRI facilities is limited, particularly in rural areas. This means that infants with suspected HIE may face significant delays in diagnosis and treatment.
Skilled Personnel: Interpreting neonatal MRI scans requires specialized training and expertise. There is a global shortage of pediatric radiologists and neuroradiologists, which is even more pronounced in resource-limited settings. This lack of trained personnel can further hinder timely and accurate diagnosis.
Transportation and Logistics: Transporting critically ill newborns to distant MRI centers poses logistical challenges and can be risky for the infant. In resource-limited settings, inadequate transportation infrastructure and limited access to ambulances can create barriers to accessing timely MRI scans.
These disparities in access to MRI can have significant downstream consequences:

Delayed Diagnosis and Treatment: Infants who experience delays in receiving an MRI diagnosis may miss the critical window for interventions like TH, which is most effective when initiated within 6 hours of birth.
Inequitable Access to Early Intervention: Early intervention programs for infants with HIE, such as physical, occupational, and speech therapy, are crucial for maximizing developmental potential. However, without a timely diagnosis, infants in resource-limited settings may not be referred to these programs early enough.
To address these disparities, it's essential to explore alternative diagnostic and prognostic tools for HIE that are more accessible and affordable, such as simplified MRI protocols, portable neuroimaging technologies, and the development of reliable biomarkers.

What are the ethical considerations surrounding the use of AI and machine learning in developing predictive models for neurodevelopmental outcomes in infants?

The use of AI and machine learning in developing predictive models for neurodevelopmental outcomes in infants, while promising, raises several ethical considerations:

Data Bias and Fairness: AI algorithms are only as good as the data they are trained on. If the training data reflects existing biases in healthcare (e.g., underrepresentation of certain racial or socioeconomic groups), the resulting models may perpetuate and even amplify these biases, leading to inaccurate or unfair predictions for certain groups of infants.
Transparency and Explainability: Many AI models, particularly deep learning algorithms, are considered "black boxes" because it's difficult to understand how they arrive at their predictions. This lack of transparency can make it challenging to identify and correct biases or errors in the model's decision-making process.
Privacy and Data Security: Developing predictive models requires access to large datasets of sensitive patient information. Ensuring the privacy and security of this data is paramount, and robust data governance frameworks are needed to prevent unauthorized access or misuse.
Informed Consent and Parental Autonomy: Obtaining informed consent for the use of infant data in AI research presents unique challenges. Parents may not fully understand the implications of using their child's data for developing predictive models, and there is a risk of coercion or undue influence, especially in vulnerable populations.
Overreliance and Deskilling: There is a risk that clinicians may become overly reliant on AI predictions, potentially leading to a decline in clinical judgment and critical thinking skills. It's crucial to ensure that AI tools are used as aids to, rather than replacements for, human expertise.
Access and Equity: As with MRI, access to AI-powered predictive tools may be unequally distributed, potentially exacerbating existing disparities in healthcare. It's essential to consider strategies for ensuring equitable access to these technologies.
Addressing these ethical considerations requires a multi-pronged approach involving:

Developing Ethical Guidelines and Regulations: Clear guidelines and regulations are needed to govern the development, validation, and deployment of AI-powered predictive models in healthcare, with a focus on fairness, transparency, and accountability.
Promoting Data Diversity and Inclusivity: Efforts should be made to ensure that training datasets for AI models are diverse and representative of the populations they are intended to serve.
Fostering Interdisciplinary Collaboration: Addressing the ethical challenges of AI in healthcare requires collaboration between clinicians, data scientists, ethicists, policymakers, and patient advocates.
Prioritizing Patient and Family Engagement: Engaging patients and families in the design and implementation of AI-powered tools is crucial for ensuring that these technologies are aligned with their values and needs.