Developing a big, diverse, and open source brain–computer interface dataset for artificial intelligence and machine learning applications (Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data)

Developing a big, diverse, and open source brain–computer interface dataset for artificial intelligence and machine learning applications (Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data) is sponsored by NIH Office of Data Science Strategy (ODSS). This administrative supplement aims to improve the AI/ML-readiness of data generated through NIH-funded research, specifically mentioning the development of a large, diverse, and open-source brain-computer interface (BCI) dataset for AI and machine learning applications.

Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data | Data Science at NIH Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data About the Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data Artificial intelligence and machine learning (AI/ML) are a collection of data-driven technologies with the potential to significantly advance biomedical research.

The National Institutes of Health (NIH) makes a wealth of biomedical data available and reusable to research communities however, not all of these data are able to be used efficiently and effectively by AI/ML applications.

To address these issues, National Institutes of Health (NIH) Office of Data Science Strategy (ODSS) announced “ Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data ” on March 6, 2023. ODSS has also posted Frequently Asked Questions (FAQs) for this funding opportunity.

The goal of this notice is to make the data generated through NIH-funded research AI/ML-ready and shared through repositories, knowledgebases, or other data sharing resources. 2023: NOT-OD-23-082 Expires May 17, 2023. 2022: NOT-OD-22-067 Expired March 18, 2022.

( Frequently Asked Questions ) 2021: NOT-OD-21-094 Expired May 27, 2021. Thirty-four awards were made in 2023 to principal investigators at 15 different institutions across the country. Awardee projects and their descriptions are available below.

View NOT-OD-23-082 Awardees NOT-OD-23-082 Award Recipients Principal Investigator Institution Project Title NIH IC ALI, AMINA ABUBAKAR AGA KHAN UNIVERSITY (KENYA) Improving AI/ML-readiness of Synthetic Data in a Resource-Constrained Setting FIC ARNAOUT, RIMA UNIVERSITY OF CALIFORNIA, SAN FRANCISCO ENRICHing NIH Imaging Datasets to Prepare them for Machine Learning NHLBI AWAD, ISSAM A UNIVERSITY OF CHICAGO Biomarkers of Cerebral Cavernous Angioma with Symptomatic Hemorrhage (CASH) - Supplemental NINDS BELL, MICHELLE L YALE UNIVERSITY Containerizing tasks to ensure robust AI/ML data curation pipelines to estimate environmental disparities in the rural south NIMHD BLETZ, JULIE A SAGE BIONETWORKS Assuring AI/ML-readiness of digital pathology in diverse existing and emerging multi-omic datasets through quality control workflows NCI CHICHOM, ALAIN MEFIRE UNIVERSITY OF BUEA Harnessing Data Science to Promote Equity in Injury and Surgery for Africa FIC CHINCHILLI, VERNON M PENNSYLVANIA STATE UNIV HERSHEY MED CTR Data Coordinating Center for the Type 1 Diabetes in Acute Pancreatitis Consortium NIDDK CHIU, YU-CHIAO UNIVERSITY OF PITTSBURGH AT PITTSBURGH Enhancing AI-readiness of multi-omics data for cancer pharmacogenomics NCI CHOI, SUNG WON UNIVERSITY OF MICHIGAN AT ANN ARBOR Patient-Oriented Research and Mentoring in Hematopoietic Cell Transplantation Supplement NHLBI CHUNARA, RUMI NEW YORK UNIVERSITY NYU-Moi Data Science for Social Determinants Training Program FIC COOK, DIANE JOYCE WASHINGTON STATE UNIVERSITY Crowdsourcing Labels and Explanations to Build More Robust, Explainable AI/ML Activity Models NIA DING, MINGZHOU UNIVERSITY OF FLORIDA Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging NIMH ERICKSON, LOREN D UNIVERSITY OF VIRGINIA IgE antibody responses to the oligosaccharide galactose-alpha-1,3-galactose (alpha-gal) in murine and human atherosclerosis NIAID FRIED-OKEN, MELANIE OREGON HEALTH & SCIENCE UNIVERSITY An AI/ML-ready closed loop BCI simulation framework NIDCD GUO, JINGCHUAN UNIVERSITY OF FLORIDA Supplement of NIDDK R01 newer GLDs and Clinical Outcomes NIDDK HIRSCH, KAREN G STANFORD UNIVERSITY PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) NINDS HSU, WILLIAM UNIVERSITY OF CALIFORNIA LOS ANGELES An AI/ML-ready Dataset for Investigating the Effect of Variations in CT Acquisition and Reconstruction NIBIB IM, HYUNGSOON MASSACHUSETTS GENERAL HOSPITAL Development of plasmon-enhanced biosensing for multiplexed profiling of extracellular vesicles NIGMS LARSON, MARY JO BRANDEIS UNIVERSITY Trajectories of non-pharmacologic and opioid health services for pain management in association with military readiness and health status outcomes: SUPIC renewal NCCIH MACCARINI, PAOLO FRANCESCO DUKE UNIVERSITY Development of AI/ML-ready shared repository for parametric multiphysics modeling datasets: standardization for predictive modeling of selective brain cooling after traumatic injury NINDS MOKUAU, NOREEN UNIVERSITY OF HAWAII AT MANOA Processing Multiomic Datasets for Improved AI/ML-readiness in Congenital Heart Disease Research NIMHD NGUYEN, THU UNIV OF MARYLAND, COLLEGE PARK Risk and strength: determining the impact of area-level racial bias and protective factors on birth outcomes NIMHD ORDOVAS, JOSE M.

TUFTS UNIVERSITY BOSTON Social Stressors, Epigenetics and Health Status in Underrepresented minorities NIMHD PANAGEAS, KATHERINE S SLOAN-KETTERING INST CAN RESEARCH MATCHES: Making Telehealth Delivery of Cancer Care at Home Effective and Safe - Addressing missing data in the MATCHES study to improve ML/AI readiness NCI PAYNE, SAMUEL H BRIGHAM YOUNG UNIVERSITY Creating AI/ML-ready data for single cell proteomics NIGMS REHM, HEIDI L BROAD INSTITUTE, INC. ClinGen AI Data Delivery Supplement NHGRI SETTE, ALESSANDRO LA JOLLA INSTITUTE FOR IMMUNOLOGY THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE NCI SHEFFIELD, NATHAN UNIVERSITY OF VIRGINIA Novel methods for large-scale genomic interval comparison NHGRI TEMPANY, CLARE M BRIGHAM AND WOMEN'S HOSPITAL Generation and Dissemination of Enhanced AI/ML-ready Prostate Cancer Imaging Datasets for Public Use NIBIB ULRICH, CORNELIA M UNIVERSITY OF UTAH Harmonizing genomic, transcriptomic, and drug response data across pre-clinical models of cancer to support machine learning approaches for personalized cancer therapy selection NCI WOLMARK, NORMAN NRG ONCOLOGY FOUNDATION, INC. NRG Oncology Network Group Operations Center NCI ZHANG, WEI WAKE FOREST UNIVERSITY HEALTH SCIENCES Developing unbiased AI/Deep learning pipelines to strengthen lung cancer health disparities research NCI ZHAO, ZHONGMING UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithms NLM Thirty-six awards were made in 2022 to principal investigators at 33 different institutions across the country.

Awardee projects and their descriptions are available below. View NOT-OD-22-067 Awardees NOT-OD-22-067 Award Recipients Principal Investigator Institution Project Title NIH IC Adams, Meredith C. B.

Wake Forest University Health Sciences Wake Forest IMPOWR Dissemination Education and Coordination Center (IDEA-CC) Using artificial intelligence to transform NIH HEAL Initiative clinical trial data to increase scientific impact. The HEAL Data Ecosystem is working to collect data across its projects and networks to meet FAIR (Findable, Accessible, Interoperable, Reusable) data standards.

This administrative supplement builds on the mission of the NIH HEAL IMPOWR network to blend existing and future chronic pain (CP) and opioid use disorder (OUD) data. The proposed work will significantly deepen and augment approaches to FAIR principles in CP and OUD data for both the HEAL network and larger NIH research community.

The overall objective of this project is to move CP and OUD data one step closer to FAIR by leveraging existing datasets and developing tools for new projects. The general hypothesis of the project is that leveraging existing CP & OUD data and collecting new data using ML/AI data quality standards will accelerate the impact of the HEAL Data Ecosystem.

The aims of the project are to transform existing datasets to be ML/AI ready, and to adapt tools to support ML/AI readiness for existing and prospectively collected HEAL common data elements (CDE). The expected outcome of this project is data optimization pipelines and tools to support the goal of ML/AI ready data.

The results of this project will provide a strong basis for further development of the HEAL Data Ecosystem, helping to bring diverse data sources together and meet FAIR data standards.

NIDA Alkalay, Ron N Beth Israel Deaconess Medical Center Curating musculoskeletal CT data to enable the development of AI/ML approaches for analysis of clinical CT in patients with metastatic spinal disease This project aimed to provide computer tomography image data for developing deep-learning methods to analyze disease progression and fracture risk in patients with metastatic spine disease based on clinical and image-based classifications.

Patients with metastatic spine disease to a high risk of pathologic vertebral fracture (PVF). Up to 50% of these patients suffer neurological deficits with further complications that may be fatal. Prediction of PVF risk is a critical clinical need for managing these patients.

Segmentation of vertebral anatomy, bone properties, and individual spinal musculature cross-sectional area from clinical CT imaging is fundamental for developing precise, patient-specific diagnostics of PVF risk. Such segmentation faces unique challenges due to the cancer-mediated alteration in skeletal tissues' radiological appearance.

Deep learning (DL) methods will speed and standardize the critical segmentation step, permitting analysis of larger datasets and promoting new DL analysis for improved insight into the drivers of PVF risk in patients with metastatic spine disease.

For this project, titled; “Curating musculoskeletal CT data to enable the development of AI/ML approaches for analysis of clinical CT in patients with metastatic spinal disease”, our work aimed to establish a curated, publicly accessible computer tomography imaging dataset from 140 metastatic spine disease patients treated with radiotherapy, imaged as part of our parent study, titled; “Predicting Fracture Risk in Patients Treated with Radiotherapy for Spinal Metastatic Disease” (AR075964).

For this purpose, we have established manual segmentation of each vertebral level, including delineation for lesion type and fractures. Based on this data, we successfully developed a testbed deep learning model for 1) segmentation of thoracic and lumbar vertebrae and 2) Complete set of thoracic and abdominal muscles, demonstrating the applicability of the curated data set and the application for developing DL methods.

Based on this effort, the curated images and associated delineation data from this cohort have been accepted and are undergoing final submission to the NIH Cancer Imaging Archive (TCIA). Integrating DL systems within our approach forms an important step in changing the patient management paradigm from reactive to data-driven proactive management to prevent PVF events and critically reduce bias in patient management.

NIAMS Bateman, Alex European Molecular Biology Laboratory UniProt - Protein sequence and function embeddings for AI/Machine Learning readiness We will incorporate new representations of proteins and their functions that will help unlock the power of AI/ML for biomedical researchers using UniProt.

UniProt represents a wealth of protein related information that is very diverse and yet highly structured, covering all living organisms from microbes to humans. It is an ideal source of information for AI/ML and indeed has already been used to help train essential tools such as AlphaFold that uses deep learning to generate 3D structural models for nearly all proteins.

This project further harnesses AI/ML techniques to enhance UniProt, particularly using sequence embeddings. Sequence embeddings provide a representation of protein sequence that can be used for a broad range of AI/ML tasks. We are making these embeddings and AI/ML models available to researchers, saving community compute time and enabling data science.

We are testing embeddings for critical tasks in UniProt such as clustering sequences to enable users to search faster. We are also exploring using embeddings for enzymatic reactions to enhance our ability to identify novel data in the literature - and improve the diversity of catalytic reactions captured in UniProt. To better understand the needs of the AI/ML community, we have held a workshop to engage leaders in the field.

We have learned about new directions and opportunities we can benefit from as well as the challenges they face in their own work that we can help with by improving our data provision.

Collectively, we will scale up protein functional annotation with AI/ML-assisted techniques, organize the growing sequence space with AI/ML-enabled sequence clustering to sustain the sequence computing, and collaborate with the AI/ML research communities to develop new solutions to benefit the broad user community of the UniProt resources. NHGRI Bertagnolli, Monica M.

Brigham And Women's Hospital A-STOR Cancer Clinical Trial Artificial Intelligence & Machine Learning Readiness 'Big data' generated through clinical trials offers an incredible opportunity to lead to new cancer discoveries and through this project, we will evaluate optimal approaches to apply artificial intelligence/machine learning algorithms to advance our understanding of cancer diagnosis, treatment, and management.

Cancer clinical trials are facilitating an explosion of biomedical data, including complex clinical data, diverse genomic data, pathologic image data, high-dimensional molecular characterization, and clinical imaging data among others.

Maximizing analyses of samples and data collected through clinical trials and the rationale is well understood – comprehensive molecular profiling should accelerate our goal of ‘precision cancer medicine’, especially when applied to the randomized clinical trials that incorporate current and emergently effective treatments.

Among cancer clinical trials, many high impact trials are designed and conducted by National Cancer Institute’s (NCI) National Clinical Trial Network (NCT), including the focus of this study – Alliance for Clinical Trials in Oncology. However, present barriers impede cancer clinical trials from unlocking the full potential of these datasets.

Currently, omics data generated from trials are largely decentralized: data are housed at a variety of sites, analyses take place locally, and other researchers do not have access until public deposition of data on repositories.

Further, analyses vary widely in bioinformatics methods, including choice of tools, dependencies, file formats, parameterizations, data quality filtering thresholds, and other workflow elements, which makes integration across groups challenging.

In this proposal, we are expanding the Alliance Standardized Translational Omics Resource (A-STOR) to realize the full potential of artificial intelligence (AI)/machine learning (ML) modeling for cancer clinical trials.

Specifically, we will: 1) rapidly expand A-STOR to host data from over a dozen existing or ongoing Alliance clinical trials and optimize infrastructure for AI/ML analyses; 2) develop a unified clinical and adverse event (AE) data dictionary to facilitate clinical data harmonization; and 3) complete an already-approved pooled multi-modal ML-based predictor as a pilot study.

Progress to date includes co-localization of digital pathology and genomic data, harmonization of clinical data dictionary, and initiation of a pilot project to interrogate the breast cancer tumor immune microenvironment through cutting edge AI-based approaches and best-in-class RNA-based immune signature approaches.

NCI Bhatt, Tanvi University Of Illinois At Chicago Perturbation training for enhancing stability and limb support control for fall-risk reduction among stroke survivors Democratizing data-driven approaches in quantitative gait analysis to enhance effectiveness of assessment and treatment approaches for stroke rehabilitation by creating harmonized gait data repository and scientific workflow library.

NICHD funds several clinical trials targeting novel balance and gait interventions. Yet there is a gap in the field pertaining to data sharing, accessibility and utilization. Machine learning models based on gait data could accurately identify pathological gait patterns, classify motor disorder, predict the need for ankle foot arthrosis, and assess rehabilitation status for stroke survivors.

However, there is a lack of publicly available data repositories for clinicians and researchers, and computational expertise is required for the use of the data. Those barriers greatly limit the development of data-driven approaches for health. Therefore, this project aims to take a step towards the democratization of gait analysis to empower a broad range of stakeholders.

To create the gait data repository (Aim 1), we will evaluate and enable metadata through data wrangling and harmonization capabilities following FAIR data principles (findability, accessibility, interoperability and reusability) before building the repository.

To uncover data issues (i.e., data loss, and data artifacts), Aim 2 will focus on data analytics leveraging harmonized data sets from Aim 1 to create scientific workflows for biomechanical data utilization (data visualization, cleaning, and analysis functionalities). To support cleaning and transformation tasks in gait data, a set of open-source libraries will be created for data cleaning, analysis, and visualization.

Our computational libraries will be made available to researchers through an easy-to-use visual interface that allows them to query and visualize the data. Additionally, a centralized website (GaitPortal) will be designed to make data and libraries publicly available. Lastly, we will demonstrate an initial use for the transformed data by developing a fall risk predictive model based on the time series gait data (Aim 3).

The demonstration code containing all basic and advanced functions will be provided for other researchers to enable customization of the code for their specific purpose(s). These user-friendly tools would improve data checking, cleaning, and analysis by cutting manual data analysis time by at least 25% and reducing overall financial cost for researchers and clinicians.

Additionally, the association between clinical measures and gait data could guide the development of an objective function to evaluate balance status and training effects, which will help the researchers and clinicians to identify individualized impairments in gait performance and balance control. This personalized insight can benefit the development of tailored rehabilitation strategies for people with hemiparetic stroke.

Clinicians can also monitor the rehabilitation progress based on real-time or post-processed feedback from biomechanical assessments, enhancing the precision and efficacy of interventions in the field of stroke rehabilitation. NICHD Casey, Joan A Columbia University Health Sciences Approaches for AI/ML Readiness for Wildfire Exposures Predicting wildfire PM2.

5 using AI/ML techniques "Artificial intelligence (AI) and machine learning (ML) models are subject to biases inherent in data used to train them. Efforts to mitigate bias have focused largely on study designs, and implementation of fair quality checks.

However, as focus is placed on generalizability of these models, it is critical to contextualize representativeness of study data used for modeling with respect to the population on which insights are intended to be used. Currently, research emphasizes descriptions of study cohorts, highlighting on whom analyses were performed.

However, summary statistics cannot provide the granularity needed to identify potential bias brought on by diverse populations and limited sample sizes. This project focuses on improving artificial intelligence/machine learning (AI/ML)-readiness of a wide range of environmental data sources used to predict wildfire fine particulate matter (PM2. 5) exposure.

Developing models to predict wildfire PM2. 5 exposure is crucial as nearly 70% of the U.S. population is exposed to wildfire smoke each year, with 30% experiencing more severe levels of exposure. PM2.

5 exposure has been associated with increased risk of respiratory care-related medical encounters and mortality among older individuals. As part of our parent R01 we are estimating the risk of incident and worsening mild cognitive impairment (MCI) and Alzheimer’s disease and related dementias (ADRD) associated with wildfire PM2. 5.

exposure. To do so, we are developing models to predict daily exposure to wildfire-specific PM2. 5 levels using a two-stage ML approach.

Stage one relies on a Bayesian machine learning algorithm to integrate multiple existing PM2. 5 prediction models to assign ambient PM2. 5 exposures.

Stage two uses NOAA’s Hazard Mapping System to identify areas exposed to wildfire smoke plumes. We use the smoke plume information combined with statistical techniques to isolate daily estimates of wildfire PM2. 5 from non-wildfire PM2.

5 levels. The data sources needed to predict PM2. 5 and wildfire PM2.

5 are disparate, not very accessible, and unfriendly to AI/ML applications. These datasets include weather variables from multiple sources, satellite smoke plums from multiple sources, air pollution monitor data, national land use variables, topographical data, and others. Although the data is rich and publicly available through US agencies, acquiring it and preparing it for analysis presents a significant investment by any researcher.

All datasets come in different spatial and temporal resolutions that need to be resolved for them to be merged. Additional processing is also needed to handle potential spurious information due to the periodicity of satellites, monitors, and cloud cover. With this administrative supplement, our goals are to improve the data processing for the vast and wide range of data sources by developing reproducible pipelines.

For example, one source of PM2. 5 predictions is obtained from the Atmospheric Composition Analysis Group, and we provide reproducible code to process and aggregate this netCDF data by applying a downscaling rasterization strategy using TIGER/Line shapefiles (see https://github. com/NSAPH-Data-Processing/pm25_components_randall_martin for more details).

We will also annotate and document the data and ensuring computational scalability. In addition, we will deposit the processed data to a public data repository, Harvard Dataverse, and include a data demonstration to further disseminate the work.

NIA Chen, Shigang University of Florida Supplement: SCH: Enabling Data Outsourcing and Sharing for AI-powered Parkinson's Research Improving AI-readiness of outsourced medical data by addressing the data privacy issue through randomization and noise addition Artificial intelligence holds the promise of transforming data-driven biomedical research for more accurate diagnosis, better treatment, and lower cost.

In the meantime, modern digital technologies make it much easier to collect information from patients in large scale. While “big” medical data offers unprecedented opportunities for building deep-learning artificial neural network (ANN) models to advance the research of complex diseases such as Parkinson’s disease (PD), it also presents unique challenges to patient data privacy.

The task of training and continuously refining ANN models with data from tens of thousands of patients, each with numerous attributes and images, is computation-intensive and time-consuming. Outsourcing computation and data to the cloud is a viable solution. However, the problem of performing the ANN learning operations in the cloud, without the risk of leaking any patient data from their sources, remains open to date.

We propose to develop novel data masking technologies based on randomized orthogonal transformation to enable AI-computation outsourcing and data sharing. The proposed research includes (1) experimental studies of training ANN models with data masking for PD prediction and Parkinsonism diagnosis, and (2) theoretical development on data privacy, inference accuracy, and model performance.

This supplement project expands the research into a new dimension of differential privacy by incorporating randomized orthogonal transformation and noise addition into the process of data masking. Differential privacy is a rigorously defined and widely adopted model, which provides a quantitative measure for privacy loss in data release. This project consists of a theoretical aim and an experimental aim.

The theoretical aim is to develop a new method of achieving differential privacy for data outsourcing that minimizes noise addition with the help of randomized orthogonal transformation. The experimental aim is to use the new method to produce sharable PD (Parkinson’s Disease) data sets under the protection of differential privacy and ready for machine learning studies.

The outcome of this research, with the new method of outsourcing data with differential privacy, is expected to have a broader impact beyond PD research in advancing the theory and implementation of cloud-based medical studies.

NLM Devinsky, Orrin New York University School of Medicine Machine learning approaches for improving EEG data utility in SUDEP research Leveraging AI and machine learning to identify EEG biomarkers of sudden unexpected depth in epilepsy (SUDEP) The proposed supplemental project is built upon the base of augmented datasets and new AI/ML techniques.

Our research team consists of SUDEP and AI/ML experts with complementary expertise, who are uniquely qualified to develop innovative analytic tools for EEG data AI/ML-readiness. First, we have explored new EEG feature extraction/engineering techniques and validated the efficacy with ML approaches. To date, our preliminary results have achieved a median AUC (area under curve) of 0.

87 in classification between SUDEP and living epilepsy patient controls---a significant improvement from our previously reported result (median AUC of 0. 77, Frontiers in Neurology, 2022). Second, we are developing explainable ML models to enhance result interpretation.

Third, we are developing and employing data augmentation techniques to improve the consistency of labeled EEG data from both SUDEP cases and living epilepsy patient controls. Finally, we will validate existing and newly developed ML methods on newly collected SUDEP and control samples at multiple sites.

Overall, this project will complement and enrich the research aims in our parent grant, and promote research rigor, transparency and reproducibility. Accomplishing these research goals will maximize the data utility and improve AI/ML-readiness in epilepsy research.

NINDS Ellisman, Mark H University of California, San Diego 3D Reconstruction and Analysis of Alzheimers Patient Biopsy Samples to Map and Quantify Hallmarks of Pathogenesis and Vulnerability This project will develop software for normalizing the signal-to-noise ratio, resolution, and contrast of acquired 3D electron microscopic datasets to facilitate use of artificial intelligence and machine learning algorithms for automatic volume segmentation.

This administrative supplement is advancing tools and methodology for the normalization of large-scale, 3D electron microscopic (EM) image volumes as a means to enhance the performance, reusability, and repeatability of high throughput artificial intelligence and machine learning (AI/ML) algorithms for automatic volume segmentation of brain cellular and subcellular ultrastructure.

This work is being conducted in the context of an active research project that is advancing the acquisition, processing/refinement, and dissemination of large-scale 3D EM reference data derived from a remarkable collection of legacy biopsy brain samples from patients suffering from Alzheimer’s Disease (AD) (5R01AG065549).

This active project is deeply rooted in the use of advance AI/ML technologies for delineating key ultrastructural constituents of neurons and glia exhibiting hallmarks of the progression of AD.

It is organized to comprehensively target areas associated with plaques, tangles and brain vasculature, attending to locations where existing findings suggest cell and network vulnerability and contain molecular interactions suspected by some to underlie the initiation and progression of AD.

Through this work, we are advancing the development and dissemination of fully trained neural-network models for volume segmentation to simplify (and reduce the costs associated with) community efforts to extract their own 3D geometries and associated morphometrics from this collection of AD reference data and similar repositories of neuronal 3D EM data.

With this supplemental effort, we will develop, refine and disseminate a set of tools which allow for direct feedback and standardization of primary image quality. With these tools users will be able to optimize and normalize imaging parameters at time of image acquisition.

The outcome of this work is to advance the use of transfer learning methods, facilitating repeatability and reuse of trained neural network models for scalable EM image segmentation.

NIA Friel, Kathleen Margaret Winifred Masterson Burke Medical Research Institute Targeted transcranial direct current stimulation combined with bimanual training for children with cerebral palsy A path to improving movement therapy by integrating 3D motion information of disabled individuals with current promising therapies.

In this supplement, we aim to use Deep Learning (DL) pose estimation models along with 3D depth sensing cameras to develop a cost effective, easy to use, and compact Deep Learning based markerless kinematic data acquisition (DL-KDA) system that can be applied to children with UCP. To achieve this overall goal, we must establish the accuracy and validity of the kinematic data obtained from the system.

We have developed a modular software framework for building and testing DL-KDA systems against a very precise marker-based motion capture gold standard (VICON). To date, we have collected kinematic data from: 8 additional children with UCP, 16 typically developing children, and 40 healthy adults when they were performing the Box and Blocks Test.

We have also so far extracted 2D images from 4 years (2015-2018) of previous video recordings and will begin annotating the images to retrain the optimal DL pose estimation model. Using kinematic data from healthy adults, we are studying the effects of 3D camera and DL parameters/architecture on the accuracy of the resulting kinematic data.

In parallel, using the extracted 2D images from previous recordings (2015-2018), we will begin annotating images with body joint locations for transfer learning-based retraining of DL pose estimation models. We will use this dataset to perform transfer learning and retrain DL pose estimation models.

The goal is to address the gap in existing training datasets used for most DL pose estimation models, which are not inclusive of individuals with movement disorders. Without carefully addressing this gap, potential ethical and scientific biases may arise if such pose estimation models are applied to underrepresented groups such as children with UCP.

Our Images will be transformed into ML/AI ready HDF5 datasets and published in public DL and NIH repositories. These datasets will be available for other researchers, when using or building DL pose estimation models for applications in UCP clinical research. Finally, we will collect data from an additional 12 children with UCP (in 2023) and use the retrained DL model for body pose estimation.

The performance of the retrained DL model will be statistically compared to the original DL model to verify if bias was indeed present. Validated kinematics for the UCP population, as well, will be uploaded to public DL and NIH repositories for use in future UCP research.

NICHD Fuller, Clifton David University of Texas MD Anderson Cancer Center Administrative Supplement: Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device We have developed a unique quality-curated “benchmark” imaging dataset with multiple human observer segmentations as an avenue to improve head and neck cancer therapy using advanced multiparametric imaging, which can ideally be used to assure quality and expand potentiate improved AI/ML model development through FAIR (re)use.

Radiotherapy (RT) treatment of head and neck cancer aims to deliver a therapeutic dose to cancer cells while minimizing the damage to surrounding healthy tissue. Identifying tumors that respond well to treatment and those that do not is essential to make RT more effective and reduce side effects.

Multiparametric MRI, a technique that combines anatomical with functional imaging, has proven useful in identifying early responders and radiation-resistant disease in head and neck cancer patients. These techniques could be used to adapt radiation therapy during treatment. Our parent grant aimed to develop hardware, software, and infrastructure for multiparametric MRI-guided RT for head and neck cancer patients.

In this supplement, the resulting imaging data will be curated, annotated, and made publicly available to facilitate community-driven artificial intelligence (AI) model building efforts. The proposed one-year supplement includes curation of high-quality anatomical and functional MRI sequences and corresponding clinical data for each patient.

These anonymized datasets will be made FAIR (findable, accessible, interoperable, reusable) and available for public use and will support the development of robust AI projects. The project will also initiate a series of public AI data challenges to foster novel AI innovation and solve clinically relevant RT problems.

The success of this project will enable a modernized and integrated biomedical data ecosystem for public use of RT data for AI model building. The proposed benchmark datasets will provide a foundation to achieve the long-term goal of personalized medicine for head and neck cancer patients using AI to reduce side effects while maintaining high cure rates.

This supplement will positively impact patients by enabling the characterization of malignancy for improved therapeutic intervention and downstream translational application of AI technologies.

NIDCR Grundberg, Elin Children's Mercy Hospital Contextualizing and Addressing Population-Level Bias in Social Epigenomics Study of Asthma in Childhood This study is developing novel approaches to quantify representativeness of study cohorts with respect to communities from which it was drawn and create a standardized scorecard to convey intrinsic biases that must be considered when designing analyses and interpreting generalizability of AI/ML results.

"Artificial intelligence (AI) and machine learning (ML) models are subject to biases inherent in data used to train them. Efforts to mitigate bias have focused largely on