2025 Seed Grants Now Open

Research Team: 

• Mathias Unberath
• Axel Krieger
• Gina Adrales

The goal of this work is to modernize the management of fall risks in hospitalized patients by developing a data-powered, automated tool called FALLPRO (Fall Prevention and Risk Optimization). FALLPRO will utilize electronic health record data for the purpose of dynamic fall risk assessment and prevention. Recognizing that current manual and subjective methods are time-consuming and prone to errors, FALLPRO will use a data-driven approach, leveraging machine learning methods and mixed-integer optimization models. By accurately assessing risk factors and optimizing prevention strategies on a daily basis, the tool seeks to enhance nursing efficiency, improve patient safety, and reduce fall-related injuries and healthcare costs.

The interdisciplinary team behind this initiative plans to develop a comprehensive model that not only advances the precision of fall risk evaluations, but also prioritizes individualized interventions, ensuring more targeted and effective fall prevention. Validating this tool against existing practices, this project will enable a significant step forward in patient care, aiming to reduce nursing load, improve standardization, and explore adaptation to various care environments and populations, such as older adults or individuals with impaired cognition.

Research Team: 

• Kimia Ghobadi
• Erik Hoyer

Low-flow vascular malformations, or LFVMs, are the most common vascular anomalies seen in patients, but to date, there are no standardized clinical or radiologic methods published to objectively determine treatment outcomes after therapy. Quantifying the local and global changes occurring in LFVMs remains a challenging but essential task both for clinicians and researchers developing and assessing new therapies. MRI has long been proposed as an objective outcome measure for LFVMs; however, it has not been clearly determined which changes on a multiparametric MRI best correlate with clinical outcomes. Small studies have reported using both manual and semi-automated MRI-based segmentation for the evaluation of LFVMs, and demonstrated that post-treatment changes in lesion volume and contrast enhancement significantly correlated with clinical outcomes. But these semi-automated techniques are slow, subjective, and do not allow for the throughput needed for widespread use.

The goal of this proposal is to develop a fully automated MRI-based 3D neural network segmentation algorithm that allows for fast, reproducible, and quantitative assessment of treatment outcomes for LFVMs and to determine the relationship between volume and signal intensity findings before and after treatment.

Research Team: 

• Craig Jones
• Clifford R. Weiss
• Adham Khalil

Research Team: 

• Jeremy D. Brown
• Axel Krieger
• Gina Adrales

Despite indications that SARS-CoV-2 infection appears to cause less severe disease in children than in adults, severe complications – including prolonged hospitalizations, invasive ventilation, and death – do occur in this population. Early identification, isolation and treatment of pediatric patients at risk for these severe complications will improve outcomes.

The project will develop predictive models with two objectives: identify pediatric patients (ages 0 to 21 years) admitted to emergency departments at Johns Hopkins who are at high risk for SARS-CoV-2 complications; and determine the probability that asymptomatic pediatric patients undergoing routine screening for elective procedures are positive for SARS-CoV-2. The research team will use the Super Learner approach, a technique in which multiple machine learning algorithms and regression models can be combined into an ensemble algorithm to produce a predictive model.

Research Team: 

• Oluwakemi Badaki-Makun
• Scott Levin
• Jeremiah Hinson

Deep learning (DL) models have demonstrated their potential to diagnose disease on medical imaging with performance approaching or even exceeding that of a human radiologist. But deep learning models suffer from a fundamental problem: they often adopt unwanted biases found within the data on which they’re trained. Ultimately, these biases can lead to problems such as discrimination and underserving or underperforming on underrepresented populations.

To make these models more equitable, the research team will quantify the impact of chest x-ray datasets with imbalances in demographics, such as sex and race/ethnicity, on the development of biased DL models for medical imaging diagnosis. Based on these findings,  the team will develop and validate methods to train DL models resistant to demographic imbalances. Ultimately, this work will set the foundation for unbiased DL algorithms which can be safely deployed in diverse patient populations.

Research Team: 

• Paul Yi
• Jeremias Sulam
• Ilya Shpitser
• Cheng Ting Lin

Neovascular age-related macular degeneration (NVAMD) is the leading cause of central vision loss in US adults over 50 years of age. Although optical coherence tomography (OCT) has revolutionized the diagnosis and management of this disease, OCT analysis still has limitations; commonly-used OCT metrics, including central subfield thickness (CST), are not good predictors of functional outcomes like visual acuity, or the clarity and sharpness of one’s vision.

A promising strategy to create a clinical tool that can predict function based on OCT images is to harness the power of deep learning. The research team plans to develop an alternative OCT metric by training a deep learning algorithm that more closely correlates structure (OCT image) with visual acuity. Such a meaningful metric will provide a useful secondary endpoint in clinical trials for new NVAMD therapies, and provide clinicians a new tool for tracking progression/improvement in NVAMD patients.

Research Team: 

• Craig Jones
• T.Y Alvin Liu
• Peter Campochiaro
• Anam Akhlaq

According to the National Brain Tumor Society (1), an estimated 700,000 people in the United States are living with a primary brain tumor and about 80,000 people in the U.S. are diagnosed with a primary brain tumor each year. As a result, large numbers of people must undergo brain surgery every year. Current state-of-art for presurgical, noninvasive planning of tumor removal is to perform blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI).

During scanning, a patient performs a specific task to localize brain functional regions related to the task. A decrease in the relative concentration of deoxyhemoglobin in active cortex reduces the T2/T2* shortening effects of deoxyhemoglobin with resultant net increase in the BOLD signal in activated areas. fMRI necessitates image acquisition methods, which are sensitive to changes in T2* and T2, have sufficient spatial resolution to cover the entire brain, and have sufficient temporal resolution to detect changes in BOLD signal associated with specifics tasks. The spatial resolutions of clinical fMRI presurgical mapping are usually between 8 mm3 and 64 mm3 (i.e. 2–4 mm along each voxel dimension). Given that human cortex is about 3 mm thick, acquiring functional mapping signals with the highest possible fidelity to the underlying neuronal processes is critical for presurgical mapping. With the advent of ultrahigh magnetic field human MRI scanners, i.e., 7T and above, recent studies have probed into depth-dependent cortical mapping with high resolution functional MRI of human brain. The purpose of this study, therefore, is to enhance resolution of 3T fMRI using artificial intelligence on 7T fMRI for the overall goal of depth-dependent cortical mapping in clinical fMRI.

Research Team: 

• Shruti Agarwal
• Haris Sair

More than 300,000 patients in the United States undergo cardiac surgery each year, and substantial morbidity and mortality are common. Acute kidney injury(AKI) is one of the most frequent post-surgical complications, with estimates ranging from 20-50% depending on the procedure. AKI is strongly correlated with reduced quality of life, progression to renal-replacement therapy (i.e. dialysis),and mortality. The pathophysiology of AKI is complex and poorly understood, but likely includes hemodynamic changes (low cardiac output, blood pressure), mechanical factors (emboli, venous congestion), and other mechanisms (vasoconstriction, drugs, inflammation)—many of which are highly modifiable. One of the key challenges to developing preventative strategies for AKI is our inability to monitor intraoperative kidney function.

This overarching goal of this proposal is to develop an automated platform to continuously forecast the risk for AKI based on real-time intraoperative physiological data. This information would allow clinicians to optimize intraoperative variables on-the-fly to reduce the likelihood of kidney injury.

Research Team: 

• Archana Venkataraman

Radiation therapy is a clinically popular treatment for cancer, and knowledge-based radiation therapy treatment planning is becoming an integral part of clinical practice.

However, there is considerable variation in clinical guidance—which identify the feasibility constraints for the plans—of treatment plans among different institutes, hospitals, and oncologists.

In this proposed study, we develop a novel methodology that combines machine learning and inverse optimization to infer utility functions and hidden constraints of a black-box decision-making process. We will evaluate the method in the context of personalized radiation therapy treatment planning, building on a large dataset of historical radiation therapy treatment plans that were clinically approved and delivered. Using a data-driven approach, we apply our iterative approach of machine learning and inverse optimization on the radiation therapy plans to find the underlying criteria that the oncologists considered when approving the plans. Once these criteria are known, a forward optimization model can be used to design personalized treatment plans automatically.

Research Team: 

• Kimia Ghobadi

Older adults are highly susceptible to postoperative complications following major surgery. A growing body of research shows that physical conditioning prior to surgery, often called “prehabilitation,” can provide significant benefit to patients, including reduced complications, shorter recoveries, and lower costs. However, tools to measure the effectiveness of prehabilitation are currently lacking.

Anton Dahbura, MCEH associate research professor, is looking to wearable technology for new insights on the benefits of prehabilitation. His team has developed a platform that can collect data from wearable activity monitors and display feedback to patients on their smartphones. By continuously monitoring patient activity with wearable devices, the team hopes to apply analytics to determine the true relationship between pre-surgery activity levels and surgical outcomes.

The team’s initial study will focus on older adults (over the age of 65) who are preparing for major abdominal colorectal surgery.

Research Team: 

• Anton Dahbura
• Erik Hoyer
• Daniel Young