Malone Seed Grant Program

2024 Seed Grants Now Open

The Malone Center for Engineering in Healthcare is pleased to announce a call for proposals for 2024 as part of its seed funding initiative to support research, expand collaboration among center affiliates, and create new external partnerships.

The Malone Center Seed Grant Program aims to assist faculty and research staff with the development of innovative, collaborative proposals that will advance the center’s mission. We encourage proposals that will create broader resources within the center for all our affiliates’ use. We also welcome proposals that help place the principal investigator and research team in a position to receive additional, external funding from federal agencies, foundations, and/or industry.

How to apply

Seed Grant Guidelines (PDF)

This grant is now open. Applications to the Malone Center Seed Grant Program may be submitted at any time until the submission deadline to [email protected].


Proposal Submission: March 29, 2024

Awards Announced: May 15, 2024
Award Start Date: June 1, 2024


Please send any questions related to proposal preparation and submission and/or requests for additional information to the Malone Center’s Program Manager, Kelly Vermandere.

2021 Seed Grant Projects

While the prevalence of autism spectrum disorder, or ASD, has significant societal and economic impacts, early intensive behavioral interventions have proven effective in enhancing social and communicative behaviors impaired by ASD. Cumulative research findings and prior works demonstrate the measurable short-term benefits of robot-mediated behavioral intervention for autism. However, most prior research on robot-mediated intervention consists of controlled laboratory studies spanning short periods of time; the longitudinal effects of robot-mediated intervention are unclear, necessitating large-scale, long-term field studies.

As part of our end goal to advance the science of robot-mediated intervention for children with ASD, this project aims to develop and pilot test a robot-mediated social play system to be used in long-term in-home deployments. This seed effort will allow us to build the engineering infrastructure and collect the preliminary data necessary to win external funding for investigating the longitudinal effects of in-home robot-mediated intervention for children with ASD.


Research Team: 
  • Chien-Ming Huang
  • Amy Keefer
  • Chung Hyuk Park

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

The rapid growth of robotic minimally invasive surgery (RAMIS) and the lack of standardized training curricula necessitates the development of control interfaces that adapt to a surgeon’s skill proficiency.

This project will develop a confidence-based shared-control framework for RAMIS that arbitrates control based on real-time assessments of surgeon performance and investigate its utility.


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


2020 Seed Grant Projects

Proliferative sickle cell retinopathy (PSR) is a vision-threatening complication of sickle cell disease, the most common inherited blood disorder in the United States. The hallmark feature of PSR visible on a retinal exam is known as sea fan neovascularization. In a previous collaboration, the team found that a deep learning system can detect sea fan neovascularization characteristic of PSR from ultra-widefield fundus photographs with high sensitivity and specificity. The system was trained from 1,131 de-identified ultra-widefield color fundus photographs, retrospectively collected from 181 Wilmer patients with sickle cell disease but never previously treated for PSR.

Based on these encouraging results, ophthalmologist Adrienne Scott and computer scientists Mathias Unberath and Craig Jones aim to build prospective and additional retrospective databases of fundus photographs to validate and refine their existing algorithm. Automated screening technology can offer a rapid, reproducible, and relatively cost-effective approach to screening patients with sickle cell disease for early signs of PRS. Ultimately, the teams hopes to deploy this technology in places where patients may not have access to high-quality healthcare, and to identify those who most need referral to a retina specialist for preventative treatment.

Research Team: 
  • Mathias Unberath
  • Craig Jones
  • Adrienne Scott

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

2019 Seed Grant Projects

Glaucoma is the second leading cause of blindness globally, with approximately 79.6 million people expected to be affected by the disease by 2020.  A small subset of glaucoma patients will experience rapid glaucoma worsening that, if untreated, can quickly lead to complete vision loss. Identifying these rapid progressors early is critical for ophthalmologists to perform interventions that can preserve a patient’s eyesight.

Mathias Unberath, MCEH assistant research professor, is teaming up with Hopkins ophthalmologist Jithin Yohannan to explore a data-driven solution to this problem. Unberath will develop machine learning algorithms that can accurately identify patients at risk for undergoing rapid glaucoma progression, based on multimodal clinical data that is acquired as part of routine glaucoma care at Wilmer Eye Institute. To this end, the team plans to aggregate functional, structural and clinical information – automated visual field tests, optical coherence tomography images, and clinical parameters such as intraocular pressure – to better understand the factors that contribute to rapid progression.

Research Team: 
  • Mathias Unberath
  • Jithin Yohannan

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

2018 Seed Grant Projects

Recent studies show that hand dexterity and strength recover mostly within the first three months after stroke, and that these two critical components of hand function are supported by separate biological systems. However, in most stroke patients, dexterous hand function does not fully recover with the standard rehabilitation therapy. These findings strongly suggest the need for intense rehabilitation targeting hand dexterity in the early post-stroke stages. Currently, the stroke rehabilitation field is missing effective tools to meet this need.

Researchers from the Malone Center and the  Brain, Learning, Animation, and Movement Laboratory (BLAM) are conducting pilot studies on the Hand Articulation Neurotraining Device (HAND), a portable rehabilitation device for the hand, which can be used in various clinical settings, starting within the hospital, immediately after brain injury, and after discharge, in the patient’s home.

  • The team has developed sensitive assessment paradigms using the device to detect hand dexterity impairment in finger individuation and precision pinch in 3D. Preliminary results show that these key features of dexterity have differential patterns of impairments. Papers reporting these results are in preparation. The device is now in a pilot clinical trial and an NIH proposal has been submitted.
Research Team: 
  • Jing Xu

The Cardiac Surgery Division at the Johns Hopkins Hospital is a highly complex organization that involves multi-disciplinary services and care teams.  The Cardiac Surgery team is routinely monitoring their quality of care, forming and testing hypotheses as to which parts of the care system need to be more efficient. This project establishes the foundation to investigate and pinpoint inefficiencies in cardiac surgery care by analyzing comprehensive data sets taken from a large cohort of patients. Researchers and clinicians from the Malone Center and Cardiac Surgery team up to validate and investigate four potential inefficiencies in the Cardiac Surgery care system: (1) Prediction of length of stay, (2) Early diagnosis of Heparin Induced Thrombocytopenia (HIT), (3) Measuring deviation from routine daily pathways, and (4) Prediction of Bounce-back patients to ICU. The larger goal of the project is to explore new data-driven approaches to improve the quality and efficiency of care for cardiac surgery patients.

Research Team: 
  • Narges Ahmidi

The overall goal of the SpineCloud project is to gain understanding and predictive power of the factors underlying spine surgery outcomes – particularly the unacceptably broad variability associated with spinal fusion. Such capability will yield evidence-based insight and decision support to improve patient selection, enable more optimal surgical planning, identify perioperative sentinel factors, and help to guide the most effective post-operative, therapeutic, and rehabilitative process on a patient-specific basis. The SpineCloud project aims to curate a database consisting of patient demographic data, image and specific anatomy, surgical procedures, and pathologies. By developing this data-intensive approach to future spine surgeries, SpineCloud will provide more favorable and consistent outcomes for patients.

  • The SpineCloud project provided the first reported method that is able to use spine morphology (analyzed from multi-modality images) as a predictor of surgical outcome.
  • A paper summarizing initial results from the SpineCloud project has been submitted for review in the Journal of Medical Imaging (under review).
  • An invention disclosure (D15678) on novel aspects of SpineCloud was submitted to JHTV.
  • Conference presentation was given at the ASNR 2019 conference, the University of Michigan “Practical Big Data” conference, and the AAPM 2019 annual meeting.
Research Team: 
  • Jeffery Siewerdsen