Health Engineering

HEALTHCARE THAT’S SMARTER, FAIRER, & MORE WITHIN REACH

New healthcare solutions require unique vision and constant collaboration. That’swhy NYU Tandon is perfectly positioned to change the way we take care ofourselves and each other.

Diagnosis is a cough away

Imagine sometime in the future, you wake up with a tickle in your throat and a stuffy nose. Your first thought might be a twinge of fear — maybe it’s something more serious than a cold, or a contagious disease. But before you call out of work or cancel plans, you might as well check. So you cough into the band of your smart watch. Shortly after, you get your results: negative for Covid, negative for flu, negative for anything nastier than seasonal allergies.

Thanks to a partnership between Tandon’s researchers and Brooklyn biotech company Mirimus, what seems like science fiction is closer than you might think.

The research is led by Professor Elisa Riedo (CBE), Associate Professor Davood Shahrjerdi (ECE and Director of the Nanofabrication Cleanroom), and Dr. Giuseppe Maria de Peppo, Director of Internal Research at Mirimus, Inc.

Riedo is particularly well known for her pioneering work on thermal scanning probe lithography (tSPL), an innovative method to nanofabricate devices and materials with molecular resolution using a heated “nano-chisel.” Recent work in collaboration with Shahrjerdi and de Peppo shows that tSPL can be used for fabricating state of the art electronic circuits with atomically thin materials, as well as to sculpt, in a biocompatible

material, the exact structure of bone tissue, with features smaller than the size of a single protein — a billion times smaller than a meter.

Shahrjerdi’s research focuses on the study of new electronic materials and devices for making nano-engineered integrated systems. Previously, his research led to a new way of enhancing the performance of electrochemical microsensors used in biochemistry for the detection of biomolecules, such as dopamine, at lower concentrations than was previously possible.

Riedo and Shahrjerdi started this partnership with Mirimus at the beginning of this year. Mirimus is a local Brooklyn health biotech company making waves in the medical testing field. The team’s unique combination of expertise in nanoelectronics, nanofabrication and biomedical research makes this team a natural fit for this incredible and exciting challenge.

Now, this new partnership may result in a small, affordable device that can go beyond just testing for COVID-19 and serve as a prototype for an electronic microchip that can be embedded in your watch or smart band, and capable of monitoring a variety of human health threats — resulting in healthier workplaces and communities, and putting you at ease on those days when you wake up feeling a little under the weather.

WEARABLE TECH FOR HEALTHIER BODIES AND MINDS

Mental healthcare from your smartwatch

But wearables can do a lot more thantell if you’re physically sick. AssociateProfessor Rose Faghih ((BME) has beenworking for the last seven years on atechnology that can measure mentalactivity using electrodermal activity(EDA) — an electrical phenomenonof the skin that is influenced by brainactivity related to emotional status.Internal stresses, whether caused bypain, exhaustion, or a particularly packedschedule, can cause changes in the EDA— changes that are directly correlated tomental states.

The overarching goal — a MultimodalIntelligent Noninvasive brain state Decoderfor Wearable AdapTive Closed-looparcHitectures, or MINDWATCH, as Faghihcalls it — would act as a way to monitor awearer’s mental state, and offer nudgesthat would help them revert back to amore neutral state of mind. For example,if a person was experiencing a particularlysevere bout of work-related stress, theMINDWATCH could pick up on this andautomatically play some relaxing music.

Rose Faghih

Now Faghih — along with RafiulAmin, her former Ph.D. student — hasaccomplished a crucial task requiredfor monitoring this information. For thefirst time, they have developed a novelinference engine that can monitor brainactivity through the skin in real time withhigh scalability and accuracy. Previousmethods measuring sympatheticnervous system activation through theskin took minutes, which is not practicalfor wearable devices. While her earlierwork focused on inferring brain activitythrough sweat activation and otherfactors, the new study additionallymodels the sweat glands themselves.The model includes a 3D state-spacerepresentation of the direct secretionof sweat via pore opening, as well asdiffusion followed by correspondingevaporation and reabsorption. Thisdetailed model of the glands provides exceptional insight into inferring thebrain activity.

The broader impact and applications ofthe methodology includes performancemonitoring, mental health monitoring,

measuring pain and cognitive stress.Mental health tracking can help bettermanage autism, post-traumatic stressdisorders, excessive irritability, suicidaltendency, and more. Performancetracking and cognitive stress tracking canhelp improve individual productivity andquality of life.

Her team is now working on ways toincorporate the model into wearables,including the elimination of informational“noise” caused by factors like robustmovement and exercise, as well asseeking potential partnerships to designand manufacture the devices that wouldcarry the algorithm.

A safer reality through virtual reality

Wearables go beyond a wristwatch.They also include the new universe ofVR headsets. And now our researchersare using these devices as a potentiallylife-saving tool.

It’s an everyday scenario: you’redriving down the highway when outof the corner of your eye you spot acar merging into your lane withoutsignaling. How fast can your eyes reactto that visual stimulus? Would it makea difference if the offending car wereblue instead of green? And if the color green shortened that split-second period between the initial appearance of the stimulus and when the eye began moving towards it (known to scientists as the saccade), could drivers benefit from an augmented reality overlay that made every merging vehicle green?

Qi Sun

Professor Qi Sun (CSE, CUSP) is collaborating with neuroscientists to find out.

He and his Ph.D. student Budmonde Duinkharjav — along with colleagues from Princeton, the University of North Carolina, and NVIDIA Research — recently authored the paper “Image Features Influence Reaction Time: A Learned Probabilistic Perceptual Model for Saccade Latency,” presenting a model that can be used to predict temporal gaze behavior, particularly saccadic latency, as a function of the statistics of a displayed image. Inspired by neuroscience, the model could ultimately have great implications for highway safety, telemedicine, e-sports, and in any other arena in which AR and VR are leveraged.

As neuroscientists make new discoveries about how the brain works, Sun hopes to bring them to bear in emerging media to unlock real-world benefits. “Think of the brain as a low-powered computer,” Sun says. “We know new technologies have an effect on our cognition and behavior, and we should be harnessing that for the good of society and helping prevent any negative effects.”

MORE ACCESSIBLE HEALTHCARE

How will rapidly expanding health telehealth and data-intensive technologies affect the future of healthcare work? A team of NYU researchers from the schools of engineering, medicine and business led by Professor Oded Nov (TMI, CUSP) are conducting a broad investigation into Digital Health Work: how to best bring scalable technologies into the clinic, empowering healthcare workers to take advantage of data-driven research and improve health outcomes for patients.

Tandon researchers received a $2.5M NSF grant to investigate data-intensivetechnologies in healthcare, including AI & telehealth tools.

The problem the team is taking on is the disconnect between healthcare practice that nurses, physician assistants, allied health staff and others are already familiar with, and the ways these practices can be altered by advanced technologies. Particularly challenging are the new reliance on telehealth and patient-driven big data which can burden practitioners who are not used to working with them.

With continued NSF investment, the Tandon-led Digital Health Work research initiative received a $2.5 million National Science Foundation (NSF) grant to address these problems.

“The new grant will help us further develop our NYU-wide research program on digital health work as an interdisciplinary research domain that brings together technological, organizational and medical innovations toward a healthy and resilient society, and an inclusive healthcare workforce,” said Nov. The project’s approach centers on alleviating misalignment between current healthcare work and data-intensive technologies, focusing on three areas:

• Co-developing tools and generalizable design principles with users that lower the barriers to technology integration for healthcare workers

• Empowering individuals within healthcare systems who have diverse roles to adopt and use the tools and improve their skills

• Enabling patient-centered healthcare that promotes autonomy and strengthens clinician-patient concordance

Oded Nov

While new technologies are constantly being developed, the hardest part to making sure they work is the “last mile” — a sociotechnical challenge that involves getting the right technologies matched with the right interfaces into the hands of diverse healthcare workers, and creating alignments between workflows, organizations, and technologies.

BUILDING BETTER TREATMENT THROUGH BIOMATERIALS

Osteoarthritis (OA) is a progressivecondition affecting the lives of more than32 million Americans. Post-traumaticosteoarthritis (PTOA), a major subsetof osteoarthritis that comprises 10%of diagnoses and disproportionallyaffects injured military personnel, has noeffective therapeutic protocols that slowor stop the progression except for overthe-counteranalgesics. Post-traumaticosteoarthritis leads to articular cartilagedamage and results in more than $3billion in health care costs each year.

Jin Kim Montclare

Researchers at NYU Tandon led byProfessor Jin Kim Montclare (CBE) haveidentified the molecular mechanismand therapeutic payload for deliveringpharmacologic treatment directly toaffected joints, effectively halting theonset and progression of post-traumaticosteoarthritis.

The researchers combined compoundsto develop a porous gel that can reachand envelop affected joints, reduceinflammation and induce regeneration.The substance, referred to as E5C, is aprotein-based gel that contains native,not synthetic, cartilage componentsthat are nontoxic and biodegradable.The properties of E5C make it a viablecandidate for injectable biomaterials.

Montclare is also working on biomaterials that could help make it easier to test for and treat COVID-19 and similar diseases. The condition, caused by the SARS-CoV-2 virus, attacks cells in the lungs, heart and brain, among other organs. Researchers soon realized that the disease affected these organs so dramatically because its distinctive spikes binded to the angiotensinconverting enzyme 2, or ACE2 receptor. The protein — common in those organs — provides the entry point for the coronavirus to hook into and infect cells.

Tandon researchers have developed a gel for delivering pharmacologic treatmentdirectly to joints to halt the onset and progression of osteoarthritis.

ACE2 receptors were thus the obvious choice when testing for or treating COVID-19. By recreating the ACE2 and introducing it to an infected body, the virus would bind to the protein, revealing itself in a test or occupying itself with a ‘fake’ receptor. But relying on the ACE2 protein alone may not provide sufficient binding to find and fight the virus.

Montclare and her team have created a new protein that has an increased ability to bind to viruses, creating a more efficient tool in the fight against COVID-19. The secret is creating a version of ACE2 that mimics a multivalent assembled protein (MAP). Multivalent assembled proteins are like naturally occurring antibodies. Their bodies have multiple sites that can link and bind to the viruses they are trying to attack, making them far more effective at hooking into their targets.

The ACE-MAP the team designed utilizes a coil-shaped cartilage oligomeric matrix protein, a nanomaterial that Montclare’s lab has used before in different applications. When fused with part of ACE2 across the coils surface, they found that the new materials greatly increased the valency compared to ACE2 alone, potentially binding to multiple virus bodies at a time rather than a single one.

This new material has potential uses in both detection and treatment. Because the biomaterial is so much more effective at attaching itself to viral bodies, it would require fewer of them compared to the natural antibodies currently used in tests and therapeutics. This technology has possible uses in testing for and treating other diseases with known receptors and a similar structure, such as HIV. Ongoing research will confirm the effectiveness of ACE-MAP in other models, and may be a key component of the fight against COVID-19 in the future.

UNVEILING HIDDEN HEALTH FACTORS

In the United States, roughly 13% ofwomen will develop breast cancer, andmore than 43,000 women are expectedto die as a result in 2021. The diseaserepresents a serious problem for publichealth. And while screening for thedisease can help prevent catastrophicresults, monitoring the progression ofcancer and how it responds to treatmentcan be difficult.

This optical tomography device can be used to recognize and track breast cancer, without the negative effects of previous imaging technology.

Now researchers at NYU Tandon aredeveloping the technology to helptrack the development of breast cancer, without causing further harm. In the lab of Professor and Chair Andreas Hielscher (BME) researchers are utilizing an optical tomography device that can be used to recognize and track breast cancer, without the negative effects of previous imaging technology. Traditional x-ray technology has a host of negative side effects due to radiation output, which makes it problematic to do regular scans on tumors. Their device uses nearinfrared light to shine into breast tissue and measure light attenuation that is caused by the propagation through the affected tissue.

The change in the amplitude of the light as it passes through the breast is a result from water, lipids, and oxyhemogoblin’s distribution throughout the tissue, and they can use these potential biomarkers as signs for cancerous tissue. By using the sensors, they can create 3D models of problematic tissue formations, and would be able to track tumors as they grow or shrink as treatments are applied. The technology, spearheaded by Hielscher, offers an opportunity for clinicians treating breast cancer patients to acquire far more information about their patients and their treatments than possible with other technologies.

Tandon researchers are using frequency domain optical imaging toreliably identify lupus arthritis and track how the disease progresses.

The technology is not just limited to breast cancer. Systemic lupus erythematosus (SLE), commonly referred to as simply “lupus”, is an autoimmune disorder where the body’s immune system attacks healthy tissue. Lupus affects somewhere between 20 to 150 people per 100,000, with variations among different racial and ethnic groups. The disease often causes arthritic symptoms in the joints, which can be debilitating in some cases.

Despite the severity of the disease, identification of lupus arthritis and assessment of its activity remains a challenge in clinical practice. Evaluations based on traditional joint examination lack precision, due to its subjective nature and accuracy in situations such as obese digits and co-existing fibromyalgia. As such, these examinations have limited ability to render quantitative data about improvement and worsening.

Alessandro Marone

Andreas Hielscher

Recently, imaging technology, especially ultrasound (US) and magnetic resonance imaging (MRI), has enabled more objective and detailed assessment of articular and periarticular abnormalities with higher sensitivity. However, MRI and US are expensive and time-consuming. Furthermore, US has been found to be very operator dependent. Therefore, both modalities are currently not routinely used in practice. There is a clear unmet need for a simple, reliable, non-invasive and low-cost imaging modality that can objectively assess and monitor arthritis progress in patients with lupus.

Now, researchers at NYU Tandon in collaboration with Columbia University are exploring optical imaging technology as a reliable way to diagnose patients and assess the progression of the disease. The researchers, including Research Assistant Professor Alessandro Marone (BME) and Hielscher, found that frequency domain optical imaging could reliably identify lupus arthritis, and could be used to track how the disease progressed.

The results provide strong evidence that frequency domain optical images could provide objective, accurate insights into SLE that were not possible

or economically feasible using other technologies. The light diffusion identified inflammation in the blood vessels around joints, similar to but distinct from the symptoms caused by rheumatoid arthritis. With this technology, caregivers may not have to rely on patient feedback to track the progression of lupus, but can see it in action.

Optical imaging methods have been used in studies comparing osteoarthritis, rheumatoid arthritis (RA) and healthy controls. The results of those studies highlighted that patients suffering from RA have higher light absorption in the joint space compared with healthy subjects. This is likely due to the presence of inflammatory synovial fluid that decreases light transmission through the inflamed joints. But these observations have never been used to study lupus before, and these findings could provide a reliable, rapid, and cost-effective method of assessing joint involvement in lupus patients.

THE POWER OF DATA + HEALTH

Mapping firearm ownership across the country

Policy-makers are faced with an exceptional challenge: how to reduce harm caused by firearms while maintaining citizens’ right to bear arms and protect themselves. This is especially true as the Supreme Court has hobbled New York State regulations restricting who can carry a concealed weapon.

Director of CUSP Maurizio Porfiri delivers a flash technical talk on the firearms ecosystem at an Engineering Safer Cities Event. Photo credit: Anna Sawulska, courtesy of the Dynamical Systems Laboratory.

While meaningful legislation requires an understanding of how access to firearms is associated with different outcomes of harm, this knowledge also calls for accurate, highly-resolved data on firearm possession, data that is presently unavailable due to a lack of a comprehensive national firearm ownership registry.

Newly published research from data scientist and firearm proliferation researcher Instiute Professor Maurizio Porfiri (MAE, BME, CUE, and Director of CUSP) and co-authors Roni Barak Ventura, a post-doctoral researcher at Porfiri’s DSL, and Manuel Ruiz Marin of the Universidad Politécnica de Cartagena, Spain, describe a spatio-temporal model to predict trends in firearm prevalence on a state-by-state level by fusing data from two available proxies — background checks per capita and suicides committed with a firearm in a given state. The study “A spatiotemporal model of firearm ownership in the United States,” in the Cell Press journal Patterns, details how, by calibrating their results with yearly survey data, the team determined that the two proxies can be simultaneously considered to draw precise information regarding firearm ownership.

Porfiri, who in 2020 received one of the first newly authorized NSF federal grants for $2 million to study the “firearm ecosystem” in the U.S., has spent the last few years exploring gun acquisition trends and how they relate to and are influenced by a number of factors, from media coverage of mass shootings to the influence of the sitting President.

“There is very limited knowledge on when and where guns are acquired in the country, and even less is known regarding future ownership trends,” said Porfiri. “Prior studies have largely relied on the use of a single, select proxy to make some inference of gun prevalence, typically within simple correlation schemes. Our results show that there is a need to combine proxies of sales and violence to draw precise inferences on firearm prevalence.” He added that most research aggregates the measure counts within states and does not consider interference between states or spill-over effects.

Their study shows how their model can be used to better understand the relationships between media coverage, mass shootings, and firearm ownership, uncovering causal associations that are masked when the proxies are used individually. While the researchers found, for example, that media coverage of firearm control is causally associated with firearm ownership, they discovered that their model generating a strong firearm ownership profile for a state was a strong predictor of mass shootings in that state.

“The potential link between mass shootings and firearm purchases is a unique contribution of our model,” said Ruiz Marin. “Such a link can only be detected by scratching the surface on the exact gun counts in the country.”

“We combined publicly available data variables into one measure of ownership. Because it has a spatial component, we could also track gun flow from one state to another based on political and cultural similarities,” said Barak-Ventura, adding that the spatial component of the work is novel. “Prior studies looked at a correlation of two variables such as increasing background checks and an increase in gun violence.”

Barak-Ventura said the team is now using their model to explore which policies are effective in reducing death by guns in a state and surrounding regions, and how the relationship between gun ownership and violent outcomes is disrupted by different legislation.

“Big Data” takes on hospitalizations

Modern predictive models require large amounts of data for training and evaluation, the absence of which may result in models that are specific to certain locations, their populations and the clinical practices there. Currently, best practices for clinical risk prediction models lack a level of “generalizability” that could vastly increase their usefulness for other clinical settings in other locations.

Rumi Chunara

A team of NYU Tandon researchers led by Associate Professor Rumi Chunara (CSE, NYU School of Global Public Health) investigated whether mortality prediction models vary significantly when applied to hospitals or geographies different from the ones in which they are developed. They also queried the data to determine specific characteristics of the datasets — involving analysis of electronic health records from 179 hospitals across the U.S. with 70,126 hospitalizations from 2014 to 2015 — that could explain variations in clinical performance based on factors like race.

In a new study, the researchers found that mortality risk prediction models that included clinical (vitals, labs and surgery) variables developed in one hospital or geographic region exhibited a lack of generalizability to different hospitals or regions. Based on a causal discovery analysis, they postulated that this lack of generalizability results from dataset shifts in race and clinical variables across hospitals or regions. In short, the race variable is intimately connected to clinical variables.

Researchers found that hospital mortality risk prediction models that included clinical variables developed inone hospital were not generalizable to different hospitals or regions, at least in part due to disparities in race.

“It is clear from this research that data models — in terms of factors like mortality risk prediction at a hospital to hospital and regional hospital group level — are not immediately generalizable, and that has implications for hospitals that can’t generate these models for themselves,” said Chunara.

Findings also demonstrate evidence that predictive models can exhibit disparities in performance across racial groups even while performing well in terms of average population-wide metrics.

“While it is well documented that clinical factors and outcomes can vary significantly by race, it is critical that we understand why those differences exist, and thus examination of data and models must be done in a larger context alongside diverse influences, from geographic and socioeconomic to clinical,” she said.

Specifically, the study suggests that beyond algorithmic fairness metrics, an understanding of data generating processes for sub-groups is needed to identify and mitigate sources of variation, and to decide whether to use a riskprediction model in new environments.

EyeScore: Predicting stroke risk

One in six deaths from cardiovascular disease is due to stroke. Caused by a blood clot inthe brain, a stroke can have severe consequences, even minutes after initially occurring.That is what makes prevention so important.

Now, thanks to NYU researchers, including a collaborative effort between NYU Assistant Professor S. Farokh Atashzar (ECE, MAE, BME, CUSP, NYU WIRELESS), and NYUAD Assistant Professor Farah Shamout, early monitoring may soon be available at an unlikely location: your eye doctor.

Tandon researchers are collaborating with NYU Abu Dhabi to develop hybrid AI models that analyze non-invasive optical coherence tomography retina scans to predict stroke risk

Their Project, called EyeScore, is developing a technology that uses non-invasive scans of the retina to predict the recurrence of stroke in patients. They use optical coherence tomography — a scan of the back of the retina — and track changes over time. The retina, attached directly to the brain through the optic nerve, can be used as an indicator for changes in the brain itself.

Atashzar and Shamout are currently formulating their hybrid AI model, pinpointing the exact changes that can predict a stroke and recurrence of strokes. The outcome will be able to analyze these images and flag potentially troublesome developments. And since the scans are already in use in optometrist offices, this life-saving technology could be in the hands of medical professionals sooner than expected.

The Data Behind Pandemics

Any large-scale pandemic, such as COVID-19, is an example of a large-scale disease propagation network that can be seen as an “interconnected mega-grid” where complex interactions and distributed delays in the interconnections lead to hard-to-predict, echoing “waves” of disease spread. Assistant Professor S. Farokh Atashzar (ECE, MAE, CUSP, NYU WIRELESS), with the support of a $1.1M Collaborative NSF grant and in collaboration with researchers at Northeastern University, will dive into these “waves” by developing novel approaches to computational network modeling and designing optimal mitigation control to minimize the spread.

S. Farokh Atashzar

This research seeks to develop a comprehensive framework for datadriven control of large-scale networks where time delays and complex behavior play an important role. In the COVID pandemic, such effects led to “reflective” spreading waves, resulting in hard-to-predict and hard-to-control phases of infection spread, which were not accurately analyzable using classic small-scale epidemic modeling approaches. Thus, new computational frameworks are needed to take into account (a) unique signatures of mega-networks of connected societies and (b) complex disease spread behavior. The goal is to enhance pandemic preparedness and to make healthcare systems and governments ready to respond well to potential future airborne epidemic diseases.