This sponsored article is delivered to you by NYU Tandon School of Engineering.
To handle right now’s well being challenges, particularly in our growing old society, we should turn out to be extra clever in our approaches. Clinicians now have entry to a variety of superior applied sciences designed to help early prognosis, consider prognosis, and improve affected person well being outcomes, together with telemedicine, medical robots, powered prosthetics, exoskeletons, and AI-powered sensible wearables. Nonetheless, many of those applied sciences are nonetheless of their infancy.
The assumption that advancing expertise can enhance human well being is central to analysis associated to medical system applied sciences. This varieties the guts of analysis for Prof. S. Farokh Atashzar who directs the Medical Robotics and Interactive Intelligent Technologies (MERIIT) Lab on the NYU Tandon School of Engineering.
Atashzar is an Assistant Professor of Electrical and Pc Engineering and Mechanical and Aerospace Engineering at NYU Tandon. He’s additionally a member of NYU WIRELESS, a consortium of researchers devoted to the following technology of wi-fi expertise, in addition to the Heart for City Science and Progress (CUSP), a middle of researchers devoted to all issues associated to the way forward for fashionable city life.
Atashzar’s work is devoted to growing clever, interactive robotic, and AI-driven assistive machines that may increase human sensorimotor capabilities and permit our healthcare system to transcend pure competences and overcome physiological and pathological boundaries.
Stroke detection and rehabilitation
Stroke is the main reason behind age-related motor disabilities and is changing into more prevalent in youthful populations as effectively. However whereas there’s a burgeoning marketplace for rehabilitation gadgets that declare to speed up restoration, together with robotic rehabilitation techniques, suggestions for a way and when to make use of them are based mostly totally on subjective analysis of the sensorimotor capacities of sufferers in want.
Atashzar is working in collaboration withJohn-Ross Rizzo, affiliate professor of Biomedical Engineering at NYU Tandon and Ilse Melamid Affiliate Professor of rehabilitation medication on the NYU Faculty of Medication and Dr. Ramin Bighamian from the U.S. Meals and Drug Administration to design a regulatory science instrument (RST) based mostly on information from biomarkers as a way to enhance the evaluation processes for such gadgets and the way greatest to make use of them. The group is designing and validating a sturdy restoration biomarker enabling a first-ever stroke rehabilitation RST based mostly on exchanges between areas of the central and peripheral nervous techniques.
S. Farokh Atashzar is an Assistant Professor of Electrical and Pc Engineering and Mechanical and Aerospace Engineering at New York College Tandon Faculty of Engineering. He’s additionally a member of NYU WIRELESS, a consortium of researchers devoted to the following technology of wi-fi expertise, in addition to the Heart for City Science and Progress (CUSP), a middle of researchers devoted to all issues associated to the way forward for fashionable city life, and directs the MERIIT Lab at NYU Tandon.NYU Tandon
As well as, Atashzar is collaborating with Smita Rao, PT, the inaugural Robert S. Salant Endowed Affiliate Professor of Bodily Remedy. Collectively, they intention to establish AI-driven computational biomarkers for motor management and musculoskeletal injury and to decode the hidden advanced synergistic patterns of degraded muscle activation utilizing information collected from floor electromyography (sEMG) and high-density sEMG. Previously few years, this collaborative effort has been exploring the fascinating world of “Nonlinear Practical Muscle Networks” — a brand new computational window (rooted in Shannon’s data idea) into human motor management and mobility. This synergistic community orchestrates the “music of mobility,” harmonizing the synchrony between muscular tissues to facilitate fluid motion.
However rehabilitation is just one of many analysis thrusts at MERIIT lab. For those who can stop strokes from occurring or reoccurring, you’ll be able to head off the issue earlier than it occurs. For Atashzar, an enormous clue may very well be the place you least count on it: in your retina.
Atashzar together with NYU Abu Dhabi Assistant Professor Farah Shamout, are engaged on a undertaking they name “EyeScore,” an AI-powered expertise that makes use of non-invasive scans of the retina to foretell the recurrence of stroke in sufferers. They use optical coherence tomography — a scan of the again of the retina — and monitor modifications over time utilizing superior deep studying fashions. The retina, hooked up on to the mind via the optic nerve, can be utilized as a physiological window for modifications within the mind itself.
Atashzar and Shamout are at the moment formulating their hybrid AI mannequin, pinpointing the precise modifications that may predict a stroke and recurrence of strokes. The result will be capable to analyze these photos and flag probably troublesome developments. And for the reason that scans are already in use in optometrist places of work, this life-saving expertise may very well be within the arms of medical professionals earlier than anticipated.
Atashzar is using AI algorithms for makes use of past stroke. Like many researchers, his gaze was drawn to the most important medical occasion in latest historical past: COVID-19. Within the throes of the COVID-19 pandemic, the very bedrock of worldwide healthcare supply was shaken. COVID-19 sufferers, inclined to swift and extreme deterioration, introduced a significant issue for caregivers.
Particularly within the pandemic’s early days, when our grasp of the virus was tenuous at greatest, predicting affected person outcomes posed a formidable problem. The merest tweaks in admission protocols held the facility to dramatically shift affected person fates, underscoring the necessity for vigilant monitoring. As healthcare techniques groaned beneath the pandemic’s weight and contagion fears loomed, outpatient and nursing heart residents had been steered towards distant symptom monitoring by way of telemedicine. This cautious method sought to spare them pointless hospital publicity, permitting in-person visits just for these within the throes of grave signs.
However whereas a lot of the pandemic’s analysis highlight fell on diagnosing COVID-19, this examine took a distinct avenue: predicting affected person deterioration sooner or later. Present research typically juggled an array of knowledge inputs, from advanced imaging to lab outcomes, however did not harness information’s temporal features. Enter this analysis, which prioritized simplicity and scalability, leaning on information simply gathered not solely inside medical partitions but additionally within the consolation of sufferers’ houses with using easy wearables.
S. Farokh Atashzar and colleagues at NYU Tandon are utilizing deep neural community fashions to evaluate COVID information and attempt to predict affected person deterioration sooner or later.
Atashzar, alongside along with his Co-PI of the undertaking Yao Wang, Professor of Biomedical Engineering and Electrical and Pc Engineering at NYU Tandon, used a novel deep neural community mannequin to evaluate COVID information, leveraging time sequence information on simply three very important indicators to foresee COVID-19 affected person deterioration for some 37,000 sufferers. The final word prize? A streamlined predictive mannequin able to aiding medical decision-making for a large spectrum of sufferers. Oxygen ranges, heartbeats, and temperatures fashioned the trio of important indicators beneath scrutiny, a selection propelled by the ubiquity of wearable tech like smartwatches. A calculated exclusion of sure indicators, like blood stress, adopted, on account of their incompatibility with these wearables.
The researchers utilized real-world information from NYU Langone Well being’s archives spanning January 2020 to September 2022 lent authenticity. Predicting deterioration inside timeframes of three to 24 hours, the mannequin analyzed very important signal information from the previous 24 hours. This crystal ball aimed to forecast outcomes starting from in-hospital mortality to intensive care unit admissions or intubations.
“In a state of affairs the place a hospital is overloaded, getting a CT scan for each single affected person could be very troublesome or not possible, particularly in distant areas when the healthcare system is overstretched,” says Atashzar. “So we’re minimizing the necessity for information, whereas on the similar time, maximizing the accuracy for prediction. And that may assist with creating higher healthcare entry in distant areas and in areas with restricted healthcare.”
Along with addressing the pandemic on the micro degree (people), Atashzar and his group are additionally engaged on algorithmic options that may help the healthcare system on the meso and macro degree. In one other effort associated to COVID-19, Atashzar and his group are growing novel probabilistic fashions that may higher predict the unfold of illness when bearing in mind the results of vaccination and mutation of the virus. Their efforts transcend the traditional small-scale fashions that had been beforehand used for small epidemics. They’re engaged on these large-scale advanced fashions as a way to assist governments higher put together for pandemics and mitigate fast illness unfold. Atashzar is drawing inspiration from his lively work with management algorithms utilized in advanced networks of robotic techniques. His group is now using related methods to develop new algorithmic instruments for controlling unfold within the networked dynamic fashions of human society.
A state-of-the-art human-machine interface module with wearable controller is one among many multi-modal applied sciences examined in S. Farokh Atashzar’s MERIIT Lab at NYU Tandon.NYU Tandon
The place minds meet machines
These initiatives symbolize solely a fraction of Atashzar’s work. Within the MERIIT lab, he and his college students construct cyber-physical techniques that increase the performance of the next-generation medical robotic techniques. They delve into haptics and robotics for a variety of medical purposes. Examples embody telesurgery and telerobotic rehabilitation, that are constructed upon the capabilities of next-generation telecommunications. The group is particularly within the software of 5G-based tactile web in medical robotics.
Lately, he acquired a donation from the Intuitive Basis: a Da Vinci analysis package. This state-of-the-art surgical system will permit his group to discover methods for a surgeon in a single location to function on a affected person in one other—whether or not they’re in a distinct metropolis, area, and even continent. Whereas a number of researchers have investigated this imaginative and prescient up to now decade, Atashzar is particularly concentrating on connecting the facility of the surgeon’s thoughts with the autonomy of surgical robots – selling discussions on methods to share the surgical autonomy between the intelligence of machines and the thoughts of surgeons. This method goals to scale back psychological fatigue and cognitive load on surgeons whereas reintroducing the sense of haptics misplaced in conventional surgical robotic techniques.
Atashzar poses with NYU Tandon’s Da Vinci analysis package. This state-of-the-art surgical system will permit his group to discover methods for a surgeon in a single location to function on a affected person in one other—whether or not they’re in a distinct metropolis, area, and even continent.NYU Tandon
In a associated line of analysis, the MERIIT lab can be specializing in cutting-edge human-machine interface applied sciences that allow neuro-to-device capabilities. These applied sciences have direct purposes in exoskeletal gadgets, next-generation prosthetics, rehabilitation robots, and probably the upcoming wave of augmented actuality techniques in our sensible and related society. One frequent vital problem of such techniques which is concentrated by the group is predicting the supposed actions of the human customers via processing alerts generated by practical habits of motor neurons.
By fixing this problem utilizing superior AI modules in real-time, the group can decode a consumer’s motor intentions and predict the supposed gestures for controlling robots and digital actuality techniques in an agile and strong method. Some sensible challenges embody guaranteeing the generalizability, scalability, and robustness of those AI-driven options, given the variability of human neurophysiology and heavy reliance of traditional fashions on information. Powered by such predictive fashions, the group is advancing the advanced management of human-centric machines and robots. They’re additionally crafting algorithms that keep in mind human physiology and biomechanics. This requires conducting transdisciplinary options bridging AI and nonlinear management theories.
Atashzar’s work dovetails completely with the work of different researchers at NYU Tandon, which prizes interdisciplinary work with out the silos of conventional departments.
“Dr. Atashzar shines brightly within the realm of haptics for telerobotic medical procedures, positioning him as a rising star in his analysis neighborhood,” says Katsuo Kurabayashi, the brand new chair of the Mechanical and Aerospace Engineering division at NYU Tandon. “His pioneering analysis carries the thrilling potential to revolutionize rehabilitation remedy, facilitate the prognosis of neuromuscular ailments, and elevate the sphere of surgical procedure. This holds the important thing to ushering in a brand new period of refined distant human-machine interactions and leveraging machine learning-driven sensor sign interpretations.”
This dedication to human well being, via the embrace of recent advances in biosignals, robotics, and rehabilitation, is on the coronary heart of Atashzar’s enduring work, and his unconventional approaches to age-old drawback make him an ideal instance of the method to engineering embraced at NYU Tandon.
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