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Engineering

<p>At Delve, we have a long history of leveraging new tools and technology strategically—to address real human problems and transform opportunities into practical solutions.  </p>
<p> </p>
<p>And, in our over 55+ years of product innovation work, we have not seen an industry make greater technological leaps or transform more lives than modern healthcare. </p>
<p>As we continue to incorporate our <a href="https://www.delve.com/services/engineering/data-science-machine-learning">data science and machine learning expertise</a> even more deeply into Delve’s client work across nine different sectors, we want to acknowledge five recent advances in the health sector, powered by data and machine learning, that have particularly inspired us.  </p>
<p> </p>
<p>This is what it looks like when the right tools are used to build the right solutions.</p>
<h2>#1: Nyquist Data&nbsp;– Harnessing the Global Healthcare Industry’s Data</h2>
<p>Using AI, big data, and machine learning, <a href="https://www.nyquistdata.com/" target="_blank" rel="noreferrer noopener"><strong>Nyquist Data</strong></a> provides business, clinical, and regulatory intelligence and analytics on medical devices and pharmaceuticals across major markets, such as the U.S., Japan, the E.U., and China. Instead of taking months or years, the data is delivered within seconds.</p>
<h3>The problem </h3>
<p>Healthcare industry information that was technically publicly available has long been locked in obscure and disparate black box computer systems scattered across the world.  <br> <br>Nyquist Data founder Michelle Wu wondered whether AI, big data, and machine learning could be used to unlock access to this data and surface insights into how to develop life-saving medical innovations faster and cheaper. </p>
<h3>Nyquist’s solution </h3>
<p><a href="https://www.nyquistdata.com/" target="_blank" rel="noreferrer noopener">Nyquist Data</a> uses natural-language processing, AI, and machine learning to aggregate, organize, and analyze huge amounts of medical data worldwide. Information and insights critical to business decision making is immediately available to pharmaceutical and medical device companies. </p>
<h3>Why we’re inspired </h3>
<p>“Publicly available” data that takes healthcare innovators months or even years to gather and analyze is not truly available. Nyquist Data is a great example of how data science and machine learning can unlock opportunity and minimize risk for medical device and pharmaceutical companies. Ultimately, consumers benefit because new life-saving products and drugs are developed, tested, and brought to market more quickly. <br></p>
<h2>#2: <strong>Blue Dot</strong><strong>&nbsp;–</strong>&nbsp;<strong>Outbreak Prediction and Planning for Governments and Private Enterprise</strong></h2>
<p><strong></strong>Billing itself “the most actionable source of global threat intelligence,” <a href="https://bluedot.global/" target="_blank" rel="noreferrer noopener">Blue Dot</a> empowers government organizations and private enterprises to quickly identify, understand, and respond to global biological threats such as COVID-19. </p>
<h3>The problem  </h3>
<p>In the wake of COVID-19, governments and private sector companies know they must prepare rigorously for future pandemic outbreaks. One of the biggest challenges: the sheer enormity of the data sets involved, and the speed with which this data evolves. </p>
<h3>Blue Dot's solution </h3>
<p>Blue Dot uses machine learning to scan over 100,000 media sources in over 65 different languages daily, enabling them to identify and track infectious disease outbreaks in near real time.  <br> <br>Nine days before the World Health Organization released its alert about COVID-19 in 2020, Blue Dot’s digital early warning system flagged a cluster of “unusual pneumonia” cases in Wuhan, China, sounding the alarm for the unprecedented global pandemic. </p>
<h3>Why we’re inspired </h3>
<p>During the height of the COVID-19 pandemic, many of us struggled to find face masks or hand sanitizing gel on empty store shelves. We read, with horror, news about hospital teams forced to improvise or reuse scarce PPE gear while treating critically ill patients.  <br> <br>Moving forward, data platforms like Blue Dot will enable government agencies to respond rapidly to future outbreaks. They will also allow manufacturers of PPE supplies and medical equipment such as ventilators to better able to forecast demand, and predict disruption to critical global assets and supply chains. </p>

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<h2>#3: Ada&nbsp;– A Doctor-Built AI Alternative to Googling Your Own Disease Symptoms</h2>
<p>Using disease predictive modeling and advancements in natural language processing, a Berlin based startup built <a href="https://ada.com/" target="_blank" rel="noreferrer noopener">Ada,</a> a medical virtual assistant that predicts diseases based on the user’s symptoms. <br></p>
<h3>The problem </h3>
<p>Millions around the world are unable to access the healthcare they need. The founders of Ada wondered if a clinical AI solution could help individuals “understand, manage, and get care for their symptoms.”  <br> <br>To make a difference, they knew Ada would need to be grounded in real, trusted medical expertise. And, it had to deliver actionable information in minutes.&nbsp;</p>
<h3>Ada’s solution </h3>
<p>The free symptom checker app now has over 12 million users and has completed over 28 million assessments. Users take just a few minutes to answer questions about their symptoms, then receive care suggestions based on AI and clinical evidence.  <br> <br>Ada’s creators claim the app is 35% more accurate, on average, than other symptom assessment apps and can detect serious health issues other solutions cannot, including rare, pediatric, obstetric, and mental health conditions. </p>
<h3>Why we’re inspired </h3>
<p>No symptom checker app, however advanced, can replace a consultation with a trained medical professional who will evaluate your symptoms in person and order follow up diagnostic tests as needed.  <br> <br>However, apps like Ada are a powerful tool that can empower individuals to better understand their symptoms, manage their health more cost-effectively, and get the care they need when they need it. With healthcare costs continuing to skyrocket in many countries including the U.S., Ada unlocks the power of data and puts some of that power back into the hands of healthcare consumers. </p>

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<h3>#4: Project InnerEye: Democratizing Medical Imaging AI</h3>
<p> Microsoft's <a href="https://www.microsoft.com/en-us/research/project/medical-image-ana" target="_blank" rel="noreferrer noopener">Project InnerEye</a> uses machine learning to build innovative tools for the automatic, quantitative analysis of 3D medical images. The InnerEye Deep Learning Toolkit has been released as open-source software on GitHub “to democratize care and ensure that as many people as possible can benefit from the tool.” </p>



<h3>The problem </h3>
<p>When performing a manual scan on a patient, a clinical oncologist must segment CT images obtained during a screening individually. This process, known as contouring, involves carefully outlining tumors vs. healthy tissue on the scan. The process can take hours. <br> </p>
<h3>InnerEye’s solution </h3>
<p>InnerEye accurately identifies tumors on patient scans while cutting CT processing times and treatment planning by up to 90%. 3D contouring can be completed in minutes instead of hours.  </p>
<p>Furthermore, Microsoft’s decision to make the toolkit open source empowers researchers, hospitals, life science organizations, and healthcare providers to build their own imaging AI models. </p>
<h3>Why we’re inspired </h3>
<p>InnerEye processes medical images 13 times faster than they can be processed manually. For a cancer patient, for whom time is often of the essence, this means the waiting time for treatment is potentially reduced as well. <br> <br>Also worth noting: Because it is open-source, InnerEye may also be more easily adopted in hospitals and health systems. Many hospital IT teams have reasonable concerns regarding new technology when algorithm performance, source code, and input data are unavailable to review. The development of open-source AI trained on open data depositories and publicly shared algorithms can address this concern, removing a major barrier to scalability. </p>

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<h3>#5: One Step Closer to 3D Printed Organs? Machine Learning Principles in 3D Bioprinting</h3>
<p>Donor organs are in short supply. Researchers are exploring how machine learning principles can move us closer to 3D printed human organs suitable transplants. </p>
<h3>The problem </h3>
<p>More than <a href="https://www.cnbc.com/2022/10/26/how-organ-donation-works-in-the-united-states.html" target="_blank" rel="noreferrer noopener">100,000 people</a> in the the United States are waiting for an organ for transplant. Only <a href="https://transplantliving.org/before-the-transplant/waiting-for-your-transplant/" target="_blank" rel="noreferrer noopener">50%</a> of them will receive the organ they need within the next five years. According to the <a href="https://oversight.house.gov/legislation/hearings/the-urgent-need-to-reform-the-organ-transplantation-system-to-secure-more" target="_blank" rel="noreferrer noopener">House Committee on Oversight and Reform,</a> every day, an estimated 33 people in the U.S. die while waiting for their transplant.</p>
<p>To address the organ shortage, medical researchers have been exploring 3D bioprinting of human organs as an option for transplantation. Unlike regular 3D printing, which uses metals and plastics, 3D bioprinting uses cells and hydrogel pre-polymers to create tissue scaffolds, and eventually, organs.  <br> <br>So far, researchers have 3D bioprinted “bio-blood-vessels", parts of a kidney, human liver patches, ears, lungs, and a <a href="https://engineering.cmu.edu/news-events/news/2020/11/18-3d-printed-heart.html" target="_blank" rel="noreferrer noopener">heart.</a> However, successful transplantation of a 3D printed human organ has not yet happened. </p>
<h3>Part of the solution? </h3>
<p><a href="https://www.mdpi.com/2072-666X/13/3/363/htm" target="_blank" rel="noreferrer noopener">Researchers at the University of Calgary</a> recently published a paper observing that although "remarkable” progress that has been made toward proving the feasibility of 3D bioprinting in organ transplants and improving patient treatment methods, fabricating fully functional tissues and organs still faces many challenges.  <br> <br>Among the challenges identified by this paper was the vast amounts of complex data collected from personalized medicine diagnostic approaches. According to the authors, such data can only be stored, processed, and analyzed using novel and highly advanced methods. Accordingly, the paper recommended combining 3D bioprinting with machine learning techniques to accelerate future development. <br> </p>
<h3>Why we’re inspired </h3>
<p>The path to bioengineering a functional human organ suitable for transplant purposes is a long one. According to the University of Calgary team, “machine learning can optimize the bioprinting process and parameters to increase printing fidelity.” This would move us one much-needed step closer to the ability to repair or replace full human organs. </p>

<p><strong>Learn more about&nbsp;<a href="https://www.delve.com/services" target="_blank">Product Development & Design Services at Delve.</a></strong></p>

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