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From the library of What’s next: archives from Developers

Intelligence at Work: MaxQ AI’s ACCIPIO intracranial hemorrhage (ICH), stroke, and head trauma software platform

Healthcare faces unprecedented pressure - with less money available, more patients, and a care provider shortage. MaxQ AI is addressing these pressures by leveraging AI and machine learning. Its ACCIPIO ICH and Stroke Platform improves care delivery and outcomes by improving intracranial hemorrhage (ICH) detection and potentially reducing missed ICH. MaxQ AI’s FDA cleared Accipio Ix and Ax solutions are available for review on Nuance’s AI Marketplace for Diagnostic Imaging, and will be integrated with Nuance’s PowerScribe One reporting platform and PowerShare Network connecting 6,500+ healthcare facilities, including stroke centers.
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Randy Rohmer, Director Commercial Operations of MaxQ AI, shares his insights about how MaxQ AI-driven algorithms aim to meaningfully help reduce misdiagnosis and healthcare costs by making artificial intelligence intrinsic to the diagnostic process for time-sensitive and life-threatening conditions.

MaxQ AI is at the forefront of Medical Diagnostic AI. The company is transforming healthcare by empowering physicians to provide “smarter care” with artificial intelligence (AI) clinical insights. Based in Tel Aviv, Israel and Andover, MA, USA, MaxQ AI’s team of deep learning and machine vision experts develop innovative software that uses AI to interpret medical images and surrounding patient data. Working with world-class clinical and industry partners, the company’s software enables physicians to make faster, more accurate decisions when diagnosing stroke, traumatic brain injury, head trauma, and other serious conditions.

Q&A

Jonathon Dreyer: Tell us about your business – when and how you started and your development journey.

Randy Rohmer: MaxQ AI is at the cutting-edge of innovative medical diagnostic artificial intelligence. We aspire to make a meaningful impact on stroke and brain trauma treatment, driven by the question “what is the cost of a missed intracranial hemorrhage (ICH)?” The potential is massive – if our solutions are able to divert only one patient per year in the US/EU acute hospitals from stroke care to wellness care, that would represent billions in savings in the first year alone, and a lifetime of difference to the patient and family.

MaxQ AI is ushering in empowered care – the partnership between AI and the skilled care providers to extend expertise to every patient. Dedicated to improving a physician’s ability to make a faster and more confident diagnosis, our solutions hold significant potential to increase the quality of care in emergency rooms in rural and community hospitals across the globe.

Our first platform of medical devices, ACCIPIO®, which means “to learn” in Latin, uses artificial intelligence in review of non-contrast CT to enhance intracranial hemorrhage (ICH) diagnosis and treatment. In collaboration with our world-class clinical partners, along with state-of-the-art technology, we envision revolutionizing acute care and treatment standards to become a globally influential entity that provides universal access to expert-level diagnosis that will make a significant difference in patients’ and physicians’ lives. When minutes matter, Accipio, plus the skilled care provider, are better together.

JD: What AI algorithms do you have, and what do they do?

RR: MaxQ AI has developed a full ecosystem of algorithms providing a comprehensive workflow solution designed to benefit the patient, care provider, and facility. Our ACCIPIO ICH and Stroke Platform with INSIGHT™ supports the radiology department, emergency room, and neuroradiology teams with a fully automated solution, designed to empower healthcare decisions in acute care settings.

  • ACCIPIO Ix (FDA cleared & CE approved, commercially available) – Provides automatic identification/detection, notification, acceleration and prioritization of suspected ICH.
  • ACCIPIO Ax (FDA cleared & CE approved, commercially available) – Provides automatic slice-level annotation of suspected ICH and a comprehensive summary of all suspected slices via MaxQ AI’s unique SliceMap™.

As future regulatory approvals are garnered, and the range of technologies work interactively, the Accipio ecosystem will gain more traction in acute care settings across the globe. The future Accipio platform will also provide AI diagnostic tools (currently in development), such as ICH expert-level diagnostic rule-out, lesion-level annotation of suspected ICH, and quantification of suspected ICH volume (currently in development).

JD: What’s the big “Aha” moment when you first show users what your AI algorithm(s) can do for them?

RR: Our users have embraced the fact that Accipio is a comprehensive workflow solution for ICH, stroke, and head trauma, not just another algorithm looking for a nail. Comprehensive, seamless, and secure. For our customers, a comprehensive solution that has seamless integration into workflow is key – clinicians and radiologists in acute care settings need answers, not more analysis. That’s why we’ve integrated AbsoluteZero™ with all of our solutions. Zero clicks to Accipio results. Zero need to leave workflow. Zero stored PHI. Zero change to original series. No on-site IT integration required. Seamless from the Start™.

JD: What challenges or needs did you see that drove you to focus on this?

RR: MaxQ is an aeronautic term that actually means ‘maximum pressure,’ which is typically the point where failure will occur. Today’s healthcare system is this breaking point with an urgent need for solutions that will open up capacity, and relieve the pressure of increased patient volume, decreased revenue, and fewer care providers. Instead of more data and analysis, adding to the decision-making burden, care providers need solutions that provide answers while seamlessly integrating into their current workflow.

MaxQ AI’s platform of medical diagnostic AI solutions do just that, which holds great promise for healthcare through significant quality, clinical, and economic advancement in the empowerment of the talented care providers having to make the “minutes matter” call. Lives will be changed, both for patients and care providers alike.

JD: What’s the number one benefit you offer?

RR: MaxQ AI will support the complete ACCIPIO ICH and Stroke Platform with INSIGHT: It will support the Radiology Department, Emergency Room, Neuroradiology, and the Stroke teams with a fully automated solution. The ACCIPIO platform will provide tools for identification & prioritization (lx) 1, slice-level annotation (Ax) 2, and triage guidance for suspected ICH presence and diagnostic quality rule-out3. The complete Accipio solution for head trauma and stroke promises to:

  • Greatly increase ICH detection and reduce missed ICHs through near real-time triage, annotation and diagnostic rule-out – because every minute matters for TBI, trauma, and stroke patients.
  • Potentially enhance clinical confidence, including mobilization of ischemic stroke and neurosurgery teams.
  • Provide the right care readied faster, improving quality to potentially avoid poor patient outcomes and to decrease costs and liability.
  • Provide slice-level annotation, lesion-level annotation and quantification of lesion volume within suspected ICH.
  • Provide automatic diagnostic ICH rule-out.
  • Address the total workflow needs of the reader; not a single algorithm –– an ecosystem designed to benefit the patient, care provider, and facility.

JD: Are there any stories you can share about how your algorithm(s) drove measurable patient care outcomes?

RR: A typical acute hospital in the Northeast US, Capital Health, is seamlessly using MaxQ AI’s Accipio Ix ICH (intracranial hemorrhage) solution, to automatically identify and prioritize non-contrast CT head images with suspected ICH. Capital Health leverages the solution to process over 1,000 non-contrast head CT scans each month, including upwards of 30 stroke cases per week. Capital Health’s quality process includes overreading a subset of cases that were read by a third-party remote radiology service overnight. One of these cases included a head trauma patient that was non-responsive when brought into the ED. The original interpretation by the night service failed to identify the presence of a suspected ICH. As part of a larger assessment of the Accipio solution in retrospective cases, Accipio Ix correctly identified a suspected ICH, in this case confirming that there was a missed ICH on the initial interpretation. What had taken hours later to find by the second read the following day, Accipio could have done in minutes. Capital Health is rolling out Accipio across the enterprise.

JD: What benefits does Nuance and its AI Marketplace for Diagnostic Imaging bring to your users?  What problems does the marketplace and integration into Nuance’s workflow solve?

RR: Our partnership through the Nuance AI Marketplace for Diagnostic Imaging will expand access to our revolutionary AI-powered ICH, stroke, and head trauma solutions to radiologists and connected healthcare facilities across the globe, who trust Nuance as a valued partner to deliver quality solutions. We view this as a powerful collaboration that will bridge the technology divide to enable more and more hospitals and healthcare organizations to seamlessly integrate our Accipio platform. Through Nuance’s cloud-based marketplace and connected PowerShare Network, we will drive potential diagnostic improvements and, in turn, improve patient outcomes and lower healthcare costs. This will fuel the best possible care in all market segments by bringing near real-time clinical confidence to the radiologist or reader.

JD: What has your experience been working with the Nuance team?

RR: The entire Nuance team has been collaborative, supportive, and excited to bring our AI algorithms and solutions to the AI Marketplace. Our organizations are committed to innovation – driving new solutions that will make a difference to healthcare and patients.

JD: What is your vision for how your solution(s) will evolve over the next 5 years?

RR: MaxQ AI is focused on the area of time-sensitive, life-threatening situations that have profound clinical and economic impact and an opportunity to help empower physicians make better decisions. For our immediate product roadmap, we will continue to focus on that area, and we anticipate additional regulatory approvals that will further extend our Accipio ICH, stroke, and head trauma platform.

Long-term, we aspire to leverage our commercially seamless adoption approach through Nuance’s intuitive and streamlined user experience to optimize the power of our medical diagnostic AI-powered solutions and provide new acute disease indications.

JD: In one sentence, tell us what you think the future of medicine will look like.

RR: We see AI as ushering in a new era of augmented healthcare through AI-powered medical diagnostic solutions, in partnership with care providers, to empower physicians around the world to better prioritize and identify life-threatening conditions in acute care settings, which will improve the quality of care, and lower system costs—all while improving the lives of the physicians themselves.

Learn more:

To learn more about MaxQ AI, visit www.maxq.ai or follow us on Twitter and LinkedIn.a

To learn more about Nuance AI Marketplace for Diagnostic Imaging, please visit https://www.nuance.com/healthcare/diagnostics-solutions/ai-marketplace.html

Intelligence at Work is a blog series by Jonathon Dreyer, Vice President, Solutions Marketing, Nuance Communications. Intelligence at Work showcases projects and applications that demonstrate how Nuance technologies extend the value, use, and performance of integration and development partner offerings. This blog series focuses on inspiring the healthcare developer community to think beyond their current state and take their innovations to new heights by tapping into the latest in artificial intelligence.

1Accipio Ix: FDA Cleared, CE Approved

2Accipio Ax: FDA Cleared, CE Approved

3Diagnostic Rule-Out, Future Device: CAUTION–Investigational device. Limited by United States and international law to investigational use.

Intelligence at Work: Qure.ai applies deep learning and artificial intelligence to streamline and improve radiologic diagnosis of chest x-rays and triage brain CTs

Qure.ai’s team of experts work to define clinically relevant problems and design real-world solutions that are deployed in 14 countries around the globe. The company seeks to improve diagnostic efficiency and accuracy in radiology, with an initial focus on chest X-rays and head CTs. Once FDA-cleared, Qure.ai algorithms will be integrated with Nuance’s next-generation reporting platform, PowerScribe One.
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Access to accurate and early diagnosis has become key to delivering quality healthcare around the globe. In many locales, the doctor-patient ratio is low, and even more so in the case of specialized practitioners such as radiologists. In underserved and remote regions, radiologist expertise is scarce, costly, and unequally distributed. Even in developed parts of the world, workloads are creating burnout issues and higher error rates for radiologists. This means that not all patients receive the most accurate, timely diagnosis. AI-driven radiology solutions can automate a lot of the routine work, saving precious time for radiologists and help mitigate clinician burnout.

Chiranjiv Singh, Chief Commercial Officer of Qure.ai, shares his insights about how Qure.ai’s algorithms aim to make radiologic diagnoses more accurate and efficient, by delivering AI capabilities within radiologists’ everyday workflows, to optimize results and deliver better patient care.

Qure.ai’s product philosophy is to solve clinical and workflow needs of customers and to go deep into certain areas rather than spreading across a spectrum of clinical areas.  Qure.ai has trained its algorithms on more than 7 million exams sourced globally and prides itself on having been validated by multiple papers in peer-reviewed research.  In line with this philosophy, Qure.ai has commercially released two algorithms to date, one focusing on detecting abnormalities in chest X-rays and the other for triage and diagnostic support of head CT scans. As of writing, the CT algorithm is 510(k) Pending with the US FDA.

Q&A

Jonathon Dreyer: Tell us about your business – when and how you started and your development journey.

CS: Qure.ai is a healthcare AI startup that applies artificial intelligence and deep learning technology to radiology imaging for quick and accurate diagnosis of diseases. Our algorithms can automatically detect clinical findings and highlight the relevant areas from X-rays, CT scans, and MRIs in a few seconds. This allows physicians to spend more quality time with patients to better understand their specific case/symptoms, communicate the diagnosis, and determine and discuss customized treatment plans – leading to better patient care.

Qure.ai was founded in 2016 by Prashant Warier and Dr. Pooja Rao. Prashant is a career data scientist and entrepreneur, and Pooja is a trained clinician. Together they bring complementary skills of engineering and medicine critical to product development. From humble beginnings in India 3 years ago, Qure.ai is now present across 14 countries through 80+ deployments and has processed more than 200,000 scans.

Our solutions have been validated and reviewed by clinicians at leading healthcare institutions such as the Massachusetts General Hospital and the Mayo Clinic, among others. The Lancet published validation of our technology, making it the first radiology AI article released by the journal. Qure.ai’s software is vendor-neutral and is deployed online with cloud-based processing capabilities integrated with the radiologists’ current reporting workflow.

JD: What AI algorithms do you have and what do they do?

CS:  We have two commercially released algorithms so far and are working to get them regulatory cleared for clinical use in the US market.

  • qXR scans abnormal chest X-rays to identify and localize 18 clinically relevant findings with an accuracy of over 95%. We have deployed this in various use cases, from screening to radiology assistance, to even post-read quality control. For example, qXR can screen for tuberculosis and is used in public health screening programs globally. When used as a point-of-care screening tool for TB, followed by immediate bacteriological/NAAT confirmation, qXR significantly reduces time to diagnosis.
  • qER is designed to triage critical cases and provide diagnostic assistance in head CT scans – a first-line diagnostic modality for patients with head injury or stroke. qER automatically detects intracranial hemorrhages (ICH) and its subtypes (intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH)), cranial fractures, midline shift and mass effect from non-contrast head CT scans.

JD: What’s the big “Aha” moment when you first show users what your AI algorithm(s) can do for them?

CS: The first Aha moment we get from customers is the depth of our capability. Unlike other AI algorithms in the market that may detect only a few findings on x-ray, we are able to detect and show accuracy numbers on 18 clinical findings from qXR. Similarly, for qER, we detect multiple sub-types of ICH along with cranial fractures, midline shift and mass effect – a larger triage capability than what most customers have seen so far from other AI vendors.

The next big Aha is when customers see our richness of peer-reviewed publications. Every AI company wants to claim high accuracy numbers, and yet there is a lack of trust among clinicians. We take this job of building trust as core to our company and therefore have invested resources to expose our algorithms to multiple independent reviews and peer-reviewed publications that help us reduce that trust deficit. The fact that our algorithms can identify and label the exact abnormalities, as well as their locations within the scans in a matter of minutes, with near-radiologist accuracy in a clinical setting, has been our biggest highlight.

Lastly, our integration within radiology workflow is the final wow! For example, we have worked with Nuance to integrate our AI algorithm outputs in PowerScribe One to allow radiologists to consume these outputs according to their preferred workflow. We are also integrating our outputs to help to prioritize radiologist worklists using PowerScribe Workflow Orchestration.

JD: What challenges or needs did you see that drove you to focus on this?

CS: Access to accurate and early diagnosis is crucial to delivering quality healthcare. In many places around the world, the availability of specialized radiology resources is limited. And even in more developed countries, the volume is increasing exponentially, putting limits on the ability of radiologists to deliver timely, accurate diagnoses.  Burnout is increasing as well as the potential for errors.  Our solutions can help automate a lot of the routine work, saving precious time for radiologists and thereby preventing clinician burnout.

We saw this as a need and simultaneously an opportunity to leverage the power of deep learning to develop solutions dedicated to this market. Our mission is to use artificial intelligence to make healthcare more accessible and affordable.

JD: What’s the number one benefit you offer?

CS: The number one benefit we offer our users is “trust and peace of mind.” This is possible only when a product is reliable and also invisible. We want our users – be it radiologists or public health experts – to focus on their patients and trust us for the accuracy of our algorithms. We also want to embed ourselves into their workflow in a manner that almost becomes invisible to their daily practice. We believe that our AI solutions shall be successful only if we are able to build integrated solutions with companies like Nuance that solve clinically relevant problems.

This is easier said than done. It means working hard to build solutions that are globally trained and validated, built on a large volume and variety of data, and embedded into diverse clinical workflows. It’s the challenge of meeting our customers’ expectations on this benefit that keeps us up at night.

JD: Are there any stories you can share about how your algorithm(s) drove measurable patient care outcomes?

CS: One of our customers is the Philippine Business for Social Progress, a local screening agency and the first adopter of artificial intelligence algorithms for tuberculosis detection in the Philippines. Working with their team, we built a custom, end-to-end TB workflow and patient registration software that helps health workers immediately refer potential TB suspects for confirmatory tests. Our solution is deployed in multiple mobile vans that move across different pockets of Manila and have been in use for >6 months. Prior to using qXR, the time to complete a patient diagnosis was >2 weeks. We have reduced that time to <1 day (from screening to x-ray to lab tests). We have identified 25% more TB cases than the original workflow and have screened 30,000+ individuals using our AI solution.

JD: What benefits does Nuance and its AI Marketplace for Diagnostic Imaging bring to your users?  What problems does the marketplace and integration into Nuance’s workflow solve?

CS: Nuance and its AI Marketplace brings two key benefits to our users. The first benefit is that it offers a single platform to review, try, and buy AI algorithms. Customers need a trusted partner with vetted solutions that connect trusted AI developers to clinical users. The Nuance AI Marketplace does this for every stakeholder in the user organization – clinicians get access to algorithms they can evaluate for clinical accuracy; IT administrators get easy integration without running multiple deployment projects with independent vendors; purchase/finance teams get streamlined negotiations and reduced time to execute multiple contracts.

The second and equally important benefit is seen once the purchase decision has been made. For our solutions to work and be used, they need to be accessible to the users when they are reviewing images and dictating their reports. We want to embed ourselves into customers’ workflow in a manner that is almost invisible to their daily practice. Nuance offers the right point and platform for this integration into the radiologist workflow for AI solutions like ours, and we are really excited to be part of this platform.

JD: What has your experience been working with the Nuance team?

CS: The experience of working with the Nuance team has been one of dealing with a team that is not only professional but also extremely knowledgeable and proficient in diagnostic imaging and reporting workflows. They understand the use cases of bringing in technologies like AI to meet real needs of their customers. I am looking forward to this partnership as we jointly work with our customers and deliver value to them.

JD: What is your vision for how your solution(s) will evolve over the next 5 years?

CS:  In the next five years, I see us offering more comprehensive solutions across various clinical domains, solving customer challenges at various points in the diagnostic journey of patients. We will enhance our capabilities by increasing our clinical coverage beyond chest x-ray and head CT that we offer today. In terms of diagnostics workflows, we see ourselves being able to offer more measurement and diagnostic tools to aid radiologists in their reads and even do tasks like treatment progression monitoring to aid other clinical users. Five years is a very long time in the field of AI, and I am confident that Qure.ai will be a dominant global player and a trusted partner for our customers over that time frame.

JD:  In one sentence, tell us what you think the future of medicine will look like?

CS: The future of medicine will be custom designed and served, focusing both on prevention and cure, and most importantly, accessible to all.

Learn more:

To learn more about Qure.ai, please visit www.qure.ai

To learn more about Nuance AI Marketplace for Diagnostic Imaging, please visit https://www.nuance.com/healthcare/diagnostics-solutions/ai-marketplace.html

Intelligence at Work is a blog series by Jonathon Dreyer, Senior Director, Solutions Marketing, Healthcare Division for Nuance Communications. Intelligence at Work showcases projects and applications that demonstrate how Nuance technologies extend the value, use, and performance of integration and development partner offerings. This blog series focuses on inspiring the healthcare developer community to think beyond their current state and take their innovations to new heights by tapping into the latest in artificial intelligence.

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Intelligence at Work: Knee Osteoarthritis Labeling Assistant (KOALA) for detecting signs of knee osteoarthritis by IBL

Read how IBL’s KOALA AI-driven application, currently pending FDA 501K approval, can help improve assessment and diagnosis of many musculoskeletal conditions and impact patient care. It will support physicians in detecting signs of knee osteoarthritis based on standard joint parameters, and help track disease progression. It is available for review on the Nuance AI Marketplace for Diagnostic Imaging, and once approved, will be integrated with Nuance’s next-generation reporting platform, PowerScribe One.
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As the population ages, arthritis and other musculoskeletal diseases are an increasing cause of physician visits and health care spending.  With increased prevalence comes an increased burden for rapid, precise diagnosis and staging, as well as an ability to predict future disability. Unfortunately, interpreting orthopedic images can be laborious. There is a need for standardization and simplification while providing quantitative disease parameters to support treatment decisions. Having precise measurements is the missing link to tracking the slow progression of degenerative diseases.

Dr. Richard Ljuhar, CEO and co-founder of ImageBiopsy Lab (IBL), shares his thoughts about how IBL’s AI-driven musculoskeletal imaging algorithms aim to improve assessment and diagnosis of a range of musculoskeletal conditions, including osteoarthritis (OA), osteoporosis, and rheumatoid arthritis.  The goal is driving timely and appropriate interventions to reduce morbidity and disability – relieving pain and improving patients’ lives.

Interpreting musculoskeletal images is a challenge due to the lack of objective analysis methods and standardized digital documentation of radiographic changes. Because of these shortcomings, diagnosis and predictive assumptions show significant inter-rater variabilities and are thus often unreliable. IBL uses state-of-the-art artificial intelligence technology to efficiently address these challenges, relieving physicians and researchers of time-consuming image analysis tasks, while at the same time improving diagnostic accuracy and predictive capability.

Q&A

Jonathon Dreyer: Tell us about your business – when and how you started and your development journey.

Richard Ljuhar: ImageBiopsy Lab (IBL) was founded by a team of experienced professionals and specialists in medical technology and AI, along with board-certified doctors in orthopedics and radiology. Based on personal experience of the management team, plus intensive discussions, brainstorming, and surveys of medical users, core elements of our AI modules have been successively worked on since 2012.  IBL was incorporated in 2016 and began implementing its business strategy. The initial focus has been on applying deep-learning methods to knee osteoarthritis (OA), and this was our first use case. But our modular platform technology is designed to be applicable to any orthopedic imaging data, so we have expanded beyond knee OA to other musculoskeletal disease applications.

JD: What AI algorithms do you have and what do they do?

RL: The focus of IBL is on digital X-ray and musculoskeletal diseases, with artificial intelligence-driven solutions for anatomical regions such as the knee, hand, hip, whole leg, and spine. Our first CE-marked/510k pending module KOALA (Knee Osteoarthritis Labeling Assistant) supports physicians in detecting signs of knee osteoarthritis based on standard joint parameters and OARSI criteria of standing radiographs of the knee. PANDA (Pediatric Bone age and Developmental Assessment) supports an objective and standardized determination of pediatric bone age. HIPPO (Hip Positioning) supports objective and standardized measurement of the most important hip angles based on digital x-rays.

JD: What’s the big “Aha” moment when you first show users what your AI algorithm(s) can do for them?

RL: A remark from Peter Steindl, MD, an orthopedic surgeon, sticks in my mind.  He said, “I guess my biggest “Aha moment” was that I realized the potential to measure and compare sclerosis, joint space narrowing, and OA-grades in an objective way in a particular patient over a couple of years. I think this device/software might be very helpful in finding the optimal timing for planning a joint replacement surgery of the patients’ knee.”

JD: What challenges or needs did you see that drove you to focus on this?

RL: After years of experience and discussions with medical experts, IBL identified that orthopedic diagnoses could benefit immensely from AI-driven solutions. Workflows are time-consuming and elaborate with interpretations often subjective and difficult to reproduce. Additionally, image reading and interpretation often hasn’t changed significantly since the introduction of radiography. The need to bring musculoskeletal/orthopedic radiology into the digital age drove our motivation to change the status quo. IBL’s software offers simplification and standardization while at the same time providing quantitative disease parameters to support treatment decisions.

JD: What’s the number one benefit you offer?

RL: While we support medical experts and their patients in numerous areas during the diagnostic pathway, we see the greatest benefit of our solutions in automation and in consistent documentations of radiological parameters. Big data and artificial intelligence cannot replace physicians, but they can relieve them of time-consuming routine tasks. This should allow medical experts to invest their time where it is most needed—with their patients!

JD: Are there any stories you can share about how your algorithm(s) drove measurable patient care outcomes?

RL: Our experience and that of our customers has shown that through our solutions there is a higher level of agreement between physicians, improved patient communication, more appropriate and timely therapy decisions, and an increase in patient loyalty.  In fact, we even had patients approaching us directly asking if we can run the digital analysis of their X-rays as they wanted to get an accurate assessment of their disease progression.

JD: What benefits does Nuance and its AI Marketplace for Diagnostic Imaging bring to your users?  What problems does the marketplace and integration into Nuance’s workflow solve?

RL: IBL and Nuance deliver their core value at the most critical interface of the radiology workflow: Translating the image information to a report. Our AI solutions facilitate this transition by providing quantitative and objective measurements. Thus, the flawless integration of our AI output to pre-fill reporting templates via Nuance delivers the most value to existing workflows. Being delivered at the heart of where radiologists’ time and decision making matters the most is what is streamlined by Nuance while proving a scalable IT infrastructure and customer base to build a win-win-win situation for IBL, Nuance and the physicians benefiting from time-saving and quality improvements.

JD: What has your experience been working with the Nuance team?

RL: We at IBL especially like the forward-thinking design of how AI results are injected to existing reporting workflows which made it highly attractive for us to collaborate. The early designs of the Nuance AI-driven solutions already reflect the experience and professionalism of a company with tremendous domain knowledge and ability to deliver the promised value of AI for physicians. Nuance’s responsive support allowed IBL to quickly ramp up demos and use cases, and we are very happy to be part of the family.

JD: What is your vision for how your solution(s) will evolve over the next 5 years?

RL: IBL will expand its portfolio of fully automated AI solutions for musculoskeletal radiology where automation matters the most – time-saving and objective outcome measures on standardized, high-volume tasks that enable easier comparison between repeated visits of the same patient. With this, the workload of the orthopedist and radiologist can decrease, while the quality of results can increase. And because precise measurements are the missing link to tracking the slow progression of certain MSK diseases, radiologists using IBL’s solutions deliver the perfect service to their referring orthopedists, who can apply IBL’s outcome measures to tailor personalized treatments and monitor their efficacy over time. The longitudinal structured data of our AI solutions supports powerful prediction models which use our AI results and clinical data to predict the future progression of the patient’s condition. This is possible due to IBL’s decade-long experience of building image processing algorithms and experience to transform immense datasets to actionable clinical decision support.

JD: In one sentence, tell us what you think the future of medicine will look like.

RL:  Automation and standardization will lead to an increasing amount of structured data which in turn will lead to a growing number of AI-applications in the years to come.

Learn more:

To learn more about ImageBiopsy Lab, please visit www.imagebiopsylab.ai

To learn more about Nuance AI Marketplace for Diagnostic Imaging, please visit https://www.nuance.com/healthcare/diagnostics-solutions/ai-marketplace.html

Intelligence at Work is a blog series by Jonathon Dreyer, Senior Director, Solutions Marketing, Healthcare Division for Nuance Communications. Intelligence at Work showcases projects and applications that demonstrate how Nuance technologies extend the value, use, and performance of integration and development partner offerings. This blog series focuses on inspiring the healthcare developer community to think beyond their current state and take their innovations to new heights by tapping into the latest in artificial intelligence.

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Intelligence at work: Visualizing functional lung tissue with LungPrint and Hyperion View by VIDA

Read how VIDA’s LungPrint Discovery AI-driven application, coming to the Nuance AI Marketplace for Diagnostic Imaging and integrated with Nuance’s next-generation reporting platform, PowerScribe One, can significantly impact the time it takes to interpret a study with a greater understanding of the underlying patient condition.
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Lung disease is among the highest causes of morbidity and mortality in the world, yet traditional imaging techniques don’t easily account for the complexity of the airway structure to make a fast, accurate diagnosis.  Due to these complexities and the increasing resolution of scanners, radiologists are challenged to review complicated reconstructions of airway trees via conventional modalities.

Susan Wood, CEO of VIDA, shares her thoughts about how VIDA’s LungPrint solution and its patent-pending and AI-driven Hyperion View airway visualization is aimed to provide greater workflow efficiencies and targeted evaluation across a range of lung conditions, including cancer, emphysema, airway obstructive diseases, asthma and interstitial lung disease.

Like a fingerprint, each lung is unique. This poses challenges in measuring lung function. LungPrint is an innovative AI-powered lung analysis solution that delivers quantitative CT information with novel airway visualizations.  It promises to empower radiologists with fully automatic lung quantification and significant boosts in reading efficiency.

VIDA’s mission is to transform pulmonary care with AI and predictive analytics to increase the efficiency, quality, and precision in reporting lung CT abnormalities.  LungPrint takes complex workflows and analyses and puts them into a context and conciseness to help radiologists’ efficiency — shaving minutes off each read.

Q&A

Jonathon Dreyer: Tell us about your business – when and how you started and your development journey.

Susan Wood: VIDA is the leading lung imaging analytics company, transforming care for pulmonary patients by empowering care teams with superior information.  VIDA has been focused on bettering outcomes and the patient journey for pulmonary patients since our beginning, over ten years ago.  We’ve developed and validated more than 30 quantitative imaging biomarkers that have been utilized in both clinical trials and clinical practice.  These biomarkers have the potential to increase the precision and personalization of lung care.  We also target a third “P” – prediction — as we develop models with the ability to assess progression and outcome probabilities.

 JD: What AI algorithms do you have and what do they do?

SW:  We offer LungPrint, starting with a product we call “LungPrint Discovery.”  It provides fully automatic quantification of lung physiology and functional tissue, including both high- and low-density analysis by lobe to flag emphysema-like and interstitial abnormalities.  It features a novel patent-pending airway visualization called “Hyperion View” with the potential to significantly accelerate interpreting complex airway anatomy.  What’s particularly exciting is that “LungPrint” is unique in every individual – like a fingerprint.  Through imaging analytics, we can uncover a unique lung profile and help providers identify personalized care plans for their patients. 

JD: What’s the big “Aha” moment when you first show users what your AI algorithm(s) can do for them?

SW:  We previewed LungPrint Discovery at RSNA 2018.  When we showed it to a renowned chest radiologist, the response was humbling: “This is a dream.  I’ve been through all these massive booths at the show and haven’t seen anything quite so unique as your Hyperion View.  Here you are with a small booth, yet, you have the most impressive technology.  How do I get it?”  When we heard that response and others like it, we knew we had something special.

JD: What challenges or needs did you see that drove you to focus on this?

SW: LungPrint helps radiologists with three key challenges:

  1. Through automation, LungPrint hopes to minimize the mundane aspects of reading a chest CT.
  2. Chest CTs are tedious to read because there are so many anatomical structures to inspect. Airways pose a unique challenge because their complexities make them difficult to visualize in any one plane.  LungPrint includes a novel, patent-pending airway visualization tool to address this challenge.  Early feedback on the feature indicates a potential for significant time savings in image review and a more complete understanding of any underlying condition.
  3. By helping radiologists provide a more precise, quantitative report to clinicians, radiologists have the potential to elevate their value among the care team.

JD:  What’s the number one benefit you offer?

SW:  Increasing the efficiency of each chest CT read while empowering radiology with auto-quantification and richer reports for referring clinicians.

 JD: Are there any stories you can share about how your algorithm(s) drove measurable patient care outcomes?

SW: One that comes to mind is the story of a 59-year-old farmer who was misdiagnosed with asthma.  Using traditional methodologies, his emphysema was missed visually; however, with the help of VIDA’s precision analysis, a lower-lobe predominant high density was flagged.  This led to additional testing and a differential diagnosis of alpha-1 emphysema. LungPrint helped to indicate the visually missed emphysema and lead to the differential diagnosis and correct treatment path for this patient.

JD: What benefits do Nuance and its AI Marketplace for Diagnostic Imaging bring to your users?  What problems does the marketplace and integration into Nuance’s workflow solve?

SW: The Nuance team has been fabulous to work with across the board.  In the course of our joint interactions, starting at the executive leadership level, to the product development teams, to the commercial implementation teams, we are seeing the very strategic fit between Nuance and VIDA take concrete form.  Specifically, the seamless integration of VIDA’s LungPrint directly into the Nuance platform provides the workflow efficiencies required for clinical acceptance of this AI solution. The depth and breadth of the Nuance team, as well as the collaborative approach, has been an exceptional experience for VIDA.  We are excited to be working with Nuance on our shared vision of bringing the power of AI to the radiologist in routine clinical practice.

JD: What is your vision for how your solution(s) will evolve over the next 5 years?

SW: We see so much potential for LungPrint through the Nuance platform.  Step one, as we’ve described, is all about clinical efficiency.  Features like Hyperion View (airway analysis) and auto-quantification by lobe will have a material effect on both chest CT report value and interpretation efficiency.

Looking beyond efficiency gains, we see exciting opportunities to make a deep clinical impact throughout the care path.  For example:

  • Detection – AI will help flag areas of interest. Beyond nodules, there are several anomalies in the thorax with which we can assist in identifying.
  • Diagnosis – Many lung diseases are diagnosed late or inaccurately because of limited evaluation methods. There is greater potential in the future for AI to provide significant value beyond detection and into disease stratification and identification of risk. We hope to help care teams identify a correct diagnosis early, leading to disease management strategies that can be employed while quality of life is high and care costs are low.
  • Disease Monitoring –Tracking of disease progression with more objective and precise information can empower physicians to make highly informed care decisions on objective, actionable data. This area is where AI can uncover hidden insights, flagging at-risk patients and predicting adverse events before they happen.
  • Treatment Selection – AI will increasingly serve a decision support role in the selection of treatments or combinations of treatments. We foresee a day where a set of potential treatment options are input into an AI model and the model outputs outcome and risk predictions to aid the physician.

In summary, we see the application of AI expanding along two axes: (1) along the care path to address needs from detection through treatment and (2) along a path of increasing maturity to the point of being truly predictive. We intend to build our product portfolio over the next 5 years to tackle many challenges in these areas.

JD: In one sentence, tell us what you think the future of medicine will look like.

SW: Healthcare will be increasingly precise, personalized, and predictive, driven in large part by the evidence and in AI-powered assistance throughout the care path.

Learn more:

Learn more about VIDA.

Learn more about Nuance AI Marketplace for Diagnostic Imaging. 

Intelligence at Work is a blog series by Jonathon Dreyer, Senior Director, Solutions Marketing, Healthcare Division for Nuance Communications. Intelligence at Work showcases projects and applications that demonstrate how Nuance technologies extend the value, use and performance of integration and development partner offerings. This blog series focuses on inspiring the healthcare developer community to think beyond their current state and take their innovations to new heights by tapping into the latest in artificial intelligence.

 

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Nuance customer TorTalk develops innovative OCR-powered text-to-speech solution for dyslexics

We love hearing stories from customers who achieve success using our industry leading OCR toolkit, and we’ve got one that’s too good not to share. We met Tor Ghai at our OmniPage CSDK Developer’s Forum earlier this year in Palma de Mallorca – here’s his story.
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Approximately 6 percent of Swedes struggle with dyslexia, including Swedish software developer Tor Ghai. For most of his life, he struggled with reading comprehension as he battled his way through required textbooks. As an adult, he searched for a way to make reading easier – and even enjoyable – so that he could stay on top of the latest in software development and maybe even read for fun. After developing a program that would read text to sight-impaired people and becoming familiar with text-to-speech engines, Tor began to develop a version for dyslexics like himself.
 

Accuracy is paramount for reading

However, finding an OCR engine to work with images of text was harder than it sounded, especially when it came to pairing the OCR engine with a voice engine that would speak the text to the user. While plenty of solutions exist for both, most of the vocalizer solutions would mangle approximately 20 percent of the words on a page – not terrible if you’re only reading one page, but cumbersome if you’re trying to read along with the audio in a multi-page document or a book. Misspeaking the word could derail the user’s train of thought. And, sometimes, that missing 20 percent of information can mean the difference between understanding what you just read and being utterly confused. Additionally, most OCR products require high-quality images, which isn’t always the case in practice. Building an OCR engine was out of the question, as it would take a lot of time.

Of all the products tested, Nuance OmniPage Capture SDK and Nuance Vocalizer had the highest rates of accuracy for recognizing, processing, and speaking text. Combining the two would make it possible for TorTalk users to get the most accurate readings. While the product will sometimes miss a word, the flow is smoother for those reading along with the text.

Additionally, the Nuance OmniPage Capture SDK provides real-time OCR capabilities. In one to two seconds, TorTalk activates the SDK to process the text, then read it to the user. The user simply clicks a button, and the program starts reading the text, whether it’s a single page or an e-book.

 

Nuance support eases development

Tor wanted to ensure TorTalk would work exactly as planned. He had a lot of questions for Nuance support, probably more questions than the average developer, he admitted. However, Nuance, and our London representative in particular, was very responsive to questions and provided him with the information he needed. Tor attended the OmniPage Capture SDK Developer’s Forum in Palma de Mallorca this past May and was able to learn more about SDK and how he can develop with it, as well as network with other developers and share ideas.
 

Capturing the university market

With Nuance OmniPage Capture SDK as its OCR backbone, and Nuance Vocalizer as the text-to-speech engine, Swedish universities quickly became interested in using TorTalk for its students. Tor had originally developed TorTalk for Windows operating systems, but with universities clamoring at his door for a Mac version, he found that, with the Nuance OmniPage Capture SDK, he could just move most of the original code to a Mac solution.

Today, 75 percent of Swedish universities, including Goteborgs Universitet and Uppsala Universitet, use TorTalk for their students. In the most common case, a student will load a PDF or ebook onto a computer screen, then place the TorTalk window around the portion to be read. If it’s electronic text, the student will highlight the text and press play. The text to speech engine then reads the text to the student. The biggest value is in the accuracy and speed, which helps students regain confidence in their ability to learn – something that Tor himself felt as he used TorTalk.

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