Tag: imaging AI

Unlocking Body Composition Insights with Voronoi Health Analytics

Posted on by Partner Content

Body composition plays a pivotal role in monitoring organ and tissue health and predicting treatment outcomes. Accurate changes in body composition metrics can indicate reduced muscle quantity and quality – a sign of sarcopenia – as well as altered fat distribution in organs such as the liver in metabolic diseases, epicardial and paracardial fats in cardiovascular health, and more.

However, manual segmentation is time-consuming and labor-intensive. 

  • Voronoi Health Analytics eliminates this bottleneck by combining cutting-edge AI with efficient visualization tools, automating the extraction of body composition metrics from CT and MRI scans. The company’s solutions transform imaging data into actionable insights, improving patient outcomes.

Voronoi Health Analytics provides innovative, intuitive AI tools that enable clinicians and researchers to extract quantitative body composition measurements rapidly and with high accuracy – no programming required. 

  • The company’s platforms are trusted by over 175 research labs across 25 countries, with numerous publications validating their accuracy and impact on clinical care and medical research.

Voronoi has two flagship solutions …

  • DAFS: A comprehensive 3D segmentation platform for analyzing multiple tissues, organs, lesions, and vasculature across CT and PET/CT imaging. DAFS also overlays CT segmentations onto PET scans, enabling rapid, high-accuracy assessments of PET tracer uptake in organs, tissues, and lesions.
  • DAFS Express: Optimized for single-slice body composition analysis from CT and MRI scans, this tool delivers precise measurements of skeletal muscle, visceral fat, intermuscular fat, and subcutaneous fat in seconds, making it ideal for high-throughput clinical settings.

Accurate body composition analysis is critical for staging body habitus, detecting onset of signatures of adverse health such as metabolic or cardiovascular disorders, evaluating disease progression, and monitoring organ and tissue health as a function of disease and intervention. Voronoi’s platforms address key challenges such as …

  • Reducing Workloads: Automate routine segmentation tasks and allow clinicians to focus on complex cases.
  • Improving Precision: Deliver consistent, reproducible results across patients and studies.
  • Advancing Care: Provide predictive insights that help optimize treatment strategies.

DAFS and DAFS Express seamlessly integrate into existing imaging workflows, enhancing efficiency without disrupting operations.

Body composition analysis goes beyond measuring muscle and fat. It quantifies all organs and tissues, creating data that drives predictive models. 

  • Voronoi’s vision is to empower healthcare professionals with tools that simplify complexity, support proactive care, and enhance patient outcomes.

Discover how Voronoi Health Analytics is revolutionizing body composition analysis. Visit the company’s website to request a demo and elevate your workflow today.

AI As Malpractice Safety Net

Posted on by Brian Casey

One of the emerging use cases for AI in radiology is as a safety net that could help hospitals avoid malpractice cases by catching errors made by radiologists before they can cause patient harm. The topic was reviewed in a Sunday presentation at RSNA 2024. 

Clinical AI adoption has been held back by economic factors such as limited reimbursement and the lack of strong return on investment. 

  • Healthcare providers want to know that their AI investments will pay off, either through direct reimbursement from payors or improved operational efficiency.

At the same time, providers face rising malpractice risk, with a number of recent high-profile legal cases.

  • For example, a New York hospital was hit with a $120M verdict after a resident physician working the night shift missed a pulmonary embolism. 

Could AI limit risk by acting as a backstop to radiologists? 

  • At RSNA 2024, Benjamin Strong, MD, chief medical officer at vRad, described how they have deployed AI as a QA safety net. 

vRad mostly develops its own AI algorithms, with the first algorithm deployed in 2015. 

  • vRad is running AI algorithms as a backstop for 13 critical pathologies, from aortic dissection to superior mesenteric artery occlusion.

vRad’s QA workflow begins after the radiologist issues a final report (without using AI), and an algorithm then reviews the report automatically. 

  • If discrepancies are found the report is sent to a second radiologist, who can kick the study back to the original radiologist if they believe an error has occurred. The entire process takes 20 minutes. 

In a review of the program over one year, vRad found …

  • Corrections were made for about 1.5k diagnoses out of 6.7M exams.
  • The top five AI models accounted for over $8M in medical malpractice savings. 
  • Three pathologies – spinal epidural abscess, aortic dissection, and ischemic bowel due to SMA occlusion – would have amounted to $18M in payouts over four years.
  • Adding intracranial hemorrhage and pulmonary embolism creates what Strong called the “Big Five” of pathologies that are either the most frequently missed or the most expensive when missed.

The Takeaway

The findings offer an intriguing new use case for AI adoption. Avoiding just one malpractice verdict or settlement would more than pay for the cost of AI installation, in most cases many times over. How’s that for return on investment?

RSNA 2024 Video Highlights

Posted on by Brian Casey

Last week’s RSNA 2024 meeting saw a major bounce in attendance, with early numbers indicating an 18% jump in the number of radiology professionals wandering the halls of McCormick Place. The increase brought total attendance at midweek to 40k. 

As in past years, AI dominated the discussion, both in the presentation rooms and on the exhibit floor. Researchers presented the latest findings on AI’s ability to aid radiologists, while vendors showcased new algorithms for use cases from mammography screening to fracture detection. New technologies like foundation models for AI training bubbled under the surface and promise to have a major impact in years to come.

It was our privilege to speak with many of the most interesting vendors exhibiting at RSNA 2024, from multinational vendors to small but promising start-ups.

We hope you enjoy watching our coverage as much as we enjoyed producing it! Check out the links below or visit the Shows page on our website.

How Are Doctors Using AI?

Posted on by Brian Casey

How are healthcare providers who have adopted AI really using it? A new Medscape/HIMSS survey found that most providers are using AI for administrative tasks, while medical image analysis is also one of the top AI use cases. 

AI has the potential to revolutionize healthcare, but many industry observers have been frustrated with the slow pace of clinical adoption. 

  • Implementation challenges, regulatory issues, and lack of reimbursement are among the reasons keeping more healthcare providers from embracing the technology.

But the Medscape/HIMSS survey shows some early successes for AI … as well as lingering questions. 

  • Researchers surveyed a total of 846 people in the U.S. who were either executive or clinical leaders, practicing physicians or nurses, or IT professionals, and whose practices were already using AI in some way.

The top four tasks for which AI is being used were administrative rather than clinical, with image analysis occupying the fifth spot … 

  1. Transcribing patient notes (36%). 
  2. Transcribing business meetings (32%).
  3. Creating routine patient communications (29%).
  4. Performing patient record-keeping (27%).
  5. Analyzing medical images (26%).

The survey also analyzed attitudes toward AI, finding …

  • 57% said AI helped them be more efficient and productive.
  • But lower marks were given for reducing staff hours (10%) and lowering costs (31%).
  • AI got the highest marks for helping with transcription of business meetings (77%) and patient notes (73%), reviewing medical literature (72%), and medical image analysis (70%).

The findings track well with developments at last week’s RSNA 2024, where AI algorithms dedicated to non-clinical tasks like radiology report generation, scheduling, and operation analysis showed growing prominence. 

  • Indeed, many AI developers have specifically targeted the non-clinical space, both because commercialization is easier (FDA authorization is not typically needed) and because doctors often say they need more help with administrative rather than clinical tasks.

The Takeaway

While it’s easy to be impatient with AI’s slow uptake, the Medscape/HIMSS survey shows that AI adoption is indeed occurring at medical practices. And while image analysis was radiology’s first AI use case, speeding up workflow and administrative tasks may end up being the technology’s most impactful application.

RSNA Goes All-In on AI

Posted on by Brian Casey

CHICAGO – It’s been AI all the time this week at RSNA 2024. From clinical sessions packed with the latest findings on AI’s utility to technical exhibits crowded with AI vendors, artificial intelligence and its impact on radiology was easily the hottest trend at McCormick Place.

Radiology greeted AI with initial skepticism when the first applications like IBM Watson were introduced at RSNA around a decade ago.

  • But the field’s attitude has been evolving to the point where AI is now being viewed as perhaps the only technology that can save the discipline from the vicious cycle of rising exam volume, falling reimbursement, and pervasive levels of burnout.

RSNA telegraphed the shift last year by announcing that Stanford University’s Curtis Langlotz, MD, PhD, would be RSNA 2024 president. 

  • Langlotz is one of the most respected AI researchers and educators in radiology, and even coined the phrase that while AI would not replace radiologists, radiologists with AI would replace those without it. 

In his president’s address, Langlotz echoed this theme, painting a picture of a future radiology in which humans and machines collaborate to deliver better patient care than either could alone.

  • Langlotz’s talk was followed by a presentation by another prominent AI luminary – Nina Kottler, MD, of Radiology Partners.

Kottler took on the concerns that many in radiology (and in the world at large) have about AI as a disruptive force in a field that cherishes its traditions.

  • She advised radiology to take a leading role in AI adoption, repeating a famous quote that the best way to predict the future is to create it yourself. 

What were the other trends besides AI at RSNA 2024? They included…

  • Photon-counting CT, which is likely to see new market entrants in 2025.
  • Total-body PET, with PET scanners that have extra-long detector arrays.
  • Theranostics, a discipline that integrates diagnosis and therapy and promises to breathe new life into SPECT.
  • CT colonography and CCTA, which will see positive reimbursement changes in 2025.
  • Continued growth of CT lung screening, especially as a tool for opportunistic screening of other conditions.
  • Continued expansion of AI for breast screening.

The Takeaway

The RSNA meeting has been called radiology’s Super Bowl and World Cup all rolled into one, and this year didn’t disappoint. RSNA 2024 showed that radiology is prepared to fully embrace AI – and a future in which humans and machines collaborate to deliver better patient care.

Mammo AI Kicks Off RSNA 2024

Posted on by Brian Casey

Welcome to RSNA 2024! This year’s meeting is starting with a bang, with two important sessions highlighting the key role AI can play in breast screening. 

Sunday’s presentations cap a year that’s seen the publication of several large studies demonstrating that AI can improve breast cancer screening while potentially reducing radiologist workload. 

  • That momentum is continuing at RSNA 2024, with morning and afternoon sessions on Sunday dedicated to mammography AI. 

Some findings from yesterday’s morning session include … 

  • Two AI algorithms were better than one when supporting radiologists in breast screening, with cancer detection ratios relative to historic performance rising from 0.97 to 1.08 with one AI to 1.09 to 1.14 with two algorithms.
  • ScreenPoint Medical’s Transpara algorithm was able to prioritize the worklist for 57% of breast screening exams by assigning risk scores to mammograms, helping reduce report turnaround times. 
  • iCAD’s ProFound AI software helped radiologists detect 7.8% more breast cancers on DBT exams, and cancers were detected at an earlier stage. 
  • Applying AI for breast screening to a racially diverse population yielded evenly distributed performance improvements.

Meanwhile, the Sunday afternoon session also included significant mammography AI presentations, such as …

  • A hybrid screening strategy – with suspicious breast cancer cases only recalled if the AI exhibits high certainty – reduced workload 50%. 
  • Lunit’s Insight DBT AI showed potential to reduce interval cancer rates in DBT screening by identifying 27% of false-negative and 36% of interval cancers.
  • In the ScreenTrustCAD trial in Sweden, using Lunit’s Insight MMG algorithm to replace a double-reading radiologist reduced workload 50% with comparable cancer detection rates.
  • A German screening program found that ScreenPoint Medical’s Transpara AI boosted the cancer detection rate by 8.7% (from 0.68% to 0.74%), with 8.8% of cancers solely detected by AI.
  • Researchers took a look back at abnormality scores from three commercially available AI algorithms after cancer diagnosis, finding evidence that cancers could be detected earlier. 

The Takeaway

Breast screening seems to be the clinical use case where radiologists need the most help, and Sunday’s sessions show the progress AI is making toward achieving that reality. 

Be sure to check back on our X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago. And if you see us on the floor of McCormick Place, stop and say hello!

Using AI-Powered Automation to Help Solve Today’s Radiology Crisis

Posted on by Partner Content

Reimbursement cuts. Radiologist and staff shortages. Rising costs. Surging imaging volumes. Overwhelming staff workloads. Shrinking margins. 

Sound familiar?

Radiology departments, imaging centers, and radiology practices are facing a perfect storm of challenges to deliver high-quality patient care while remaining profitable and competitive. 

  • This familiar narrative emphasizes the need for change and to embrace automation, AI, and technology solutions that automate routine tasks. 

RADIN Health has developed an innovative, cloud-based (SaaS), all-in-one technology stack based on the firsthand experience of radiologist Alejandro Bugnone, MD, CEO and medical director of Total Medical Imaging (TMI), a teleradiology group that reads for imaging centers and hospital systems nationally.  

  • Dr. Bugnone and his team of radiologists were similarly suffering from supply and demand imbalance, reimbursement cuts, increasing study volumes, and customer pressures to maintain their margin. 

As a software developer and seasoned radiologist, Dr. Bugnone was equally frustrated by the lack of a comprehensive, end-to-end technology solution in the market to address these same issues for his teleradiology practice.  

  • In evaluating numerous RIS, PACS, AI voice recognition, and workflow management solutions, his team found that each required expensive interfaces, separate company fees, and ongoing support, yet as an ecosystem still did not deliver a seamless experience that would provide a return on investment. 

An alternative is a system based on straight-through processing, a concept first pioneered in the financial services industry in which automation electronically processes transactions without manual intervention. 

“I knew there had to be a better way forward. I founded RADIN Health for healthcare and teleradiology practices [like TMI], imaging centers, and radiology departments based on straight-through processing, similar to how Wall Street sped up financial transactions without any human intervention,” Dr. Bugnone said. 

RADIN Health is a cloud-based platform that combines RIS, PACS, dictation AI, and workflow management into an all-in-one software solution. 

  • It leverages artificial intelligence, machine learning, OCR/AI, natural language processing (NLP), and other intellectual property.

Dr. Bugnone said TMI has achieved remarkable efficiencies with RADIN. 

“Our results at TMI have been staggering since implementing RADIN over the past 18 months for our complex teleradiology practice,” Dr. Bugnone noted. “With RADIN DICTATION AI, our radiologists have increased their productivity and efficiency, reducing dictation times 30% to 50%.” 

By adding RADIN SELECT, TMI reduced its SLAs more than 50% and FTEs by 70% for managing operational workflow tasks, all while adding 35% in study volumes.  

  • RADIN’s all-in-one technology solution has enabled Total Medical Imaging to meet the challenges of the radiology crisis without hiring new personnel – simply by unlocking the efficiency of their existing staff. 

“We have enjoyed significant growth in 2024 without the need to hire additional staff,” Dr. Bugnone concluded.

Watch the video below to see how RADIN’s all-in-one solution can help your practice.

Low-Dose CT Confounds CAD in Kids

Posted on by Brian Casey

When it comes to pediatric CT scans, clinicians should make every effort to reduce dose as much as possible. But a new study in AJR indicates that lower CT radiation dose can affect the performance of software tools like computer-aided detection. 

Initiatives like the Image Wisely and Image Gently projects have succeeded in raising awareness of radiation dose and have helped radiologists find ways to reduce it.

But every little bit counts in pediatric dose reduction, especially given that one CT exam can raise the risk of developing cancer by 0.35%. 

  • Imaging tools like AI and CAD could help, but there have been few studies examining the performance of pulmonary CAD software developed for adults in analyzing scans of children.

To address that gap, researchers including radiologists from Cincinnati Children’s Hospital Medical Center investigated the performance of two open-source CAD algorithms trained on adults for detecting lung nodules in 73 patients with a mean age of 14.7 years. 

  • The algorithms included FlyerScan, a CAD developed by the authors, and MONAI, an open-source project for deep learning in medical imaging. 

Scans were acquired at standard-dose (mean effective dose=1.77 mSv) and low-dose (mean effective dose=0.32 mSv) levels, with the results showing that both algorithms turned in lower performance at lower radiation dose for nodules 3-30 mm … 

  • FlyerScan saw its sensitivity decline (77% vs. 67%) and detected fewer 3mm lung nodules (33 vs. 24).
  • MONAI also saw lower sensitivity (68% vs. 62%) and detected fewer 3mm lung nodules (16 vs. 13).
  • Reduced sensitivity was more pronounced for nodules less than 5 mm.

The findings should be taken with a grain of salt, as the open-source algorithms were not originally trained on pediatric data.

  • But the results do underscore the challenge in developing image analysis software optimized for pediatric applications.

The Takeaway

With respect to low radiation dose and high AI accuracy in CT scans of kids, radiologists may not be able to have their cake and eat it too – yet. More work will be needed before AI solutions developed for adults can be used in children.

Mammography AI Predicts Cancer Before It’s Detected

Posted on by Brian Casey

A new study highlights the predictive power of AI for mammography screening – before cancers are even detected. Researchers in a study JAMA Network Open found that risk scores generated by Lunit’s Insight MMG algorithm predicted which women would develop breast cancer – years before radiologists found it on mammograms. 

Mammography image analysis has always been one of the most promising use cases for AI – even dating back to the days of computer-aided detection in the early 2000s. 

  • Most mammography AI developers have focused on helping radiologists identify suspicious lesions on mammograms, or triage low-risk studies so they don’t require extra review.

But a funny thing has happened during clinical use of these algorithms – radiologists found that AI-generated risk scores appeared to predict future breast cancers before they could be seen on mammograms. 

  • Insight MMG marks areas of concern and generates a risk score of 0-100 for the presence of breast cancer (higher numbers are worse). 

Researchers decided to investigate the risk scores’ predictive power by applying Insight MMG to screening mammography exams acquired in the BreastScreen Norway program over three biennial rounds of screening from 2004 to 2018. 

  • They then correlated AI risk scores to clinical outcomes in exams for 116k women for up to six years after the initial screening round.

Major findings of the study included … 

  • AI risk scores were higher for women who later developed cancer, 4-6 years before the cancer was detected.
  • The difference in risk scores increased over three screening rounds, from 21 points in the first round to 79 points in the third round.
  • Risk scores had very high accuracy by the third round (AUC=0.93).
  • AI scores were more accurate than existing risk tools like the Tyrer-Cuzick model.

How could AI risk scores be used in clinical practice? 

  • Women without detectable cancer but with high scores could be directed to shorter screening intervals or screening with supplemental modalities like ultrasound or MRI.

The Takeaway
It’s hard to overstate the significance of the new results. While AI for direct mammography image interpretation still seems to be having trouble catching on (just like CAD did), risk prediction is a use case that could direct more effective breast screening. The study is also a major coup for Lunit, continuing a string of impressive clinical results with the company’s technology.

Imaging News from ESC 2024

Posted on by Brian Casey

The European Society of Cardiology annual meeting concluded on September 2 in London, with around 32k clinicians from 171 countries attending some 4.4k presentations. Organizers reported that attendance finally rebounded to pre-COVID numbers. 

While much of ESC 2024 focused on treatments for cardiovascular disease, diagnosis with medical imaging still played a prominent role. 

  • Cardiac CT dominated many ESC sessions, and AI showed it is nearly as hot in cardiology as it is in radiology. 

Major imaging-related ESC presentations included…

  • A track on cardiac CT that underscored CT’s prognostic value:
    • Myocardial revascularization patients who got FFR-CT had lower hazard ratios for MACE and all-cause mortality (HR=0.73 and 0.48).
    • Incidental coronary artery anomalies appeared on 1.45% of CCTA scans for patients with suspected coronary artery disease.
  • AI flexed its muscles in a machine learning track:
    • AI of low-dose CT scans had an AUC of 0.95 for predicting pulmonary congestion, a sign of acute heart failure. 
    • Echocardiography AI identified HFpEF with higher AUC than clinical models (0.75 vs. 0.69).
    • AI of transthoracic echo detected hypertrophic cardiomyopathy with AUC=0.85.

Another ESC hot topic was CT for calculating coronary artery calcium (CAC) scores, a possible predictor of heart disease. Sessions found … 

  • AI-generated volumetry of cardiac chambers based on CAC scans better predicted cardiovascular events than Agatston scores over 15 years of follow-up in an analysis of 5.8k patients from the MESA study. 
  • AI-CAC with CT was comparable to cardiac MRI read by humans for predicting atrial fibrillation (0.802 vs. 0.798) and stroke (0.762 vs. 0.751) over 15 years, which could give an edge to AI-CAC given its automated nature.
  • An AI algorithm enabled opportunistic screening of CAC quantification from non-gated chest CT scans of 631 patients, finding high CAC scores in 13%. Many got statins, while 22 got additional imaging and 2 intervention.
  • AI-generated CAC scores were also highlighted in a Polish study, detecting CAC on contrast CT at a rate comparable to humans on non-contrast CT (77% vs. 79%), possibly eliminating the need for additional non-contrast CT.  

The Takeaway

This week’s ESC 2024 sessions demonstrate the vital role of imaging in diagnosing and treating cardiovascular disease. While radiologists may not control the patients, they can always apply knowledge of advances in other disciplines to their work.

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