Category: Artificial Intelligence

Detecting the Radiographically Occult

Posted on by Jake Fishman

A new study published in European Heart Journal – Digital Health suggests that AI can detect aortic stenosis (AS) in chest X-rays, which would be a major breakthrough if confirmed, but will be met with plenty of skepticism until then.

The Models – The Japan-based research team trained/validated/tested three DL models using 10,433 CXRs from 5,638 patients (all from the same institution), using echocardiography assessments to label each image as AS-positive or AS-negative.

The Results – The best performing model detected AS-positive patients with an 0.83 AUC, while achieving 83% sensitivity, 69% specificity, 71% accuracy, and a 97% negative predictive value (but… a 23% PPV). Given the widespread use and availability of CXRs, these results were good enough for the authors to suggest that their DL model could be a valuable way to detect aortic stenosis.

The Response – The folks on radiology/AI Twitter found these results “hard to believe,” given that human rads can’t detect aortic stenosis in CXRs with much better accuracy than a coin flip, and considering that these models were only trained/validated/tested with internal data. The conversation also revealed a growing level of AI study fatigue that will likely become worse if journals don’t start enforcing higher research standards (e.g. external validation, mentioning confounding factors, addressing the 23% PPV, maybe adding an editorial).

The Takeaway – Twitter’s MDs and PhDs love to critique study methodology, but this thread was a particularly helpful reminder of what potential AI users are looking for in AI studies — especially studies that claim AI can detect a condition that’s barely detectable by human experts.

Trained to Underdiagnose

Posted on by Jake Fishman

A new Nature study suggests that imaging AI models might underdiagnose patient populations who are also underdiagnosed in the real world, revealing new ethical and clinical challenges for AI development, regulation, and adoption.

The Study – The researchers trained four AI models to predict whether images would have positive diagnostic findings using three large/diverse public CXR datasets (one model w/ each dataset, one w/ combined dataset, 707k total images). They then analyzed model performance across various patient populations.

The Underdiagnosed – The AI models were mostly likely to underdiagnose patients who are female, young (0-20yrs), Hispanic and Black, and covered by Medicaid (low-income). AI underdiagnosis rates were even more extreme among patients who belonged to multiple underserved groups, such as Hispanic females or younger Black patients.

The Overdiagnosed – As you might expect, healthy patients who were incorrectly flagged by the AI models as unhealthy were usually male, older, White, and higher income.

The Clinical Impact – In clinical use, a model like this would result in traditionally underserved patients experiencing more missed diagnoses and delayed treatments, while traditionally advantaged patients might undergo more unnecessary tests and treatments. And we know from previous research that AI can independently detect patient race in scans (even if we don’t know why).

The Takeaway – AI developers have been working to reduce racial/social bias in their models by using diverse datasets, but it appears that they could be introducing more systemic biases in the process (or even amplifying them). These biases certainly aren’t AI developers’ fault, but they still add to the list of data source problems that developers will have to solve.

The State of AI

Posted on by Jake Fishman

A group of radiology leaders starred in Canon Medical’s recent State of AI in Radiology Today Roundtable, sharing insights into how imaging AI is being used, where it’s needed most, and how AI might assume a core role in medical imaging.

The panelists were largely from the user/clinical side of imaging (U of Maryland’s Eliot Siegel, MD; UC Irvine’s Peter Chang, MD; UHS Delaware’s Cindy Siegel, CRA; U of Toronto’s Patrik Rogalla, MD; and Canon’s Director of Healthcare Economics Tom Szostak), with deeper AI experience than many typical radiology team members.

Here are some of the big takeaways:

We’re Still Early – The panel started by making sure everyone agrees on the definition of AI and much of ensuing discussions focused on AI’s future potential, which says a lot about where we are in AI’s lifecycle.

Do We Need AI? – The panelists agreed that radiology does indeed need AI, largely because it can improve the patient experience (shorter scans, faster results, fewer call-backs), help solve radiology’s inefficiency problems, and improve diagnostic accuracy.

Does AI Really Improve Efficiency? – Outside of image reconstruction, none of the panelists were ready to say that AI currently makes radiologists faster. However, they still believe that AI will improve future radiology workflows and outcomes.

Finding The Killer App – Things got a lot more theoretical at the halfway point, when the conversation shifted to what “killer apps” might bring imaging AI into mainstream use, including AI tools that:

  • Identify and exclude normal scans with extremely high accuracy (must be far more accurate than humans and limit false positives)
  • Curate and submit all CMS quality reporting metrics (eliminates admin work, generates revenue)
  • Identify early-stage diseases for population health programs (keeps current diagnostic workflows intact)
  • Interpret and diagnose all X-ray exams (eliminates high volume/repetitive exams, rads don’t read some XRs in many countries)
  • Improve image quality, allow faster scans, reduce dosage (aka DL image reconstruction)

AI’s Radiologist Impact – The panelists don’t see AI threatening radiologist jobs in the short to mid-term given AI’s current immaturity, the “tremendous inefficiencies” that still exist in radiology, and the pace of imaging volume growth. They also expect volume growth to drive longer term demand for both AI and rads, suggesting that AI adoption might even amplify future volume growth (if AI expands bandwidth and cuts interpretation costs, the laws of economics suggest that more scans would follow).

What AI Needs – With most of the technical parts of building algorithms now figured out, AI’s evolution will depend on getting enough training data, improving how AI is integrated into workflows, and making sure AI is solving radiology’s biggest problems. Imaging AI also needs healthcare to be open to change, which would require clear clinical, operational, and financial upsides.

Arterys’ Platform Expansion

Posted on by Jake Fishman

At a time when many major AI companies are trying to become AI platform companies, Arterys announced a trio of 3rd party AI alliances that showed how a mature AI platform might work.

Arterys Expands Neuro AI – Arterys launched neuroradiology AI alliances with Combinostics and Cercare Medical, expanding Arterys’ already-comprehensive Neuro AI suite (also includes MRI brain tumor diagnostics, CT stroke & ICH detection, 2D/4D Flow brain MRI). Combinostics’ cNeuro cMRI supports multiple neurological disorder assessments (specifically dementia and multiple sclerosis), while Cercare Perfusion automates brain CT and MRI perfusion map generation and stoke treatment decision making. 

Arterys Adds Breast AI – Arterys launched a global distribution agreement with iCAD, making iCAD’s full suite of breast health solutions available in Arterys’ new Breast AI suite. iCAD’s portfolio is certainly broad enough to deserve its own “suite,” ranging from 2D and 3D mammography detection, personalized risk screening and decision support, and density assessments. The Arterys Breast AI suite also makes iCAD available as a cloud-based SaaS solution for the first time (previously only on-premise).

Arterys Platform Impact – Arterys’ integration of multiple complementary AI tools within curated AI Suites is unique and makes a lot of sense. It seems to be far more helpful to provide neurorads with integrated access to a suite of neuro AI tools, than to provide them with one or two tools for every subspecialty.

The Takeaway – Arterys’ new alliances reveal a far more subspecialist-targeted approach than we usually see from AI platforms or marketplaces. It also shows that Arterys is committed to its vendor neutral strategy, effectively doubling its number of active AI partners (previously: Imaging Biometrics & Avicenna.AI in Neuro suite, MILVUE in Chest MSK Suite), while highlighting the value of its cloud-native structure for integrating new partners within the same user interface.

Viz.ai’s Care Coordination Expansion

Posted on by Jake Fishman

Viz.ai advanced its care coordination strategy last week, launching new Pulmonary Embolism and Aortic Disease modules, and unveiling its forthcoming Viz ANX cerebral aneurysm module.

PE & Aortic Modules – The new PE and Aortic modules use AI to quickly detect pulmonary embolisms and aortic dissection in CTA scans, and then coordinate care using Viz.ai’s 3D mobile viewer and clinical communications workflows. It appears that Viz.ai partnered with Avicenna.AI to create these modules, representing a logical way for Viz.ai to quickly expand its portfolio.

Viz ANX Module – The forthcoming Viz ANX module will use the 510k-pending Viz ANX algorithm to automatically detect suspected cerebral aneurysms in CTAs, and then leverage the Viz Platform for care coordination.

Viz.ai’s Care Coordination Strategy – Viz.ai called itself “the leader in AI-powered care coordination” a total of six times in these two announcements, and the company has definitely earned this title for stroke detection/coordination. Adding new modules to the Viz Platform is how Viz.ai could earn “leadership” status across all other imaging-detected emergent conditions.

The Takeaway – Viz.ai’s stroke detection/coordination platform has been among the biggest imaging AI success stories, making its efforts to expand to new AI-based detection and care coordination areas notable (and pretty smart). These module launches are also an example of diagnostic AI’s growing role throughout care pathways, showing how AI can add clinical value beyond the reading room.

Right Diagnoses, Wrong Reasons

Posted on by Jake Fishman

An AJR study shared new evidence of how X-ray image labels influence deep learning decision making, while revealing one way developers can address this issue.

Confounding History – Although already well known by AI insiders, label and laterality-based AI shortcuts made headlines last year when they were blamed for many COVID algorithms’ poor real-world performance. 

The Study – Using 40k images from Stanford’s MURA dataset, the researchers trained three CNNs to detect abnormalities in upper extremity X-rays. They then tested the models for detection accuracy and used a heatmap tool to identify the parts of the images that the CNNs emphasized. As you might expect, labels played a major role in both accuracy and decision making.

  • The model trained on complete images (bones & labels) achieved an 0.844 AUC, but based 89% of its decisions on the radiographs’ laterality/labels.
  • The model trained without labels or laterality (only bones) detected abnormalities with a higher 0.857 AUC and attributed 91% of its decision to bone features.
  • The model trained with only laterality and labels (no bones) still achieved an 0.638 AUC, showing that AI interprets certain labels as a sign of abnormalities. 

The Takeaway – Labels are just about as common on X-rays as actual anatomy, and it turns out that they could have an even greater influence on AI decision making. Because of that, the authors urged AI developers to address confounding image features during the curation process (potentially by covering labels) and encouraged AI users to screen CNNs for these issues before clinical deployment.

UCSF Automates CAC Scoring

Posted on by Jake Fishman

UCSF is now using AI to automatically screen all of its routine non-contrast chest CTs for elevated coronary artery calcium scores (CAC scores), representing a major milestone for an AI use case that was previously limited to academic studies and future business strategies.

UCSF’s Deployment – UCSF becomes the first medical center to deploy the end-to-end AI CAC scoring system that it developed with Stanford and Bunkerhill Health earlier this year. The new system automatically identifies elevated CAC scores in non-gated / non-contrast chest CTs, creating an “opportunistic screening pathway” that allows UCSF physicians to identify high-CAC patients and get them into treatment.

Why This is a Big Deal – Over 20m chest CTs are performed in the U.S. annually and each of those scans contains insights into patients’ cardiac health. However, an AI model like this would be required to extract cardiac data from the majority of CT scans (CAC isn’t visible to humans in non-gated CTs) and efficiently interpret them (there’s far too many images). This AI system’s path from academic research to clinical deployment seems like a big deal too.

The Commercial Impact – Most health systems don’t have the AI firepower of Stanford and UCSF, but they certainly produce plenty of chest CTs and should want to identify more high-risk patients while treatable (especially if they’re also risk holders). Meanwhile, there’s growing commercial efforts from companies like Cleerly and Nanox.AI to create opportunistic CAC screening pathways for all these health systems that can’t develop their own CAC AI workflows (or prefer not to).

The False Hope of Explainable AI

Posted on by Jake Fishman

Many folks view explainability as a crucial next step for AI, but a new Lancet paper from a team of AI heavyweights argues that explainability might do more harm than good in the short-term, and AI stakeholders would be better off increasing their focus on validation.

The Old Theory – For as long as we’ve been covering AI, really smart and well-intentioned people have warned about the “black-box” nature of AI decision making and forecasted that explainable AI will lead to more trust, less bias, and greater adoption.

The New Theory – These black-box concerns and explainable AI forecasts might be logical, but they aren’t currently realistic, especially for patient-level decision support. Here’s why:

  • Explainability methods describe how AI systems work, not how decisions are made
  • AI explanations can be unreliable and/or superficial
  • Most medical AI decisions are too complex to explain in an understandable way
  • Humans over-trust computers, so explanations can hurt their ability to catch AI mistakes
  • AI explainability methods (e.g heat maps) require human interpretation, risking confirmation bias
  • Explainable AI adds more potential error sources (AI tool + AI explanation + human interpretation)
  • Although we still can’t fully explain how acetaminophen works, we don’t question whether it works, because we’ve tested it extensively

The Explainability Alternative – Until suitable explainability methods emerge, the authors call for “rigorous internal and external validation of AI models” to make sure AI tools are consistently making the right recommendations. They also advised clinicians to remain cautious when referencing AI explanations and warned that policymakers should resist making explainability a requirement. 

Explability’s Short-Term Role – Explainability definitely still has a role in AI safety, as it’s “incredibly useful” for model troubleshooting and systems audits, which can improve model performance and identify failure modes or biases.

The Takeaway – It appears we might not be close enough to explainable AI to make it a part of short-term AI strategies, policies, or procedures. That might be hard to accept for the many people who view the need for AI explainability as undebatable, and it makes AI validation and testing more important than ever.

ImageBiopsy Lab & UCB’s AI Alliance

Posted on by Jake Fishman

Global pharmaceutical company UCB recently licensed its osteoporosis AI technology to MSK AI startup ImageBiopsy Lab, representing an interesting milestone for several emerging AI business models.

The UCB & ImageBiopsy Lab Alliance – ImageBiopsy Lab will use UCB’s BoneBot AI technology to develop and commercialize a tool that screens CT scans for “silent” spinal fractures to identify patients who should be receiving osteoporosis treatments. The new tool will launch by 2023 as part of ImageBiopsy Lab’s ZOO MSK platform.

UCB’s AI Angle – UCB produces an osteoporosis drug that would be prescribed far more often if detection rates improve (over 2/3 of vertebral fractures are currently undiagnosed). That’s why UCB developed and launched BoneBot AI in 2019 and it’s why the pharma giant is now working with ImageBiopsy Lab to bring it into clinical use.

The PharmaAI Trend – We’re seeing a growing trend of drug and device companies working with AI developers to help increase treatment demand. The list is getting pretty long, including quite a few PharmaAI alliances targeting lung cancer treatment (Aidence & AstraZeneca, Qure.ai & AstraZeneca, Huma & Bayer, Optellum & J&J) and a diverse set of AI alliances with medical device companies (Imbio & Olympus for emphysema, Aidoc & Inari for PE, Viz.ai & Medtronic for stroke).

The Population Health AI Trend – ImageBiopsy Lab’s BoneBot AI licensing is also a sign of AI’s growing momentum in population health, following increased interest from academia and major commercial efforts from Cleerly (cardiac screening) and Zebra Medical Vision (cardiac and osteoporosis screening… so far). This alliance also introduces a new type of population health AI beneficiary (pharma companies), in addition to risk holders and patients.

The Takeaway – ImageBiopsy Lab and UCB’s new alliance didn’t get a lot of media attention last week, but it tells an interesting story about how AI business models are evolving beyond triage, and how those changes are bringing some of healthcare’s biggest names into the imaging AI arena.

Who Owns AI Evaluation and Monitoring?

Posted on by Jake Fishman

Imaging AI evaluation and monitoring just became even hotter topics, following a particularly revealing Twitter thread and a pair of interesting new papers.

Rads Don’t Work for AI – A Mayo Clinic Florida neuroradiologist took his case to Twitter after an FDA-approved AI tool missed 6 of 7 hemorrhages in a single shift and he was asked to make extra clicks to help improve the algorithm. No AI tool is perfect, but many folks commenting on this thread didn’t take kindly to the idea of being asked to do pro-bono work to improve an algorithm that they already paid for. 

AI Takes Work – A few radiologists with strong AI backgrounds clarified that this “extra work” is intended to inform the FDA about postmarket performance, while monitoring healthcare tools and providing feedback is indeed physicians’ job. They also argued that radiology practices should ensure that they have the bandwidth to monitor AI before deciding to adopt it.

The ACR DSI Gets It – Understanding that “AI algorithms may not work as expected when used beyond the institutions in which they were trained, and model performance may degrade over time” the ACR Data Science Institute (DSI) released a helpful paper detailing how radiologists can evaluate AI before and during clinical use. In an unplanned nod to the above Twitter thread, the DSA paper also noted that AI evaluation/monitoring is “ultimately up to the end users” although many “practices will not be able to do this on their own.” The good news is the ACR DSI is developing tools to help them.

DLIR Needs Evaluation Too – Because measuring whether DL-reconstructed scans “look good” or allow reduced dosage exams won’t avoid errors (e.g. false tumors or removed tumors), a Washington University in St. Louis-led team is developing a framework for evaluating DLIR tools before they are introduced into clinical practice. The new framework comes from some big-name intuitions (WUSTL, NIH, FDA, Cleveland Clinic, UBC), all of whom also appear to agree that AI evaluation is up to the users.

The Takeaway – At least among AI insiders it’s clear that AI users are responsible for algorithm evaluation and monitoring, even if bandwidth is limited and many evaluation/monitoring tools are still being developed. Meanwhile, many AI users (who are crucial for AI to become mainstream) want their FDA-approved algorithms to perform correctly and aren’t eager to do extra work to help improve them. That’s a pretty solid conflict, but it’s also a silver lining for AI vendors who get good at streamlining evaluations and develop low-labor ways to monitor performance.

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