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TB AI’s Moment | X-Ray IDs | COVID Perfection

“The future doesn’t happen all at once…”

Koios Medical CEO, Chad McClennan, in response to the WHO’s big tuberculosis AI recommendation.



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The WHO’s Big TB Recommendation

The World Health Organization just recommended using computer-aided detection AI software to identify and triage tuberculosis in chest X-rays — as an alternative to human interpretations.

  • Recommendation & Rollout – The WHO’s updated TB screening guidelines recommends using CAD tools for screening/triaging TB in individuals who meet certain criteria (e.g. ≥15 years, regional prevalence, exposure history, comorbidities, etc.). The WHO also released a suite of digital tools to help countries launch and operate their CAD-based TB screening programs.
  • TB AI’s High Ceiling – The WHO’s new TB screening guidelines should significantly expand the use of TB CAD solutions, given that about a quarter of the world’s population is infected with TB and 10m become ill/contagious each year. That could mean a whole lot more X-rays and automated TB diagnoses, and a lot of opportunities for the companies who make TB CAD solutions (e.g. Qure.ai, Lunit).
  • A Long Time Coming – Chest X-rays have played a key role in TB screening and triage for decades, but there’s never been enough human clinicians to support CXR screening programs. In more recent years TB AI developers and TB health organizations have actively (and successfully) worked to drive real-world TB CAD adoption and validation, leading to the WHO’s new recommendation.
  • An AI Milestone – The WHO’s new guidelines could also have ramifications beyond tuberculosis, as it’s the first major endorsement for AI-only image interpretation and it could serve as a blueprint for other screening efforts in low-resource regions. Whether it leads to AI-only discussions in developed regions is unclear, but that’s worth keeping an eye on too.

The Wire

  • Ultrasound Mindreader: A Caltech team is using functional ultrasound to develop noninvasive brain–machine interfaces (BMIs) that can interpret neural activity and then transmit instructions to machines or computers (e.g. allow a paralyzed person to move a robotic arm). The Caltech scientists view their functional ultrasound-based BMI as a potential breakthrough, since BMI currently relies on surgically implanted electrodes or less-effective external modalities like fMRI (too bulky, expensive) and electroencephalography (low spatial resolution).
  • COVID Ultrasound AI Perfection: The Lawson Health Research Institute developed an ultrasound AI model (trained w/ 243 patients, 612 recordings, 121,400 frames) that identified COVID pneumonia with “perfect” accuracy. The model achieved this “perfect” score using ultrasound scans from 25 patients with COVID and other lung diseases, outperforming 61 ultrasound-trained acute care physicians who identified the same COVID scans with accuracy that was “no better than a coin toss.”
  • Hold on to Contrast Monitoring: A new JACR opinion paper argued that radiologists should think twice before making technologists responsible for monitoring contrast administration for severe reactions (as proposed in a separate JACR editorial). The paper encouraged radiologists to keep this responsibility because: 1) The number of severe reactions is “far from trivial;” 2) Radiologists have unique medical knowledge that would be difficult to transfer to RTs; 3) This change would undermine rads’ goal to increase patient interaction; 4) It could cut reimbursements.
  • Identifying X-Rays: All those anonymized chest X-ray datasets might not be as anonymous as we think. A team of German researchers used a public CXR dataset and an X-ray classification system to develop a deep learning-based reidentification model that was able to identify two images from the same person with 95.55% accuracy. In theory, hackers with access to unanonymized X-rays could use reidentification models like this to gain access to patient healthcare data that’s included in anonymized images (diagnoses, histories, site of care, etc.).
  • Automated Reminders Work: Automated imaging appointment reminder systems are just as effective for avoiding no-shows as conventional phone/mail reminders performed by imaging staff. That’s from a Michigan Medicine study that compared CT and MRI imaging appointments during the months before (using calls + written letters) and after adopting an automated appointment reminder system (using automated texts + calls + patient portal notifications). Patients reminded with traditional communication methods had a 2.82% “missed care opportunity” rate (292/10,348), while patients reminded with the new automated communication methods missed 2.44% of their appointments (262/10,719) one month later.
  • GE’s New C-Arm: GE Healthcare announced the FDA clearance of its new OEC 3D mobile C-arm surgical system, highlighting the flexibility of its combined 3D and 2D imaging, and the CT-level quality of its 3D volumetric images.
  • No Explanation Necessary: There’s been many calls for greater AI explainability, but a recent JACR editorial argues “that explainability might not be sufficient to promote trust” and it “might not even be necessary.” That’s because: 1) Humans can’t even adequately explain their own decisions; 2) AI explainability might come at the expense of accuracy; 3) Trustable AI goes far beyond the model (also data, workflow, designers, ecosystem, etc.); and 4) We still don’t have “continuous learning” models that could explain decisions with historical context. Until we have explainable AI sorted out, the authors suggested that we should foster AI trust through educating rads on AI’s limitations, enforcing AI best practices, and educating patients about AI.
  • Lucida Funded: UK imaging AI startup Lucida Medical completed a multimillion-pound Seed round that it will use to develop and validate its AI solutions that analyze MRI scans for signs of cancer, initially focusing on a prostate cancer solution. Lucida’s MRI analysis software combines radiogenomics, machine learning, and image processing.
  • ScanVan’s Screening Success: A new Clinical Imaging study detailed the New York-based ScanVan mobile mammography screening program’s success, suggesting that no-cost exams and mobile access are key to improving screening among underserved women. In 2019, the ScanVan screened 3,745 women (66% Hispanic & African American, 43% uninsured, 15% Medicare), identifying 17 new breast cancers and resulting in 258 recalls.
  • PMV Acquires Mindshare: Precision Medical Ventures (PMV) acquired lung CT AI company, Mindshare Medical, revealing plans to accelerate the commercialization of Mindshare’s RevealAI-Lung CADx lung nodule characterization software. PMV will do business under the Mindshare Medical name, while bringing in new well-connected leaders, new funding, and likely new strategies.
  • AI Fear & Understanding: The more radiologists understand AI, the less they’re scared of being replaced by it, although plenty of rads are still concerned. That’s from a survey in European Radiology (n = 1,041 radiologists/residents in 2019) that found that 48% view AI positively and 38% fear replacement by AI, while optimistic and pessimistic responses were directly correlated with the respondents’ knowledge of AI. The authors (who appear to be among the AI optimists) called for expanded AI education.
  • Two Sides to Pediatric Brain MRI Incidentals: Pediatric brain MRI scans result in far more incidental findings that many might think, but very few of clinical significance. That’s from a new JAMA study that reviewed brain MRIs from 11,679 9-10yrs-old children and discovered incidentals in 2,464 (21.1%) of them. However, only 431 of the incidentals were assigned category 3 (3.7%, “consider referral”) and 20 were assigned category 4 (0.2%, “consider immediate referral”).

The Resource Wire

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