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2021-12-6.md

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2021-12-6

Agenda Items:

  • RSNA

    • Mohannad is a rockstar!
    • HyperFine is cool
      • Runs on 110v
      • .64 T
      • Super Small
    • RSNA AI/ML Stuff
    • Mohannad -
      • Startup "Within health"
        • Neat followup solution
        • What stood out - how they figured out the end to end problem
          • communicating with patient, nagging them
          • Create roadmap of actions "how to get people to do followup"
          • Woojin Kim is advisor
    • Attendance? - 40% of normal - 20,000
    • Vendors said booth traffic was not effected much, interactions were good
    • Fun game: https://bsbingo.techiemaestro.com/
    • https://twitter.com/shadowdoc/status/1466440302626058245?s=21
  • How to embed AI results in reporting (David Kwan)

    • Problem: AI in the real world doesn't work (designed around a specific cohort)
    • Need metadata for AI results in standards
      • Localation of scan?
      • Vendor
      • Product
      • Version
    • Mohannad - yes have thought about it somewhat, but nothing in the works yet?
      • Many vendors were so new to AI/ML that they weren't familiar with the standards
      • Demo does not force any vendor to do any particular thing
      • Federated learning was featuree in the demo (nvidia/microsoft)
      • If you are interested in joing the AI demo for RSNA 2022, talk to Mohannad Hussain
    • Difficult to retrain a model and have any guarantees on performance
      • e.g. tune on local cohort - cannot guarantee it has same performance if expanded to include other images
    • Q: Is this question for medical devices (FDA) or from medical report perspective?
      • David Kwan: Both
        • Medical Devices - Point of Care (POC) US - used in ER or in surgery.
          • problem with POC US, does not fit usualy diagnostic imaging workflow
          • PACS is order based workflow
          • IHE has a profile for encounter based workflow
          • In federated learning - need to know acquisition device details (version, vendor, product, etc)
        • Reporting
          • if physicial takes an AI result - the metadata must be present (vendor, product, version)
            • Needed in case of possible mal practice suite
          • Most radiologists are having a hard time retreiving the metadata and inserting it into the report
            • Radiology report is today still just text (HL7 OBX segment)
      • Related issue - acquisition device Q/C *phantom scanning)
    • Q: Should reports be stored as JSON in a standard form with metadata not rendered to the rad?
      • DICOM has a JSON mapping
      • DICOM also has structured report IOD
        • Plain text / OBX reporting is the norm in radiology, but really needs an overhaul
    • Liability - who is responsible if a radiologist accepts an AI result and its wrong?
    • Lung nodules - very time consuming to count/measure/report - AI can help a lot
  • Controversial subject - how important are standards in the era of web and cloud computing?

  • JPEG-XL DICOM WorkItem Approved

    • NOTE - it is NOT added to the standard yet, just approved to propose it to the standard
    • Main use case for JPEG-XL is to replace JPEG baseline for lossy color images
    • Better compression, better color accuracy, faster
  • Q: How often is lossy compression used for diagnosis?

    • BIG CAN OF WORMS
    • It can be, but not allowed in some places
    • used when bandwidth is limited (e.g. teleradiology, disadvantaged countries - especially less litigious)
    • There are established ways to use lossy for interpretation
    • Many radiologists refuse to read lossy images