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12 Responses

  1. Jadey
    Jadey July 29, 2012 at 9:07 pm |

    Very, very cool and good for her. Does it say anywhere what her rate of false positives was? I didn’t quite understand that aspect of the reported test data.

  2. Angie unduplicated
    Angie unduplicated July 29, 2012 at 9:41 pm |

    This is red hot: teenage app developer pwns cytotechs. LMAO. Brittany, science Olympics should exist, and don’t. Cheering 4U.

  3. Shauna
    Shauna July 29, 2012 at 10:51 pm |

    Jadey: if you go to the google site, there’s a slideshow. Slide #17 gives the following breakdown:

    Actual Outcomes
    Positive | Negative
    Positive 222 15
    Inconclusive 14 11
    Negative 2 417

    Which is apparently a 93.67% positive predictive rate and 99.52% negative predictive rate. (Presumably that means 6.33% false positives and .48% false negatives.) Which makes sense – the slide show stresses that the model was “weighted towards malignancy” – that is, to err on the side of saying it’s malignant when it wasn’t.

  4. LC
    LC July 29, 2012 at 11:22 pm |

    Yeah, I would expect a serious false positive rate. At the same time, as long as it is pitched as a first pass to be confirmed, that’s still exceedingly useful. It’s great stuff.

  5. Jadey
    Jadey July 29, 2012 at 11:24 pm |

    @ Shauna

    Ah, thanks – I didn’t realize that the slideshow contained so much more detailed information. The specificity rate (96.53%) was exactly what I was looking for, and it’s equally impressive to the sensitivity rate! I actually wish more sources were reporting both – a sensitive but non-specific test is about as useless as an insensitive test.

    Yes, it makes more sense to gear a testing model like this more toward reducing false negatives than false positives, given the relative consequence of failing to get treatment for a malignant tumour versus getting unnecessary treatment for a benign one, but I’m very happy to see that she achieved such excellent results on both scores!

  6. LC
    LC July 29, 2012 at 11:26 pm |

    As with all positive predictive values, it will depend on the population it is used in.

  7. pkle
    pkle July 30, 2012 at 12:31 am |

    LC –
    With a pattern-recognition software like this one, you could conceivably train it on multiple populations, using only data from those relevant to a given patient. Definitely very dependent on being given enough data from each group, though.

  8. samanthab
    samanthab July 30, 2012 at 7:04 am |

    I’d love to here about the background here. Who gave her the educational tools here? I’m not diminishing what she’s done, by any means, but it’s clear she’s been getting incredible support somewhere- she obviously didn’t pull data out of her ass-, and I’d love to hear that story, too.

  9. LC
    LC July 30, 2012 at 9:17 am |

    pkle – absolutely. That’s part of what makes this so interesting, it should be fairly tunable. From what has been described, though, it strikes me that it is going to swing to being a better screen to eliminate negatives no matter the case.

    And I’m with samanthab, I’d like to see where she got all the support from.

  10. LC
    LC July 30, 2012 at 5:02 pm |

    Which is not to in any way diminish her accomplishments, but rather to note how much can be accomplished with the benefit of support and attention.

    Quoted for truth, (and I suspect what both samanthab and I were getting at).

  11. pathologist
    pathologist August 7, 2012 at 7:33 pm |

    Not to rain on anyone’s parade, but the usefulness of this software is pretty dubious. I’m a pathologist who looks at fine needle aspirates…the program that she wrote requires that the slides be evaluated by a person and then the criteria be entered into the program by hand. The criteria are well-known features of malignancy (nothing new here). Now, if it was based on image analysis with slide scanning by a computer, it would be useful. This is NOT hospital ready.

    Not that I could write a program like that now, or when I was 17…

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