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To give a counter point of view here, a close relative of mine is a pathologists. We talked a lot about this subject and so far he is unimpressed by the result

To give a counter point of view here, a close relative of mine is a pathologists. We talked a lot about this subject and so far he is unimpressed by the results. Moreover, it seems that the ability to provide fast (in seconds to minutes) analysis implies powerful computational ability and data network. Finally, the data acquisition right now takes more or less 20 to 45 minutes depending on the patient and the targeted area, which means the promised analysis quickness is not such a real pro. I trust that science, research and ongoing work will end up providing interesting results, but many many startups will burn their cash before being able to provide real world usage services. Like you said, there are LOTs of challenges here. Certainly not a "low hanging fruit", not that a profitable business either since most countries will squash costs anywhere they can because the health cost keeps growing, and a very difficult legal environment to deal with. However, I am very thankful for your hard work and will to push toward a better future for health.

maxerickson 8y ago
The "years for a human" implies a different context than rapid individual results.
taneq 8y ago
I thought the primary importance of machine vision for this kind of cancer screening was that it doesn't give so many false negatives due to fatigue? IIRC there was a study that went back over cancer patients' screening tests and found that most of them had failed to identify visible, diagnosable tumors well before their disease was actually identified.
- raducu 8y ago
  A very important use case is that a lot of places don't have easy access to good diagnosticians, and software + hardware are easy to scale while humans, not so much.
ihnorton 8y ago
> Moreover, it seems that the ability to provide fast (in seconds to minutes) analysis implies powerful computational ability and data network. Finally, the data acquisition right now takes more or less 20 to 45 minutes depending on the patient and the targeted area, which means the promised analysis quickness is not such a real pro. In surgical pathology, the patient is still under surgery while a pathologist makes a rapid, preliminary diagnosis on fresh tissue. This can be done in under 10 minutes, but typically takes 20-30 in practice -- the bulk of which time is spent flash-freezing and sectioning. Pathologists have only a few minutes to review after prep, so any digital augmentation technology must run on the order of a few minutes to be of use in guiding the surgery. This guidance may be crucial because many tumors cannot be characterized ahead of time due to the lack of non-invasive diagnostic tools (the most promising is gene profiling of bloodborne cells, but that is very challenging due to low circulating concentrations). Tumor type and grade are a large factor in a surgeon’s aggressiveness-vs-risk calculation, and this is especially true in brain tumors where cognitive damage poses a quality-of-life risk which must be weighed against potential survival gains. (not a pathologist, but I've done research, classifier, and software dev in the field -- very small, hands-on lab, so spent a huge amount of time in frozen section, OR, and with paths reviewing sections)

maxerickson 8y ago

The "years for a human" implies a different context than rapid individual results.

taneq 8y ago

I thought the primary importance of machine vision for this kind of cancer screening was that it doesn't give so many false negatives due to fatigue? IIRC there was a study that went back over cancer patients' screening tests and found that most of them had failed to identify visible, diagnosable tumors well before their disease was actually identified.

raducu 8y ago
A very important use case is that a lot of places don't have easy access to good diagnosticians, and software + hardware are easy to scale while humans, not so much.

ihnorton 8y ago

> Moreover, it seems that the ability to provide fast (in seconds to minutes) analysis implies powerful computational ability and data network. Finally, the data acquisition right now takes more or less 20 to 45 minutes depending on the patient and the targeted area, which means the promised analysis quickness is not such a real pro. In surgical pathology, the patient is still under surgery while a pathologist makes a rapid, preliminary diagnosis on fresh tissue. This can be done in under 10 minutes, but typically takes 20-30 in practice -- the bulk of which time is spent flash-freezing and sectioning. Pathologists have only a few minutes to review after prep, so any digital augmentation technology must run on the order of a few minutes to be of use in guiding the surgery. This guidance may be crucial because many tumors cannot be characterized ahead of time due to the lack of non-invasive diagnostic tools (the most promising is gene profiling of bloodborne cells, but that is very challenging due to low circulating concentrations). Tumor type and grade are a large factor in a surgeon’s aggressiveness-vs-risk calculation, and this is especially true in brain tumors where cognitive damage poses a quality-of-life risk which must be weighed against potential survival gains. (not a pathologist, but I've done research, classifier, and software dev in the field -- very small, hands-on lab, so spent a huge amount of time in frozen section, OR, and with paths reviewing sections)