Hello, my name is Devesh Sarda, and I'm a undergraduate at the Ubicomp
Lab in the University of Washington. In this presentation, I would like
to talk about our work on monitoring rapid diagnostic test
administration.
So what are rapid diagnostic tests? Rapid diagnostic tests, or RDTs, are
medical tests are quick and easy to administer with a limited amount of
resources. Thus, they're especially suited for initial medical
screening, as well as point of care medical testing. The problem is that
people are not experienced with such tests often make mistakes in the
administration, which leads to inaccurate results. Our solution to
this problem is RDTCheck.
RDTCheck is a smartphone application that guides users through the
instructions of a RDT. We ensure that the users follow the
protocols of the test using a variety of computer vision
techniques, which we describe in detail in the next slide. The
application provides users with real time feedback to correct their
mistakes and prevent those mistakes from happening in the future.
RDTCheck application guides the users to the steps of the QuickVue
Influenza A+B test, which is a test that utilizes a swab for sample
collection. This test contains many steps that are similar to other
tests that utilize a swab for sample collection, which means that a lot
of the software can be generalized to other swab based RDTs. This
application has seven major steps with the first two being placing the
test tube inside the box and adding a buffer solution to the test tube.
The application utilizes relatively similar computer vision techniques
to ensure that the user has completed these steps properly.
What the application does is that it takes the first incoming image and
treats it as the base image. All later frames are then compared
against this base image using image alignment and image subtraction to
get a difference between the box in the current frame and in the base
image. The application then utilizes noise reduction along with contour
detection to ensure that the test tube has been placed inside the box
and that enough buffer solution has been added to the test tube.
The next three steps all involve the interaction of the swab with the
buffer solution. The application uses a CSRT-based region of interest
tracker to track the swab inside the test tube. To initialize the tracker
we'd need to not only provide it frame, but also an initial bounding
box. We find the initial bounding box using color thresholding.
For the initial frame, we search for white in the initial frame after
converting it to the HSV color space.
The final two steps involve placing the strip inside the test tube. We
use multi-scale template matching to ensure that the strip has been
placed inside the test tube and in the proper orientation. We utilize
a template image of the downward arrows which not only helps ensure
that the strip has been placed but also it has been placed in the
proper orientation.
In our initial study, we asked participants to deliberately make
certain mistakes and record if the application could detect those
mistakes or not. Specifically, we asked users to make mistakes that
are common in swab based RDT Such as not adding enough buffer
Solution or not swirling the swab or a long enough period of time. We
recruited a total seven articipants, four male and three
female whose ages range from 25 to 42. All of the participants had no
prior experience with RDT of any sort, swab based or otherwise.
We were able to measure an average error detection rate of 91.1% per
step. As you can see from the table on the right, we placed a special
emphasis on this swirling the swab in the buffer solution case. This is
because one of the most common errors in swab based RDT is
inproper swirling of the swab with the buffer solution. Thus, we wanted
to ensure that the RDTCheck application could detect that error
with a high enough success rate and provide users feedback on how to
correct that mistake.
Through our initial study we were able to show that RDTCheck can
accurately detect mistakes in RDT administration. The next step
is to conduct a usability study on RDTCheck. For usability study, you
want to have a larger number of participants than we did for our
initial study. We also want to have participants with different
experience levels in RDT administration. This is because we
want to see what role experience level has in the ability to have the
RDTCheck application to detect and correct the user's mistakes. After
we have incorporated the feedback we received from the usability study
into the application, the next thing we want to do is deploy the RDTCheck
application into the field. We want to see how well the RDTCheck
application does in a non-controlled environment with the general public.
You may want to measure how accurately it can detect and fix
users mistakes in that setting.