IEEE Robotics & Automation Magazine - June 2017 - 80

Novice
Total Time
Average Time
Standard Deviation
Expert
Total Time
Average Time
Standard Deviation

Endoscope
5:42
0:19
0:13

HydroJet
8:24
0:28
0:25

2:54
0:10
0:06

13:18
0:44
1:11

Figure 10. The results of comparative trials between the
HydroJet and a standard flexible endoscope. Total time refers
to the cumulative time to complete all three trials with a given
endoscopic device, while average time and standard deviation
refer to the time needed to identify a single point. Time data
given in minutes:seconds format.

a randomized order to prevent memory bias from affecting
the results. The results of this trial are reported in Figure 10
for both the expert and novice users.
With novice users, the HydroJet took approximately 50%
longer than the flexible endoscope to complete a procedure.
With the expert user, the difference between the HydroJet and
flexible endoscope was much larger due to the user's expertise
in using traditional endoscopes. Although the HydroJet takes
longer than the flexible endoscope to complete a screening
procedure, it still can provide screening care in a reasonable
amount of time and shows potential for improvement with
operator training.
It is worth comparing the optical capabilities of the HydroJet to that of the flexible endoscope to better understand the
results. The endoscope used for comparison has a 140° field
of view and a focal distance of 2-100 mm. In contrast, the
camera used in the HydroJet has a 54° field of view and a focal
distance of 10-50 mm. The discrepancy in the quality of camera used in each device is expected to give the endoscope a
baseline advantage, regardless of capsule controllability. These
results can be considered to be a conservative estimate of the
capabilities of the HydroJet. Of course, using a camera with a
wider field of view would definitely reduce the time required
to complete a procedure.
Conclusions and Future Work
The HydroJet endoscopic platform addresses the need for a
low-cost, portable system for UGI cancer screening in LMICs.
In this study, a novel water distribution system is introduced,
which addresses many of the deficiencies of the previous
design. Open-loop and throttle control of the actuating jets is
examined and show good controllability of the reaction thrust.
The range of stable positions the capsule can reach was further
80

IEEE ROBOTICS & AUTOMATION MAGAZINE

June 2017

examined, and a total number of 2,197 total positions was
found for the three jets. This number is invariant on the tether
length, depending only on the resolution of the pinch valve,
which allows full controllability and stable spatial resolution
with differing tether lengths. Finally, comparative trials were
conducted to evaluate the medical practicality of the platform.
Future work includes the implementation of closed-loop
control of jet actuation force, which can reduce the training
required to operate the platform. This type of control could
enable semiautonomous operation, wherein the platform can
help the user control movement of the capsule to visualize
regions of interest. Even if retroflexion is feasible, the increased time required and the need to learn a new maneuver
to reach adequate performance are limitations of the current
platform that will be addressed in future work. Further demonstration of the capsule mobility both ex vivo and in vivo is
needed to better assess clinical efficacy. Additional in vivo
trials to assess the medical accuracy of the platform are
planned, with the goal of a comparative assessment between
the HydroJet platform and traditional endoscopy. With the
success of medical trials, the HydroJet platform can address a
deficiency in point-of-care medicine for the LMIC setting.
More broadly, the HydroJet can enable the widespread
implementation of UGI cancer screening programs, reducing the rate of incident cancers and global cancer mortality.
Acknowledgments
Research reported in this article was supported by the National
Institute of Biomedical Imaging and Bioengineering of the
National Institutes of Health (NIH) under award number
R01EB018992, by the National Science Foundation (NSF)
under grant number IIS-1453129 and CNS-1239355, by the
Royal Society under grant number CH160052, by the Engineering and Physical Sciences Research Council (EPSRC), by
the European Research Council (ERC) under grant agreement
number 638428, and by the Vanderbilt Institute for Surgery
and Engineering (VISE). Any opinions, findings and conclusions, or recommendations expressed in this article are those of
the authors and do not necessarily reflect the views of the NIH,
the NSF, the Royal Society, the EPSRC, the ERC, or VISE.
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Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2017