Predicting Infection Using Infrared Thermography in Premature Infants

Abstract

Predicting
when a neonate will fall victim to an infection or a disease allows prevention
through early medicine administering. Such physiological conditions can be made
visible using infrared thermography (IRT). This is a technique for measuring
heat emitted in the infrared spectrum and transforming them into visible
signals that can be recorded photographically. This thesis will contribute to
the prediction of infection in (pre)term neonates by quantifying the
interaction between IRT and a neonatal incubator without (and with) a neonate
in it.  A system was designed that
consisted of three modules: a measurement (incubator and IRT camera), back-end
(embedded system and server), and front-end module. The scope of this thesis is
limited to the measurement module and the embedded system of the back-end
module. Minimum camera requirements were set up which required the camera to:
be inexpensive (i.e. ≤ €1000,-), be mobile, be open-source (for Linux), have a
minimum frames-per-second of 5, have a resolution of at least 160x160 pixels
with a field of view (FOV) of 27°, sensitivity of < 0.1°C, and safe to the
patient. Such a camera was found in the FLIR One Pro. For this thesis a
different FLIR camera was used due to lack of budget, namely the FLIR A305sc,
which was already available at the TU Delft. The A305sc is not open-source,
which required a work-around. The Aravis Open Source Project allowed for
communication with the camera. Internal camera parameters had to be determined
to calculate temperature based on analogue-to-digital values. ExifTool was used
on a file stored by the camera to extract these parameters. This calculated
temperature was compared to the temperature as determined by FLIR’s software
and led to a difference in the range of 1·106°C. An open-source application was
written that can connect with this IRT camera that has a GenICam interface
using Aravis. Additionally, this application implemented the temperature
calculation based on the internal camera parameters. The hood of the incubator
is opaque to infrared, which required the design of a measurement setup to
circumvent this. Three different setups were discussed, with the final choice
falling on placing the camera in front of an opened incubator porthole on a
tripod, and sealing this porthole with high or low density ethyl polyethylene
(HDPE/LDPE). Regular H/LDPE used for construction site was found to have a
attenuated transmissivity as found in literature. To quantify the interaction
between IRT and a neonatal incubator, the IRT measurements were to be compared
against the current golden standard sensor, namely thermistors. These sensor
values were to be read out from the incubator as this would also be used in the
final product. Code was written which allows for automatic detection between
the GE GiraffeTM Omnibed and the Dräger Caleo® incubator, automatic connecting,
and manipulation of all sensors values to a standard string which allows for
easy uploading to the InfluxDB database on the server. To be allowed to perform
measurements on human subjects, approval had to be acquired by the human
research ethics committee (HREC) of the TU Delft and the respective hospital. A
“non-wet medisch wetenschappelijk onderzoek met mensen” (nWMO) request was
submitted and approved, which resulted in 25 recorded sick and healthy neonates
in incubators divided over two hospitals (the JKZ in The Hague, and the RDGG in
Delft), with over 25 hours of recording material. Simultaneously, measurements
were performed on an empty incubator to gain an understanding in the behaviour
of an incubator when actors from outside interacted with the internal
environment. Measurements that were performed included determining the
reflected apparent temperature (RAT) for every possible opened porthole and for
both incubator types. The RAT for the Caleo was found to be higher for every
measurement for the GE. The accuracy of the IRT and hospital skin temperature
sensors was compared against a calibrated Pt-100 sensor, which show that the
Pt-100 sensor measures an equal value as the hospital skin temperature sensor,
whereas The IRT camera measured .6°C higher. The effect of changing the
distance on IRT values was measured, which shows that for a distance of 0.2m to
1.2m the accuracy of the IRT camera is within the specified accuracy. Finally,
the effect of opening additional portholes on IRT was measured, the effect of
the airboost setting on IRT, and the measurement of opening additional
portholes was repeated with a different IRT camera. Overall the IRT camera measures
a higher temperature than the hospital skin temperature sensors, but follows
the skin temperature sensors’ pattern.