Another exciting project on the topic of noninvasive medical technology, thermal modeling and optimization. This research topics addresses the possibility of detecting a small, asymptomatic, intraocular tumor using infrared imaging technology. The algorithm uses concepts of heat transfer and pattern search algorithm to analyze the eye surface temperature distribution and to determine the existence of a tumor inside the eye. Although results of the algorithm have shown a promising potential to detect early stage tumors, they need to be validated clinically.

1. New Technology to Detect
Eye Tumors
Senay Tewolde, Ming Chyu, and Kevin Long
linkedin.com/in/senaytewolde

2. Texas Tech University researchers have
developed an algorithm that uses eye
surface temperature to detect
early stage, asymptomatic, intraocular
tumors.

3. Clinical Facts

4. Most Intraocular tumors originate
in the choroid layer.
Small Large

5. Ocular melanoma and
retinoblastoma are the two
primary tumors known to grow
inside the human eye.

6. Ocular melanoma is the most
aggressive type of eye cancer and
can be deadly, if not detected early.
Ocular melanoma affects 2,500 adults
annually, and the incidence rate
increases significantly for people age
50 and older.

7. Eye removal remains a frequent
treatment for ocular melanoma.
Retinoblastoma, although a very
rare disease, is the third most
common type of cancer in children
under the age of five.

8. How the Algorithm
Works

9. The tumor detection algorithm
is based on four computational
frameworks.

10. Record eye surface
temperature using
an IR camera.
Measure Eye Surface Temperature

11. Image processing and mapping
Localization of the ocular
surface area from the
thermal image.
Map the image onto the
ocular boundary surface
of the physical model.
R aw image Localized
Physical
mod el

12. Inverse Analysis
• Read the OST from the meshed
eye model
• Define the integration domains
• Create the discrete functions
• Set the weak form equation
• Specify the solve by creating a
linear problem
• Check the goodness of fit test
Define eye regions
Expression for weak form
Expression for BCs*
Expression for test function
Expression for unknown
function
Expression for solution
estimate
Sundance

13. A pattern search
algorithm based on
asynchronous
Generating Search
Set (GSS) method
Optimization
• Generate a set of trial points
• Evaluate the trial points via
HOPSPACK asynchronous GSS
• Checks if there exists a point
that satisfies the target value
• True – replace with the new best
point
• False – reduce the step length as
appropriate
• The process continues …
HOPSPACK SUNDANCE
The process
continues until the
minimum objective
target value is
satisfied.
Given
• Main Code
(Sundance)
• Input file name
• Output file name
• Tag #

14. Optimization
• Generate a set of trial points
• Evaluate the trial points via
HOPSPACK asynchronous GSS
• Checks if there exists a point
that satisfies the target value
• True – replace with the new best
point
• False – reduce the step length as
appropriate
• The process continues …
HOPSPACK SUNDANCE
The process
continues until the
minimum objective
target value is
satisfied.

15. Results of the Detection
Accuracy

16. Results of the diagnostic tests
reveal that the detection accuracy
of a suspected intraocular tumor
depends on the location and
intensity of the tumor.

17. For a very small size tumor (3 mm
basal diameter), with hypothesized
heat generation rate of 56.0-59.0
Cal/day, the detection accuracy of
the algorithm varied from 70.0%
to 97.0%.

18. Clinical Contributions

19. The first algorithm to use ocular
surface temperature to detect a
small intraocular tumor,
demonstrating its potential
application for an early stage
detection.

20. The first algorithm to quantify
the intensity of a tumor, which
could in turn be used to
determine whether the tumor is
benign or malignant.

21. Rigorous clinical study will be
required to validate the present
results, including estimation of
the heat generation rate.