2. Mental disorders rank 3rd on
Disability Adjusted Life Years
in the US
60% of adults with a
mental disorder aren’t
treated
1 in 5 adults in the US
develop a mental disorder
in a given year
2
3. In a study, depression was
misdiagnosed 52.7% of the time
by general practitioners
Estimated total 40%
misdiagnosis rate
Incorrect treatment impairs
emotional and physical abilities
3
4. Purpose
Develop an algorithm for mental health disorder
diagnosis based on single-photon emission computed
tomography data using deep neural networks
4
5. Single-photon emission
computed tomography (SPECT)
◎ A radioactive tracer is injected into the
patient
○ iodine-123, technetium-99m, xenon-
133, thallium-201, and fluorine-18
◎ Tracer flows through the bloodstream
without being absorbed tissue cells
◎ Orbiting Camera (Computer
Tomography) records gamma rays
emitted by tracer
◎ Blood flow corresponds to intensity of
brain activity
◎ Measures blood flow to regions
throughout the brain
○ Spatial Resolution of about 10mm
5
6. Deep Neural Networks
◎ Task too complex for a naive
algorithm
◎ A mathematical model
○ Comprised of computational
nodes, each of which has a
nonlinear functions
○ Consist of layers - input,
intermediate, and output
○ Improve over training period
○ With a large dataset, neural
networks are able to extrapolate
knowledge 6
7. Procedure and Methods
◎ Task 1: Classifying General Mental Illness Type:
○ Anxiety
○ Psychotic
○ Eating
◎ Task 2: Classifying Between ADHD and Anxiety
○ Commonly misdiagnosed disorders
○ Differing treatments
◎ Dataset: Over 16000 data points
○ 600 numerical values per scan
○ Other characteristics like age, gender, and race
◎ Anonymous
7
○ Mood
○ Dementia
○ Normal
8. Procedure and Methods
◎ Filled not recorded data values with column mean
◎ Standardised all of the values
◎ Training the Model:
○ Training/Validation (90%/10%) Data Split
○ 4 Dense Layers, 30 Computational Units per Layer
○ 1500 training iterations (2 hours)
○ Accuracy and Loss Logging
○ Overfitting
8
9. Results
◎ General Mental Disorder
Classification
○ 99.1% Accuracy on
Validation Data
○ 99.5% Accuracy on
Training Data
◎ ADHD vs Anxiety
Classification
○ 95.4% Accuracy on
Validation Data
○ 97.6% Accuracy on
Training Data
9
10. Conclusion
◎ Not always 99% accuracy, but much greater than
60% general correct diagnosis rate
○ Does not replace professional medical treatment, but
instead serves as a second opinion to a professional
◎ Applications in Clinics
○ Pointing patients towards a specialized expert
○ More rigorous than questionnaires and polls
◎ Improvements
○ Larger Dataset allows a higher accuracy and allows for the
use of more advanced algorithms 10
11. Resources
Aguirre, Geoffrey K. “Demystifying BOLD FMRI Data.” TheScientist, 17 Feb. 2016, pp. 55–69.,
10.1007/978-1-4419-1329-63.
Amaro and Barker. Study design in fMRI: basic principles. Brain Cogn. 2006, 60:220-232.
Colton, C.W. & Manderscheid, R.W. (2006). Congruencies in Increased Mortality Rates, Years of Potential Life
Lost, and Causes of Death Among Public Mental Health Clients in Eight States. Preventing Chronic
Disease: Public Health Research, Practice and Policy
“Introduction to FMRI.” Nuffield Department of Clinical Neurosciences, University of Oxford,
www.ndcn.ox.ac.uk/divisions/fmrib/what-is-fmri/introduction-to-fmri.
National Association of State Mental Health Program Directors Council. (2006). Morbidity and Mortality in
People with Serious Mental Illness. Alexandria, VA: Parks, J., et al. Retrieved January 16, 2015 from
Krizhevsky Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep
convolutional neural networks." Advances in neural information processing systems. 2012.
Murphy KP. Machine learning: a probabilistic perspective. Cambridge (MA): MIT Press; 2012.
“SPECT Imaging, Myocardial Perfusion, Brain Imaging.” Open Medscience,
openmedscience.com/radiology/spect-imaging/.
“What Is FMRI?” What Is FMRI? - Center for Functional MRI - UC San Diego,
fmri.ucsd.edu/Research/whatisfmri.html.
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