Over the past decades, over 50% of the United States population was diagnosed with a psychiatric disorder (CDC, 2021).
The most common types of psychiatric disorders include major depressive disorder (MDD), anxiety disorder, bipolar disorder (BD),
dementia, attention-deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), post traumatic stress disorder (PTSD),
and Schizophrenia (WHO, 2022). Patients suffering from psychiatric disorders deal with symptoms ranging from excessive sadness to reduced ability to focus.
A patient may become detached from both reality and society leading them down a dark path. In the short term, these symptoms may not seem horrible, but in the
long run, they can lead to the complete breakdown of one’s mental and physical health. A major component of understanding these disorders is coming to the
realization that the patient’s experience varies case-by-case, meaning that every person's symptoms are unique to them and not everyone should receive the same
treatment (Salters-Pedneault, 2021). Psychiatric disorders can take over a patient’s life, not only damaging their relationship with the things and people they
love, but also their relationship with themselves.
The largest concern regarding mental illnesses has to do with the treatment process.
Since treatment is case-dependent, it is crucial that doctors are able to properly classify patients. Current methods of classification include inputting the
patients Magnetic Resonance Imaging (MRI) scans into picture archiving and communication systems (PACS). According to Doctor Kovvuru from the University of Arkansas
for Medical Sciences, psychiatrists then use this system to transfer the images to Sectra which is a software that biomarks the scans, but does not analyze them.
Biomarkers, measurable factors that act as significant points that appear on the images, help doctors differentiate between the MRI scans of different disorders
(Rasetti et al., 2011). The main biomarkers that these scans check for are gray matter density, subcortical volumes, cortical thickness, fractional anisotropy,
mean diffusivity, axial diffusivity, radial diffusivity, and functional connectivity (de Vos et al., 2020).
In order to identify these biomarkers, specific types of MRI scans are used: anatomical MRI scans, structural MRI scans, and diffusion MRI scans.
Going into more depth about each specific scan, an anatomical scan is used to study the shape, volume, and developmental changes of the brain; therefore,
this model was used by de Vos et al. (2020) when considering gray matter density, subcortical volumes, and cortical thickness. The next type of scan was a
diffusion MRI scan which is mainly used for detecting tumors and monitoring tumor response to treatments over a period of time; hence why de Vos et al.
(2020) used it for the calculation of anisotropy, mean, axial, and radial diffusivity. Lastly, they used a resting state functional MRI scan which is used
for displaying normal and abnormal functional brain connectivity in different conditions. From this, they derived the functional connectivity and the amplitude
of low-frequency fluctuations (de Vos et al., 2020). It is crucial that the right types of MRI scans are put to use when trying to diagnose a patient because
without the right details, it is extremely difficult to correctly classify.
However, even with the assistance of the detailed biomarkers, doctors are still not able to properly inspect the scans because the biomarkers are
outweighed by the amount of overlapping phenotypes displayed in the MRI scans. The mental health disorders with the most similar phenotypes are MDD,
BD, ADHD, and Schizophrenia. Major Depressive Disorder is the greatest cause of disabilities and affects about 10% of the world. In 2013, it was identified
that 60% of people that were classified as having Major Depressive Disorder were actually misdiagnosed (Bloomberg School of Public Health, 2013). Patients with
MDD are most commonly misdiagnosed with BPD, a disorder that leads to extreme mood swings. The effects of Bipolar Disorder go to two different extents:
hypomania, emotional heights, and depression, emotional lows. (Gao et al., 2018). Another neurological disorder is Schizophrenia, a neurological disorder
where patients envision reality in an abnormal way because of hallucinations and delusions. ADHD is a neurological disorder that leads to low attention spans
and hyperactivity. ADHD is most commonly diagnosed in a person's childhood. All of these disorders can be both genetic or acquired (Galderisi et al., 2017).
Machine learning algorithms have been commonly used to help recognize patterns and analyze biomarkers.
There are many different types of machine learning algorithms, but in order to look for specific traits, classification algorithms are the most common.
When creating a machine learning algorithm, Neural Networks are used. Although not every classification algorithm, such as random forests, requires Neural
Networks, it is the most efficient approach (Xin et al., 2019). Neural Networks are a series of algorithms that recognize patterns in a data set. In order to
increase an algorithm’s accuracy, models are optimized by repeatedly tampering with/testing kernels, layers, inputs, outputs, and weights. Layers receive the
weighted input from the previous layer and pass these values as output to the next layer. In this process, they transform the input with a set of mostly
non-linear functions before passing it on. This cycle repeats multiples with the inputs and outputs. Layers are the highest-level building block in deep learning,
and there are many different types of layers, including activation and output layers. Activation layers control how well a model learns the training data.
The most common example of this would be softmax which helps produce a multinomial probability. A multinomial probability is a type of probability distribution
that allows one to find specific numbers for many different outcomes, given that the outcome has a fixed probability that it will happen. Output layers
are the final layer where predictions are received. The two most common output layers are Rectified Linear Unit (ReLU), which prevents exponential growth
by linearizing them, and Sigmoid, which applies a sigmoid function to the input so that the output is between 0 and 1 (Kim et al., 2022).
When looking at MRI scans to identify biomarkers, in specific, the three most common machine learning algorithms used when considering image classification
are Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Transfer Learning. Convolutional Neural Networks are mainly focused on processing
data like images. They are useful because they have a massive data capacity, but on the downside, they have a slower operation time and need more RAM and
memory storage, which is hard to get access to as it is expensive. Support Vector Machines are used for binary classification, but since this project will
focus on four different mental disorders, a binary classification wouldn't be useful. The positive side to SVMs is that they have a distinct margin that
separates between classes, but on the negative side, they are not good for large data sets. Following along with this, if the number of features is greater
than the sample number, then the SVM will underperform on it, meaning it will not train properly and will be inapplicable to other datasets. Transfer learning
is the process of taking previous algorithms and adjusting them to fit the criteria of your project. The three most common transfer learning methods for
classification are Inception V3, ResNet50, and VGG16 (Wahlang et al., 2022). All three of these methods are types of convolutional Neural Networks that have
different layers and layer densities.
By improving upon these models and using machine learning algorithms to help not only display the biomarkers but also help classify between the disorders,
the amount of misdiagnoses can be reduced. If patients are being misclassified, then their treatment will also be incorrect,
leading to horrible side effects. For example, with the wrong medication, patient’s can deal with problems including memory impairment and addiction to that
substance (Alyssa, 2021). These effects will in turn, lead to a worsened condition since the patient was expecting a positive outcome but will instead be in
distress and sadness as they are actually going in the wrong direction.
Although the idea of machine learning in the medical field is not new, the model constructed in this study compared four different neurological disorders
(MDD, BD, ADHD, and Schizophrenia), unlike any project done before. Introducing machine learning and image classification to the realm of neurological disorder
diagnosis is an extremely recent discovery. Beyond this, most classifications are binary, meaning they classify between the null and alternative.
This method identifies biomarkers for all four disorders and identifies specific biomarkers. Patients with neurological complications deserve to get better,
not worse, because of a mistake in their treatment process. They have ensured their trust in this treatment, and it will only make their neurological decline even
more if we are not careful with their medication process.