• ALS: Diagnosis by Deduction

      Morgan, Gideon; Vajipayajula, Dhruv; Shah, Aarohi; McGrath, Rose; Swanchara, Melissa; Leonard, Brian; Leonard|0000-0002-4901-0977 (Temple University. Grey Matters, 2021-05)
    • Bridging the Gap Between the Science & People Affected by Traumatic Brain Injury

      Sotelo, Angelica; Baffoe-Bonnie, Jude; Shah, Aarohi; Michel, Erin; Jozwik, Matthew; Cában Rivera, Carolina (Temple University. Grey Matters, 2021-05)
      Most Americans have probably seen media coverage of a National Football League (NFL) game. Because American football is a full contact sport, it is probably not surprising that frequent collisions between players result in concussions, or “mild” traumatic brain injury (TBI) [1]. While concussions have been associated with American football and its players since 1994, athletes are not the only people affected by them [2]. 69 million individuals sustain TBI each year worldwide [3]. According to the Centers for Disease Control and Prevention (CDC), while a concussion itself is not life-threatening, it is the after effects of the concussion that contribute to complications which may hinder a person’s quality of life for some time [1]. Recent research on the oculomotor system and neuro-optometric rehabilitation may offer affected individuals more opportunities for concussion recovery. Concussions affect our brain in a multitude of ways, including our physical, chemical, mental, and visual processes; however, neuro-optometric rehabilitation is a glimmer of hope for those recovering from traumatic brain injury.
    • Grey Matters, Issue 1, Spring 2021

      Shah, Mansi (Temple University. Grey Matters, 2021-05)
    • Grey Matters, Issue 2, Fall 2021

      Shah, Mansi; Gibson, Eve (Temple University. Grey Matters, 2021-12)
    • Machine Learning Applications to the Diagnosis of Neurodegenerative Diseases

      Post, Cristen (Temple University. Grey Matters, 2021-12)
      Imagine you are enjoying a game of Pictionary with your family. As the picturist, you pick up a card from the deck. The card reads “umbrella” as you flip it over. You quickly start sketching an umbrella as the sand timer begins its one minute countdown. As you draw, a family member analyzes the drawing to guess the word. This game of Pictionary is analogous to machine learning, which is a type of artificial intelligence. Artificial intelligence (AI) is broadly defined as the use of computer algorithms in a way that imitates critical analysis and thinking analogous to humans. Machine learning is a subset of AI that allows computer algorithms to make accurate predictions based on a set of data. As children, we are shown pictures of objects, including umbrellas, and are taught that the image of an umbrella correlates to the word umbrella. This is the process of learning. Having seen umbrellas multiple times, our brains learn to associate the image with the word and can now recognize umbrellas. Similar to how our brains learn, machine learning allows for a set of computer algorithms (also known as a model) to learn by being shown a set of data and taught the patterns among it. The model can then make predictions based on a new set of data by applying the patterns it learned. As artificial intelligence (AI) improves efficiency and accuracy, it is emerging as a powerful tool to aid in providing solutions in multiple complex fields. Medicine is an example of a field that AI is used for, particularly the areas of diagnosis and treatment. Since neurodegenerative diseases at present have no cures, early diagnosis and avoiding misdiagnosis are crucial to ensuring patients have a good quality of life [3]. This article will investigate the application of machine learning techniques to the diagnosis and treatment planning of neurodegenerative diseases.