We already introduced Neha Mani, the winner of the Regeneron International Science and Engineering Fair 2021 in Applied Microbiology. Ron Nachum was awarded in the category of Disease Detection and Diagnosis and therefore also received a ZEISS Primostar 3 microscope donation.
Ron is a senior at Thomas Jefferson High School for Science and Technology (Virginia, USA). He has conducted several research projects at the intersection of computer science and medicine, specializing in the use of machine learning and computer vision to increase access to healthcare.
His works have been recognized by professional organizations and press, including a peer-reviewed publication and presentations at events.
Assessing neurodegenerative diseases
Ron developed a novel system for assessment of Alzheimer’s and Parkinson’s diseases called PANDwriting that requires just a camera, pen, and paper. He advanced novel computer vision algorithms to extract information about patients’ fine motor abilities from videos of handwriting and then assessed them with machine learning based on these features for fast, accessible, and accurate testing. As such, PANDwriting is especially important for increasing healthcare access and equity in low-income areas and resource-poor health systems around the world.
Ron tells us about his research, participating in Regeneron ISEF 2021 and his future vision:
What was your motivation for the application?
I’ve had a passion for STEM for as long as I can remember, always looking to explore challenging problems in math and science from a young age. In high school, I found a home using computer science, especially computer vision and machine learning, to increase access to healthcare. When my grandfather, an important role model of mine, passed away in 2020 from Alzheimer’s Disease, I knew I needed to apply the lessons I’d learned to combat neurodegenerative diseases in his honor. This combination motivated me to develop PANDwriting and submit the project to ISEF, as well as continue working on it to create real-world impact and help people around the world.
Describe the project you’ve submitted.
I developed an accessible, fast, and accurate system for neurodegenerative disease screening. My system, called PANDwriting, uses a regular camera to capture videos of patient handwriting movements. These videos are analyzed with novel computer vision and machine learning algorithms I developed to extract information describing fine motor movement ability. Based on this extracted fine motor kinematic information, PANDwriting then classifies patients and provides diagnostic assessments for Parkinson’s or Alzheimer’s diseases.
Why are your findings important?
The PANDwriting system has the potential for massive impact in the fight against neurodegenerative diseases, as it can be used for diagnosis and long-term monitoring on a wide scale at little-to-no cost, taking just a few minutes to provide accuracy rivaling that of the clinical process. This system has major implications for in-office or at-home use in low-cost areas and resource-poor health systems, aiding in widespread access to diagnostic information and better treatment outcomes for millions of people. Furthermore, due to the nature of this system as one leveraging computer vision and machine learning, as more data is collected it has a massive capacity to improve in accuracy and scope over time. Computer vision systems can capture a wide variety of information beyond just the handwriting movements, such as patient posture and pen grip which can further increase the system’s accuracy. Most importantly, however, PANDwriting is generalizable. The computer vision system for extracting fine motor movement data from handwriting is the first of its kind, and is applicable not just for neurodegenerative diseases but also strokes, arthritis, and early developmental disorders. Overall, PANDwriting offers a fast, accurate, and inexpensive system for detecting neurodegenerative diseases in millions of patients around the world.
What was the hardest part?
I thought the most difficult – and most rewarding – aspect of this project was the circumstances in which I conducted it. The COVID-19 pandemic presented major challenges to all aspects of development and testing, especially so in limiting my ability to collect data to test the system. These circumstances forced me to adapt and develop a robust system that could be tested in a variety of environments and without large-scale data collection made nearly impossible by the COVID-19 pandemic. Now, as the pandemic is winding down with vaccinations, I am able to pursue this data collection as I work to take my project to the real world.
When you’ll graduate, where would you like to work?
When I graduate from high school I’d like to see myself blazing my own trail and spinning off my innovations in computer science into procuts and entrepreneurship that impact people around the world. I think that working in medicine is a great way to do that, but I also hope to explore other fields in college and beyond to find the area in which I can best improve the lives of others.