Hi! My name is Sophia, and this is my first devlog for SPARC!

––My overall plan for this project–––––––––––
My plan for this project is to train an AI model to be able to predict whether or not a person has scoliosis, and the severity of the scoliosis based on a picture or video of their back.
After training the model, I would like to create a website that people can visit to measure the severity of their scoliosis with simply their webcam.
In the future, I would like to track the measurements in a log and create a graph that will show the changes in the scoliosis measurements.

—Current Phase+Progress———————————–
Right now, I am working on training the model, testing it, and analyzing its accuracy. In particular, I want the model to find the coordinates of 10 points along the spine that I can use to calculate the deviation and the severity of the scoliosis.
I first learned about how to use YOLOv11n, a pretrained model, from https://docs.ultralytics.com/tasks/pose#train.Afterwards, I downloaded a dataset that the AI can train on from Roboflow.
Then, I set up and activated a virtual environment to prevent dependency conflicts, and installed ultralytics to use YOLO and PyTorch.
After coding up a python program, I was able to train the model for 20 epochs (each one complete pass through the dataset) on a very small dataset of ~50 training images. When I tested the model on an image, its predictions were inaccurate and had low confidence levels. It even detected the person’s back multiple times and made multiple predictions…
I realized that this dataset was too small, so I switched to a larger dataset of ~400 images, and am continuing to train my model until it is able to accurately detect the points along the spine.