Building the Future of Ultrasound: My Journey in Machine Learning and RMSK Certification
Introduction
As an RMSK-certified clinician, I have always been fascinated by the role of diagnostic ultrasound in musculoskeletal assessment. Ultrasound is a powerful tool, but its interpretation requires years of practice, pattern recognition, and experience. I started to think: How can we make ultrasound more accessible to young clinicians, or those new to the field, without requiring years of trial and error?
The answer led me to an exciting intersection of machine learning and ultrasound interpretation. By developing an AI-powered segmentation tool, I wanted to create a solution that assists clinicians in identifying key anatomical structures, improving accuracy, and bridging the gap for those new to ultrasound.
The Inspiration: A Research-Driven Approach
My project was heavily inspired by research in deep learning segmentation of musculoskeletal ultrasound images. In particular, I built upon the work published by Francesco Marzola, Nens van Alfen, Jonne Doorduin, and Kristen M. Meiburger in their study:
Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment.
(Computers in Biology and Medicine, 2021, https://doi.org/10.1016/j.compbiomed.2021.104623)
Their dataset provided valuable ultrasound images and segmentation masks, which I used to train a custom U-Net deep learning model. My goal was to take this research and build a functional, deployable AI tool that could support real-world clinical decision-making.
Data Exploration and Machine Learning Approach
Before building the AI model, I conducted a thorough data analysis to understand the characteristics of the dataset. Below are some key visualizations of the dataset:
Figure: Distribution of key dataset attributes such as age, body length, and weight.
Key Observations:
• The dataset includes a diverse age range, capturing both young and older individuals.
• Body length distribution is skewed, likely representing variations in anatomical structures.
• Weight distribution follows a near-normal pattern but includes a significant range of values.
Model Training Pipeline
1. Data Preprocessing:
• Images were normalized and resized to ensure consistent input.
• Segmentation masks were binarized to create clear delineation between structures.
• Augmentation techniques such as flipping, rotation, and brightness adjustments were applied.
2. Deep Learning Model:
• I implemented a U-Net architecture, a widely used segmentation model for medical imaging.
• The model was trained using cross-entropy loss and Dice coefficient to optimize segmentation accuracy.
• Validation and testing were performed to fine-tune hyperparameters and avoid overfitting.
