Inspiration
The driving force behind VisiBone was the personal impact osteoporosis has had on my family, with several members suffering from this debilitating condition. Recognizing the widespread prevalence and the often silent, undetected nature of osteoporosis until a fracture occurs, I was motivated to create a solution that could facilitate early detection and proactive management. Our goal was to leverage cutting-edge technology to make a meaningful difference in how this disease is approached globally.
Paper
What it does
VisiBone utilizes advanced machine learning techniques to assess and predict the risk of osteoporosis. It comprises two main technological components: a gradient boosting model and a convolutional neural network (CNN). The gradient boosting model analyzes user-inputted demographic and lifestyle data to evaluate osteoporosis risk, while the CNN processes X-ray images to detect signs of bone weakness. Together, these tools offer a comprehensive assessment, helping users and healthcare providers identify and address osteoporosis risk proactively. Additionally, Visibone provides extensive research on ways to prevent osteoporosis and limit its effect. This information was collected from online sources and from first hand accounts of my family.
VisiBone also emphasizes the importance of education and awareness in managing osteoporosis effectively. The platform includes a dedicated section that offers users actionable advice on lifestyle changes that can help prevent the onset or progression of osteoporosis. This includes dietary recommendations, exercise routines tailored to enhance bone strength, and information on medications and supplements that can contribute to bone health. By integrating this preventive advice with its risk assessment tools, VisiBone not only aids in early detection but also empowers individuals to take charge of their bone health through informed decision-making. This holistic approach underscores our commitment to providing a comprehensive solution that addresses all facets of osteoporosis management, making it a valuable resource for both at-risk individuals and healthcare professionals alike.
How we built it
I built VisiBone using a combination of HTML, CSS, JavaScript for the frontend, and Python with Flask for the backend. For the ML models, I used a variety of modern models, created from different libraries such as Tensorflow and PyTorch.
On the prediction page of VisiBone, I employed a gradient boosting model, which is particularly effective for handling tabular data. This model, built using libraries that support ensemble learning techniques, evaluates user-inputted demographic and lifestyle data to assess the risk of osteoporosis. Gradient boosting works by constructing a strong predictive model from a sequence of weaker models, typically decision trees. It improves prediction accuracy by focusing on the mistakes of previous models and correcting them in subsequent iterations, making it highly effective for our use case.
Conversely, the classification page utilizes a Convolutional Neural Network (CNN) to analyze X-ray images for signs of bone weakness indicative of osteoporosis. This CNN, implemented using PyTorch, processes images through multiple layers that detect patterns and features essential for making accurate assessments. Each layer of the network applies filters to capture various aspects of the image, from basic edges to more complex shapes, which are then used to determine the presence of osteoporotic characteristics. This approach is well-suited for image classification tasks and is integral to providing a secondary, confirmatory analysis based on visual data.
Challenges we ran into
One of the major challenges was ensuring the accuracy and reliability of the models, given the variability in the quality of input data and X-ray images. Balancing the sensitivity and specificity of the models to optimize for both early detection and minimal false positives was particularly tough. Additionally, integrating two complex models on a single platform while maintaining a responsive and user-friendly interface presented significant technical hurdles.
Accomplishments that we're proud of
I am immensely proud of developing a platform that successfully integrates two sophisticated machine learning models to address a critical healthcare issue. Achieving high accuracy rates—90.82% for the gradient boosting model and 81.33% validation accuracy for the CNN—was a significant milestone. Moreover, this is one of my first hackathons, and it has been really rewarding to compete in.
What we learned
Throughout this project, I gained deeper insights into the practical challenges of applying machine learning in healthcare, especially in terms of data quality and model training. I also learned about the importance of interdisciplinary collaboration, combining expertise in machine learning, web development, and clinical knowledge to create a solution that is technically sound and medically relevant.
What's next for VisiBone
Looking forward, we aim to expand VisiBone's capabilities by incorporating more diverse data sets and improving the algorithms based on user feedback and ongoing research. We plan to explore additional features, such as integrating genetic factors into the risk assessment model. Additionally, scaling the platform to handle more simultaneous users and extending its reach to other bone-related diseases are key objectives. We are also considering partnerships with healthcare providers to further validate and enhance the platform's clinical utility.
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