CONTEXT

Our team received a game-changing dataset from vendors, comprising extensive lung CT scans. The objective was to automatically detect and segment cancerous lung nodules using a Deep Learning model. The client required an advanced solution to identify patients most likely to have malignant nodules, optimizing recruitment and ensuring a homogeneous study population. Additionally, the model aimed to quantify tumor volume changes with higher precision than manual assessments, improving treatment efficacy evaluation. 

RESOLUTION

Our experts implemented a Convolutional Neural Network (CNN) to extract critical imaging features potentially linked to genetic or molecular markers. This approach not only enhanced nodule detection but also provided deeper insights into tumor biology, paving the way for personalized treatment strategies. 

RESULT

The project was a success, ensuring the quality and consistency of medical data used for training the Deep Learning model. A rigorous validation process on independent datasets confirmed the model’s reliability and clinical utility. Additionally, our team addressed potential biases in data and model predictions while maintaining patient privacy and informed consent throughout the study.