Methods and systems for determining health of an organ based on medical image data using artificial intelligence

Abstract

A method for determining the health of an organ, specifically the thymus, using medical image data is disclosed. The method involves generating a foundation model by retrieving and pre-processing a set of images related to various organs, including CT scans of the thymus. A deep convolutional neural network is then used to produce embedding vectors, which are refined through a self-supervised learning framework. A task-specific classifying model is generated, comprising a Quality Control (QC) model and a score model. The QC model provides binary predictions differentiating between normal thymus degradation and abnormalities, while the score model evaluates fat and soft tissue levels in the thymus. The health of a potential organ is evaluated by receiving a CT of an organ to be evaluated, generating an embedding vector using the foundation model, and applying the task-specific classifying model to produce a health score. Clinical solutions, including chemotherapy and immunotherapy are then applied based on the health score.