Thoramo is a research project exploring how machine learning and medical imaging can support the detection of thoracic diseases, with a focus on using deep learning models trained on chest X-rays. The aim was to test whether AI could reliably flag abnormalities that often go unnoticed until they progress to more severe conditions.

After Retimo, which explored retinal imaging for diabetic retinopathy, I wanted to expand my research into thoracic imaging — an area with enormous clinical importance. Chest X-rays are one of the most common diagnostic tools in medicine, yet interpreting them can be challenging, even for experienced radiologists. Thoramo set out to explore whether convolutional neural networks (CNNs) could help flag conditions like pneumonia and other thoracic abnormalities earlier and with greater consistency.

To keep the research practical and clinically relevant, the system was evaluated by a medical doctor from Sulaiman Al Habib Hospital in Riyadh, one of the largest private healthcare groups in the Middle East. That external perspective helped validate the potential of the approach and highlighted both the opportunities and the limits of AI as a supportive diagnostic tool.