Muhammad Talha Iqbal

Academic

Career

Verbatim text

AITRAX
AITRAX is a deep learning product developed for predicting the progression of pulmonary fibrosis, a chronic lung disease. The product uses convolutional neural networks (CNNs) to analyze medical images, such as CT scans or X-rays of the lungs, and identify specific features that indicate pulmonary fibrosis, such as scarring or abnormalities in lung tissue.
To provide a more comprehensive picture of a patient's health, AITRAX incorporates other patient information, including medical history and lung function tests, into the model using recurrent neural networks (RNNs) and other deep learning techniques. The product has been trained on a large dataset of patient information, and it can now predict the progression of pulmonary fibrosis in new patients.
AITRAX is useful to doctors as it helps identify patients who are at high risk of developing severe symptoms and who may benefit from early intervention and treatment. It can also be used to monitor the progression of the disease in patients over time and enable doctors to adjust treatment plans as necessary, leading to better patient outcomes.
Overall, AITRAX has the potential to revolutionize the diagnosis, monitoring, and treatment of pulmonary fibrosis, leading to improved outcomes for patients and better management of the disease.
Technology Used:
TensorFlow, Angular, Node, MongoDB, Express
Supervisor Name:
Dr. Labiba Fahad
Group Members:
Muhammad Talha Iqbal (i19 - 0621)
Hassan Ali Khan (i19-0739)
Fawaz Ahmed Dar (i19 - 2196)

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