Preventing Progression

Identifying the Pulmonary Fibrosis Progressive Phenotype via Deep Learning Algorithms Using Big Data

Lead: Prof Tamera Corte

While the overall prognosis for pulmonary fibrosis is poor, the disease course is highly variable with different rates of progression.

The ability to stratify patients based on their predicted disease course is critical to inform management decisions including when to commence anti-fibrotic treatment, lung transplantation referral, planning end-of-life care and enrolment in clinical trials. No staging model to date has truly predicted an individual’s disease trajectory.

  • We are employing multidimensional staging and deep learning algorithms (using big data) for the prognostication of pulmonary fibrosis patients leading to precision guided care.
  • We are developing deep learning algorithms using HRCT data for prognostication of patients with pulmonary fibrosis.