Developing automated algorithms for detection of lung pathology
Specific aim 1: Use deep learning techniques to classify de-identified lung radiologic studies (CTs, x-rays) according to the type of follow up that would be recommended by radiologists. Other input data may include serial data available on patients, the clinical and demographic metadata such as age, gender, family history, prior medical history and comorbidities, and smoking history. This would allow for risk stratification of patients being screened and guide the most appropriate surveillance and management.
Specific aim 2: Apply deep learning techniques to predict total and cause-specific morbidity and mortality from de-identified lung CTs and/or X-rays and the input data specified in aim 1.
Specific aim 3: Apply modern computer vision and deep learning methods, especially example-based super-resolution, to reconstruct a CT scan approximation from the scout image or from an x-ray. Determine if these CT scan approximations are sufficiently accurate to support aim 1 and aim 2. If successful this would significantly reduce radiation exposure and the associated cancer risks.
Daniel Tse, Google
Diego Ardila, Google
Atilla Kiraly, Google
Wenxing Ye, Google
Shravya Shetty, Google
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.
Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S
Nat. Med. 2019 May PUBMED