Can you build an algorithm that automatically detects potential pneumonia cases?

In this competition, you’re challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.

Here’s the backstory and why solving this problem matters.

Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease. In the United States, pneumonia accounts for over 500,000 visits to emergency departments [1] and over 50,000 deaths in 2015 [2], keeping the ailment on the list of top 10 causes of death in the country.

While common, accurately diagnosing pneumonia is a tall order. It requires review of a chest radiograph (CXR) by highly trained specialists and confirmation through clinical history, vital signs and laboratory exams. Pneumonia usually manifests as an area or areas of increased opacity [3] on CXR. However, the diagnosis of pneumonia on CXR is complicated because of a number of other conditions in the lungs such as fluid overload (pulmonary edema), bleeding, volume loss (atelectasis or collapse), lung cancer, or post-radiation or surgical changes. Outside of the lungs, fluid in the pleural space (pleural effusion) also appears as increased opacity on CXR. When available, comparison of CXRs of the patient taken at different time points and correlation with clinical symptoms and history are helpful in making the diagnosis.

CXRs are the most commonly performed diagnostic imaging study. A number of factors such as positioning of the patient and depth of inspiration can alter the appearance of the CXR [4], complicating interpretation further. In addition, clinicians are faced with reading high volumes of images every shift.

To improve the efficiency and reach of diagnostic services, the Radiological Society of North America (RSNA®) has reached out to Kaggle’s machine learning community and collaborated with the US National Institutes of Health, The Society of Thoracic Radiology, and MD.ai to develop a rich dataset for this challenge.

The RSNA is an international society of radiologists, medical physicists and other medical professionals with more than 54,000 members from 146 countries across the globe. They see the potential for ML to automate initial detection (imaging screening) of potential pneumonia cases in order to prioritize and expedite their review.

Challenge participants may be invited to present their AI models and methodologies during an award ceremony at the RSNA Annual Meeting which will be held in Chicago, Illinois, USA, from November 25-30, 2018.

Thank you to the National Institutes of Health Clinical Center for publicly providing the Chest X-Ray dataset [5].

NIH News release: NIH Clinical Center provides one of the largest publicly available chest x-ray datasets to scientific community

Original source files and documents

Also, a big thank you to the competition organizers!

Rui P, Kang K. National Ambulatory Medical Care Survey: 2015 Emergency Department Summary Tables. Table 27. Available from: www.cdc.gov/nchs/data/nhamcs/web_tables/2015_ed_web_tables.pdf

Deaths: Final Data for 2015. Supplemental Tables. Tables I-21, I-22. Available from: www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06_tables.pdf

Franquet T. Imaging of community-acquired pneumonia. J Thorac Imaging 2018 (epub ahead of print). PMID 30036297

Kelly B. The Chest Radiograph. Ulster Med J 2012;81(3):143-148

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR 2017, http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf