News | Artificial Intelligence | June 03, 2019

SIIM and ACR Host Machine Learning Challenge for Pneumothorax Detection and Localization

Competition will see teams design artificial intelligence-based algorithms, with winning algorithms open sourced to benefit radiology

SIIM and ACR Host Machine Learning Challenge for Pneumothorax Detection and Localization

June 3, 2019 — The Society for Imaging Informatics in Medicine (SIIM) and the American College of Radiology (ACR) are collaborating with the Society of Thoracic Radiology (STR) and MD.ai to host a Machine Learning Challenge on Pneumothorax Detection and Localization on Kaggle. The challenge will use augmented annotations on the public chest radiograph dataset from the National Institutes of Health (NIH). The augmented annotations were created by radiologists from SIIM and STR using a commercial web-based tool from MD.ai, and follow the ACR Data Science Institute’s structured artificial intelligence (AI) use case for pneumothorax detection.

“SIIM is very excited to leverage prior annotation work and share the resulting dataset with ACR in this challenge” said Steven G. Langer, Ph.D., CIIP, professor of radiologic physics and imaging informatics at Mayo Clinic and co-chair of the SIIM Machine Learning Committee.

Challenge participants will develop high-quality pneumothorax detection algorithms to prioritize patients for expedited review and treatment and promote the development of clinically relevant use cases for AI. Standards-based healthcare application programming interfaces (APIs) will be used to reduce the interoperability barriers to clinical implementation post-competition.

The ACR DSI and SIIM will use their respective talents and resources to promote deployment of the winning algorithm(s) into clinical use for the benefit of the greater medical imaging community, improving quality and efficiency in healthcare.

“We are encouraging participants to use standard healthcare APIs (FHIR and DICOMweb) in the competition,” added his fellow Co-Chair George Shih, M.D., MS, associate professor of clinical radiology at Weill Cornell Medicine, who is a paid consultant and an equity board member for MD.ai.

“This Kaggle competition will result in open source algorithms to help solve a serious healthcare problem that can lead to death if not identified and treated quickly,” said Bibb Allen Jr., M.D., FACR, ACR Data Science Institute chief medical officer. “By co-hosting this challenge to engage data scientists in solving real clinical problems defined in a structured AI use case, we are bringing together the radiology and technical communities to generate new healthcare solutions and improve patient care.”

“The STR is excited to participate in the augmentation of the NIH dataset by providing our subspecialty expertise in the annotation and adjudication process,” said Carol C. Wu, M.D., chair of the STR Big Data Subcommittee.

SIIM and the ACR will kick off the Pneumothorax Detection Challenge at the SIIM 2019 Annual Meeting, June 26-28, in Aurora, Colo., and award the winning teams at the 2019 SIIM Conference on Machine Intelligence in Medical Imaging (C-MIMI), Sept. 22-23 in Austin, Texas.

For more information: www.siim.org

Related Content

Radiation After Immunotherapy Improves Progression-free Survival for Some Metastatic Lung Cancer Patients
News | Lung Cancer | September 18, 2019
Adding precisely aimed, escalated doses of radiation after patients no longer respond to immunotherapy reinvigorates...
Nurse Practitioners, Physician Assistants Rarely Interpret Diagnostic Imaging Studies
News | Radiology Business | September 18, 2019
September 18, 2019 — Although Medicare claims data confirm the...
Varian Unveils Ethos Solution for Adaptive Radiation Therapy
News | Image Guided Radiation Therapy (IGRT) | September 16, 2019
At the 2019 American Society for Radiation Oncology (ASTRO) annual meeting, being held Sept. 15-18 in Chicago, Varian...
FDA Clears GE Healthcare's Critical Care Suite Chest X-ray AI
Technology | X-Ray | September 12, 2019
GE Healthcare announced the U.S. Food and Drug Administration’s (FDA) 510(k) clearance of Critical Care Suite, a...
Richardson Healthcare Receives CE Mark Approval for ALTA750 Canon/Toshiba CT Replacement Tube
News | Computed Tomography (CT) | September 11, 2019
Richardson Healthcare, a Division of Richardson Electronics Ltd., announced it has received CE Mark approval for the...
iCAD's ProFound AI Wins Best New Radiology Solution in 2019 MedTech Breakthrough Awards
News | Computer-Aided Detection Software | September 09, 2019
iCAD Inc. announced MedTech Breakthrough, an independent organization that recognizes the top companies and solutions...
Imaging Biometrics and Medical College of Wisconsin Awarded NIH Grant
News | Neuro Imaging | September 09, 2019
Imaging Biometrics LLC (IB), in collaboration with the Medical College of Wisconsin (MCW), has received a $2.75 million...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used deep learning, a form of machine learning, which is a type of artificial intelligence. Image courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Society of Breast Imaging (SBI) | September 06, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Philips and Fujifilm booths at SIIM 2019.

Philips and Fujifilm booths at SIIM 2019.

Feature | SIIM | September 06, 2019 | By Greg Freiherr
Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Inform
Sudhen Desai, M.D.

Sudhen Desai, M.D.

Feature | Pediatric Imaging | September 04, 2019 | By Jeff Zagoudis
Burnout has become a popular buzzword in today’s business world, meant to describe prolonged periods of stress in the