Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$500,000.00
Dec 1, 2025
For-profit organization
ATLAS Tactical and ATLAS Probe
1040732
Deep Breathe is developing portable, turnkey, novice-friendly devices to assist warfighters with rapid trauma triage. The long-term vision is to equip military personnel with a simple, screenless ultrasound device with onboard algorithms that detect life-threatening consequences of traumatic injury. This all-in-one device, named “ATLAS Probe”, will revolutionize battlefield diagnostic capabilities. Given the long time required to design and manufacture hardware, we are developing ATLAS Tactical – a mobile application that interfaces with handheld ultrasound devices, functioning as a comprehensive trauma assessment platform. Lastly, we are developing an educational mobile app featuring early prototypes of the algorithms in ATLAS Tactical.
$30,000.00
Aug 25, 2025
For-profit organization
Product Growth Lead
1032518
This project supports the development of an AI-powered lung ultrasound diagnostic tool tailored for veterinary use. Through agile collaboration with veterinarians, the team gathers clinical requirements, coordinates structured interviews, and manages data acquisition and labelling. The role ensures insights are translated into product features, oversees testing protocols, monitors quality, and identifies opportunities for improvement. Market research, collaborator outreach, and economic analysis help evaluate fit and adoption potential across veterinary hospitals and clinics.
$178,000.00
Dec 2, 2024
For-profit organization
Efficient Automated FAST Exam - Phase 1
1026471
Deep Breathe is developing AI-powered algorithms to automate the right upper quadrant (RUQ) assessment of the FAST (Focused Assessment with Sonography in Trauma) exam, a critical ultrasound procedure used in trauma care to detect internal bleeding. The project will create machine learning models capable of identifying free fluid in the abdomen, improving accuracy and accessibility for novice ultrasound operators. The automation of this process aims to standardize emergency diagnostics, reduce human error, and expand use in high-stress, resource-limited environments such as mass casualty incidents or military scenarios.
$30,000.00
May 1, 2023
For-profit organization
YOUTH - Image quality assurance pre-processing for lung ultrasound AI
1005426
This project is a dedicated technical effort to assure a minimum level of image quality for lung ultrasound images that are to be subjected to a computer vision machine learning solution. This project - by creating a pre-processing, image analysis solution - provides an essential quality step in a pathway to assure that images subjected to already developed lung ultrasound image classifiers contain the necessary anatomic elements and are composed in such a fashion as to optimize the accuracy of the downstream machine learning model.