In 2024, the Johns Hopkins Data Science and AI Institute awarded John C. Malone Associate Professor of Computer Science Mathias Unberath one of its first Trusted Dataset Awards, providing him the opportunity to further DSAI’s mission to establish Johns Hopkins as a premier source of trusted datasets for research, scholarship, and translation.
“A principal goal of the Trusted Datasets Awards was to begin building a body of tools and datasets broadly useful for AI researchers with a particular focus on the data,” says Jim Bellingham, DSAI’s research director for safety and assurance. “We are excited by the results of this first round of research, the first of what we hope to be many contributions to safer, more trustworthy AI.”
Through their awarded project, “A Trusted Living Dataset and Compute Infrastructure for Ambient Intelligence in JHM Operating Rooms,” Unberath and his collaborators in the Johns Hopkins Whiting School of Engineering, School of Medicine, Applied Physics Laboratory, and Armstrong Institute for Patient Safety and Quality have created a trusted living dataset and developed sophisticated algorithms to advance ambient intelligence within the Johns Hopkins community.
The research team generated more than 50 videos that capture simulated operating room workflows, including OR staff reenacting surgical procedures. Using these, the team is developing a suite of tools and algorithms that support the detection of humans and objects in the OR. These toolsets, videos, and annotations comprise a dataset and infrastructure that can advance research in ambient AI for surgical environments. In compiling this dataset, Unberath’s team developed a new protocol for annotating surgical videos, addressing limitations in previous related work. The new protocol highlights the need for a pre-specified annotation vocabulary and dictionary, quality assurance, and control measures.
Additionally, Unberath and his team are working with Information Technology at Johns Hopkins to establish a multi-tiered storage and compute access framework that is HIPAA-compliant to protect sensitive patient health information. The goal is to make the dataset securely accessible through Johns Hopkins platforms to enable compliant and efficient model development and training.
Together, the team’s videos, annotations, and toolsets comprise a one-of-a-kind trusted dataset and infrastructure poised to significantly advance research in ambient AI for surgical environments.
“The Trusted Dataset Award allowed us to advance our work on ambient AI for operating room analytics, building our platforms for the secure handling of OR video data captured from both room-level and egocentric camera video feeds, as well as algorithms that handle de-identification among other processing steps,” says Unberath. “We will continue this line of work beyond the Trusted Dataset performance period, further enhancing and expanding the platform and building algorithms for automated data analytics to enhance OR efficiency.”
Learn more about the Data Science and AI Institute Trusted Dataset Awards here.