As part of the Artificial Intelligence-Enhanced Management of Severe Traumatic Brain Injury (AIMS-TBI) project, up to ten trauma ICUs in Australia will begin real-time streaming of traumatic brain injury patient monitoring data to the project’s data cloud by the end of this year. Once operational, the scalable AIMS-TBI cloud architecture, developed in collaboration with the Pawsey Supercomputing Center (PSC), Royal Perth Hospital (RPH), The Alfred Hospital, the Royal Melbourne Hospital (RMH), and Curtin and Monash Universities, will enable the timely delivery of monitoring-dependent machine learning and/or deep learning algorithms to the bedside of TBI patients in participating hospitals.

AIMS-TBI project data Scientist Mr. Shiv Meka (left) and Project lead Dr. Robert McNamara (right) during the first live testing of the AIMS-TBI system. (Photo courtesy of the Pawsey Supercomputing Center).

The three-year development of the AIMS-TBI system required extensive effort and resources. Using high resolution ICM+ data from the three founding hospitals, several hundred thousand hours of high-performance computer processing time were required to develop the algorithms and architectures. The custom system (figure 1) is hosted on a hybrid data cloud that combines the scalability, flexibility, and geographic reach of Amazon Web Service's data cloud with the processing power of the PSC. Using this equitable approach, the system can be delivered scalably to patients in regions lacking the necessary processing resources and expertise.

Figure 1. Schematic of AIMS-TBI hybrid cloud architecture (Nov 2022). The AIMS-TBI hybrid cloud consists of 32 separate sub applications. Note that this configuration will change with the upgrade in PSC facilities.

The AIMS-TBI system is designed to capture and process data streamed from the bedside monitor of TBI patients during operation. On data capture, data is initially pre-processed by a pipeline of algorithms which package, compress/expand, clean, align, interpolate, and/or convolute the data. Upon completion of initial pre-processing, the data is split into two distinct streams. The first is to archive the cleaned data on the project’s time series database to allow for further analysis and storage. The second stream involves the operation of clinically relevant algorithms. Currently, a number of traumatic intracranial hypertension (tIH) prediction algorithms operate on this stream, each with its own unique data pre-processing requirements. A handful of AIMS-TBI tIH prediction algorithms are currently undergoing real time observational testing, with testing of other larger tIH prediction as well as other types of algorithms on hold pending restoration of full PSC operations.

The implementation of the AIMS-TBI system promises to revolutionize the management of severe traumatic brain injury patients by providing healthcare professionals with timely, accurate, and actionable insights derived from advanced machine learning and deep learning algorithms. These insights have the potential to improve patient outcomes by enabling clinicians to make more informed decisions based on individualized patient data.

Benefits of AIMS-TBI System

Personalized Patient Care

By leveraging the power of machine learning and deep learning algorithms, the AIMS-TBI system can identify patterns and trends in patient data that may not be readily apparent to healthcare providers. This can help tailor treatment plans to the specific needs of individual patients, potentially improving the overall effectiveness of the care they receive.

Early Warning System

The intracranial hypertension prediction algorithms employed by the AIMS-TBI system can serve as an early warning system for healthcare providers, allowing them to take preventative measures to avoid or mitigate the effects of potentially life-threatening complications.

Improved Clinical Decision-Making

The AIMS-TBI system can provide clinicians with valuable insights into the underlying causes and progression of traumatic brain injuries, leading to better-informed treatment decisions and hopefully ultimately improved patient outcomes.

Data Sharing and Collaboration

The AIMS-TBI project's data cloud enables seamless sharing of information between participating hospitals and research institutions, fostering collaboration and the development of new treatment approaches for traumatic brain injuries.

Challenges and Future Directions

While the AIMS-TBI system represents a substantial advance in the treatment and management of severe traumatic brain injuries, there are still challenges to be addressed. These include the validation of the system's predictive algorithms, ensuring data privacy and security, and the integration of the AIMS-TBI system into existing hospital workflows and electronic health record systems.

Validation of Predictive Algorithms: The accuracy and reliability of the AIMS-TBI system's predictive algorithms are crucial for its clinical usefulness. Rigorous validation through large-scale, multi-center clinical trials will be essential to establish the effectiveness of these algorithms in identifying early warning signs and guiding treatment decisions for TBI patients. Additionally, continuous refinement and improvement of the algorithms based on real-world data design feature of the AIMS-TBI system to maintain algorithm accuracy and relevance over time. This later feature of the system requires validation and monitoring to ensure proper operation and utility.

Data Privacy and Security: With the increasing use of cloud-based systems and the collection of vast amounts of patient data, ensuring the privacy and security of this sensitive information is paramount. Robust data encryption and protection measures must be implemented to safeguard patient data from unauthorized access, data breaches, and potential cyberattacks. Furthermore, adherence to regional and international data protection regulations is essential to maintain compliance and trust among patients and healthcare providers.

As the AIMS-TBI project moves into the next phase of implementation, ongoing research and development efforts will focus on refining the system's predictive algorithms, incorporating additional data sources, and exploring the potential applications of the technology in other areas of medicine. With the full operation of the Pawsey Supercomputing Centre and the continued collaboration between participating institutions, the AIMS-TBI project stands poised to make a significant impact on the lives of patients suffering from traumatic brain injuries.