In developing medical interventions using untethered milli- and microrobots, ensuring safety and effectiveness relies on robust methods for detection, real-time tracking, and precise localization within the body. However, the inherent non-transparency of the human body poses a significant obstacle, limiting robot detection primarily to specialized imaging systems such as X-ray fluoroscopy, which often lack crucial anatomical details. Consequently, the robot operator (human or machine) would encounter severe challenges in accurately determining the location of the robot and steering its motion. This study explores the feasibility of circumventing this challenge by creating a simulation environment that contains the precise digital replica (virtual twin) of a model microrobot operational workspace. Synchronizing coordinate systems between the virtual and real worlds and continuously integrating microrobot position data from the image stream into the virtual twin allows the microrobot operator to control navigation in the virtual world. We validate this concept by demonstrating the tracking and steering of a mobile magnetic robot in confined phantoms with high temporal resolution (< 100 ms, with an average of ~20 ms) visual feedback. Additionally, our object detection-based localization approach offers the potential to reduce overall patient exposure to X-ray doses during continuous microrobot tracking without compromising tracking accuracy. Ultimately, we address a critical gap in developing image-guided remote interventions with untethered medical microrobots, particularly for near-future applications in animal models and human patients.

Abstract-- A hybrid continuum robot design is introduced that combines a proximal tendon-actuated section with a distal telescoping section comprised of permanent-magnet spheres actuated using an external magnet. While, individually, each section can approach a point in its workspace from one or at most several orientations, the two-section combination possesses a dexterous workspace. The paper describes kinematic modeling of the hybrid design and provides a description of the dexterous workspace. We present experimental validation which shows that a simplified kinematic model produces tip position mean and maximum errors of 3% and 7% of total robot length, respectively. Continuum robots have attracted considerable attention for applications in minimally invasive diagnostics and therapeutics Figure 1: Hybrid robot comprised of proximal tendon-actuated over the past decade [1].

When most people think of artificial intelligence (AI) they picture robots from Hollywood blockbusters or science fiction, but in reality, machine learning is already being used for many real-life applications. Parvin Mousavi, a professor in the Queen's School of Computing, is one of the researchers on the forefront of AI developments and is working to advance next generation medical interventions. In recognition of her leadership in the field, Dr. Mousavi was recently named as a Canadian Institute for Advanced Research (CIFAR) Chair of Artificial Intelligence at the Vector Institute. The chairs will help advance Canadian leadership in priority areas under the Pan-Canadian Artificial Intelligence Strategy at CIFAR, which has identified AI for health as a priority area for growth. Broadly speaking, Dr. Mousavi's research focus is on using AI to better peoples lives, but a more in-depth look reveals a track record of advancing patient centric care, and data modelling using AI to increase the uptake of new methods used in clinical decision-making.

Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring.

It has been widely recognized that uncertainty is an inevitable aspect of diagnosis and treatment of medical disorders. Such uncertainties hence, need to be considered in computerized medical models. The existing medical modeling techniques however, have mainly focused on capturing uncertainty associated with diagnosis of medical disorders while ignoring uncertainty of treatments. To tackle this issue, we have proposed using a fuzzy-based modeling and description technique for capturing uncertainties in treatment plans. We have further contributed a formal framework which allows for goal-oriented modeling and analysis of medical treatments.

New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP) to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.

CHICAGO: New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP)1 to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable2 representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.

Melissa Mulholland was 16 weeks pregnant with her second child when her doctor noticed something unusual in an ultrasound scan. It was a rare fetal condition called posterior urethral valves, PUV, and it meant her son wouldn't survive the womb without medical intervention. She was fortunate to have a doctor skilled in detecting the condition and intervening to address it, and the good news is that her son, Conor, is now 5 years old. But the experience left Mulholland thinking about the families who aren't so lucky to have such expert health care. She wondered if technology could be a solution. She's not an engineer, and doesn't have a technical background, but she works at Microsoft, so she's familiar with the latest technologies in her role working with the company's cloud customers and partners. She asked a question that not a lot of people would ask: could artificial intelligence help?

The research, now published in the Nature journal Scientific Reports, has implications for the early diagnosis of serious illness, and medical intervention. Researchers from the University's School of Public Health and School of Computer Science, along with Australian and international collaborators, used artificial intelligence to analyse the medical imaging of 48 patients' chests. This computer-based analysis was able to predict which patients would die within five years, with 69% accuracy -- comparable to'manual' predictions by clinicians. This is the first study of its kind using medical images and artificial intelligence. "Predicting the future of a patient is useful because it may enable doctors to tailor treatments to the individual," says lead author Dr Luke Oakden-Rayner, a radiologist and PhD student with the University of Adelaide's School of Public Health.

A computer's ability to predict a patient's lifespan simply by looking at images of their organs is a step closer to becoming a reality, thanks to new research led by the University of Adelaide. The research, now published in the Nature journal Scientific Reports, has implications for the early diagnosis of serious illness, and medical intervention. Researchers from the University's School of Public Health and School of Computer Science, along with Australian and international collaborators, used artificial intelligence to analyze the medical imaging of 48 patients' chests. This computer-based analysis was able to predict which patients would die within five years, with 69% accuracy - comparable to'manual' predictions by clinicians. This is the first study of its kind using medical images and artificial intelligence.