Abstract: Extended Reality (XR) technologies are gaining traction as effective tools for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a novel system for real-time 3D tracking and visualization of intracardiac echocardiography (ICE) catheters, with precise measurement of the roll angle. The system's data is integrated into an interactive Unity-based environment, rendered through the Meta Quest 3 XR headset, combining a dynamically tracked catheter with a patient-specific 3D heart model. This immersive environment allows the testing of the importance of 3D depth perception, in comparison to 2D projections, as a form of visualization in XR. Our experimental study, conducted using the ICE catheter with six participants, suggests that 3D visualization is not necessarily beneficial over 2D views offered by the XR system; although all cardiologists saw its utility for pre-operative training, planning, and intra-operative guidance. The proposed system qualitatively shows great promise in transforming catheter-based interventions, particularly ICE procedures, by improving visualization, interactivity, and skill development. Keywords: Percutaneous Cardiac Intervention, Extended Reality, Computer Vision, 3D visualization, ICE catheter, Roll Angle 1. INTRODUCTION Minimally invasive interventions (MII) have revolutionized the field of cardiac care, offering patients reduced recovery times, lower risks of complications, and shorter hospital stays compared to traditional open-heart surgeries. These procedures, such as percutaneous cardiac interventions, rely on the precise navigation of catheters through complex vascular structures and heart chambers[1-6].

Sharing protected health information (PHI) is critical for furthering biomedical research. Before data can be distributed, practitioners often perform deidentification to remove any PHI contained in the text. Contemporary deidentification methods are evaluated on highly saturated datasets (tools achieve near-perfect accuracy) which may not reflect the full variability or complexity of real-world clinical text and annotating them is resource intensive, which is a barrier to real-world applications. To address this gap, we developed an adversarial approach using a large language model (LLM) to re-identify the patient corresponding to a redacted clinical note and evaluated the performance with a novel De-Identification/Re-Identification (DIRI) method. Our method uses a large language model to reidentify the patient corresponding to a redacted clinical note. We demonstrate our method on medical data from Weill Cornell Medicine anonymized with three deidentification tools: rule-based Philter and two deep-learning-based models, BiLSTM-CRF and ClinicalBERT. Although ClinicalBERT was the most effective, masking all identified PII, our tool still reidentified 9% of clinical notes Our study highlights significant weaknesses in current deidentification technologies while providing a tool for iterative development and improvement.

Summary: Ocular drift, or tiny eye movements that seem random can be influenced by prior knowledge of an expected visual target, researchers report. A very subtle and seemingly random type of eye movement called ocular drift can be influenced by prior knowledge of the expected visual target, suggesting a surprising level of cognitive control over the eyes, according to a study led by Weill Cornell Medicine neuroscientists. The discovery, described Apr. 3 in Current Biology, adds to the scientific understanding of how vision--far from being a mere absorption of incoming signals from the retina--is controlled and directed by cognitive processes. "These eye movements are so tiny that we're not even conscious of them, and yet our brains somehow can use the knowledge of the visual task to control them," says study lead author Dr. Yen-Chu Lin, who carried out the work as a Fred Plum Fellow in Systems Neurology and Neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine. Dr. Lin works in the laboratory of study senior author Dr. Jonathan Victor, the Fred Plum Professor of Neurology at Weill Cornell Medicine. The study involved a close collaboration with the laboratory of Dr. Michele Rucci, professor of brain and cognitive sciences and neuroscience at the University of Rochester.

An artificial intelligence algorithm can determine non-invasively, with about 70 percent accuracy, if an in vitro fertilized embryo has a normal or abnormal number of chromosomes, according to a new study from researchers at Weill Cornell Medicine. Having an abnormal number of chromosomes, a condition called aneuploidy, is a major reason embryos derived from in vitro fertilization (IVF) fail to implant or result in a healthy pregnancy. One of the current methods for detecting aneuploidy involves the biopsy-like sampling and genetic testing of cells from an embryo--an approach that adds cost to the IVF process and is invasive to the embryo. The new algorithm, STORK-A, described in a paper published Dec. 19 in Lancet Digital Health, can help predict aneuploidy without the disadvantages of biopsy. It operates by analyzing microscope images of the embryo and incorporates information about maternal age and the IVF clinic's scoring of the embryo's appearance.

Yael Bensoussan, MD, is part of the USF Health's department of Otolaryngology - Head & Neck Surgery. She's leading an effort to collect voice data that can be used to diagnose illnesses. Yael Bensoussan, MD, is part of the USF Health's department of Otolaryngology - Head & Neck Surgery. She's leading an effort to collect voice data that can be used to diagnose illnesses. Voices offer lots of information.

Employing artificial intelligence to help improve outcomes for people with cardiovascular disease is the focus of a three-year, $15 million collaboration among Cornell Tech, the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS) and NewYork-Presbyterian – with physicians from its affiliated medical schools Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons (Columbia University VP&S). The Cardiovascular AI Initiative, to be funded by NewYork-Presbyterian, was launched this summer in a virtual meeting featuring approximately 40 representatives from the institutions. "AI is poised to fundamentally transform outcomes in cardiovascular health care by providing doctors with better models for diagnosis and risk prediction in heart disease," said Kavita Bala, professor of computer science and dean of Cornell Bowers CIS. "This unique collaboration between Cornell's world-leading experts in machine learning and AI and outstanding cardiologists and clinicians from NewYork-Presbyterian, Weill Cornell Medicine and Columbia will drive this next wave of innovation for long-lasting impact on cardiovascular health care." "NewYork-Presbyterian is thrilled to be joining forces with Cornell Tech and Cornell Bowers CIS to harness advanced technology and develop insights into the prediction and prevention of heart disease to benefit our patients," said Dr. Steven J. Corwin, president and chief executive officer of NewYork-Presbyterian. "Together with our world-class physicians from Weill Cornell Medicine and Columbia, we can transform the way health care is delivered."

Weill Cornell Medicine researchers are using machine learning, a form of artificial intelligence, to shed light on genetic mutations associated with spina bifida. In this birth defect, the neural tube that forms the spinal cord during pregnancy does not close so that spinal nerves are exposed, resulting in paralysis and high risk of other complications. Their new study, published Dec. 16 in PNAS, "brings us closer to being able to provide a precision medicine approach to families who are looking to ensure healthy birth outcomes and the greatest potential for infants affected by spina bifida," said senior author Dr. Margaret Elizabeth Ross, director of the Center for Neurogenetics and professor of neuroscience in the Feil Family Brain and Mind Research Institute and the Nathan Cummings Professor in Neurology at Weill Cornell Medicine. Spina bifida is a complex genetic disorder, meaning it's not generally caused by malfunction in a single gene but usually requires an interplay of several genes that have been altered in relatively small ways. Environmental conditions such as nutrition and the medications and supplements women are taking can also impact fetal health.

In collaboration with Oregon Health & Science University and Weill Cornell Medicine, the goal is to code an easy-to-use smart phone app to help assess whether a follow-up medical diagnosis is needed. "Alzheimer's is tough to deal with and it's very easy to confuse its early stage, mild cognitive impairment, with normal cognitive decline as we're getting older," said Zhou, who leads a research group in the Department of Computer Science and Engineering. Although this AI approach might sound like science fiction, Zhou and his team have already shown in preliminary tests that it is as accurate as MRIs in recognizing early warning signs. These tests used data collected by collaborators at Oregon Health & Science University who are leading a clinical trial studying how conversations might serve as therapeutic intervention for dementia or early Alzheimer's. Joining Zhou on this grant are Hiroko Dodge, a professor of neurology at Oregon Health & Science University, and Fei Wang, an assistant professor of health care policy and research at Weill Cornell Medicine.

With the support of a new grant worth $3.9 million, Michigan State University researchers are developing technology that scans speech and vocabulary patterns to catch early signs of Alzheimer's disease, the most common cause of dementia. Jiayu Zhou, an associate professor in MSU's College of Engineering, is leading the effort that's powered by artificial intelligence, or AI, and funded by the National Institutes of Health. In collaboration with Oregon Health & Science University and Weill Cornell Medicine, the goal is to code an easy-to-use smart phone app to help assess whether a follow-up medical diagnosis is needed. "Alzheimer's is tough to deal with and it's very easy to confuse its early stage, mild cognitive impairment, with normal cognitive decline as we're getting older," said Zhou, who leads a research group in the Department of Computer Science and Engineering. "It's only when it gets worse that we realize what's going on and, by that time, it's too late."

The first generation to grow up entirely in the 21st century will never remember a time before smartphones or smart assistants. They will likely be the first children to ride in self-driving cars, as well as the first whose healthcare and education could be increasingly turned over to artificially intelligent machines. Futurists, demographers, and marketers have yet to agree on the specifics of what defines the next wave of humanity to follow Generation Z. That hasn't stopped some, like Australian futurist Mark McCrindle, from coining the term Generation Alpha, denoting a sort of reboot of society in a fully-realized digital age. "In the past, the individual had no power, really," McCrindle told Business Insider.