Gianluca De Novi

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

In an era characterized by the convergence of cutting-edge technologies, the world of healthcare and public health is on the brink of a profound transformation that will shape the future of medicine and wellness. This transformation is not merely an incremental step forward; it is a paradigm shift. It is driven by the synergistic integration of digital twins, blockchain technology, artificial intelligence (AI), and multi-omics platforms, collectively propelling us into uncharted territory. The integration of these innovations holds the potential to rewrite the rules of engagement in clinical trials, revamp the strategies for preventing public health crises, and redefine the way we manage, share, and secure healthcare data. As we embark on this journey of exploration and innovation, we find ourselves at a pivotal juncture, akin to the invention of the microscope in biology or the discovery of antibiotics in medicine. We are at the crossroads of a new era, one that teems with immense promise and transformative power.

This article presents the development of a prototype robotic eye-motion system for a novel simulator of ocular and craniofacial trauma that was developed for practical skills training of eye surgeons and first responders. The simulator fills a gap in the ophthalmological training domain, specifically between virtual reality-based systems for cataract and retinal repair and part-task trainers without quantitative measurement capabilities. Combining physical anatomical modules, instrument tracking and embedded sensors with a data acquisition/feedback system in a portable bench-top structure, it offers an alternative to animal- and cadaver-based training. The prototype robotic eye system described includes multiple human eye globe motion features: eye pitch and yaw motion, proptosis, and sensing of applied force to detect pressure/load applied to the globe.