Researchers at Johns Hopkins are developing AI-driven video analysis tools to provide surgeons with unbiased skill assessments and personalized feedback. The system segments surgical procedures, detects instruments, and assesses skill in cataract surgery. Dr. Shameema Sikder is leading the development of technologies to improve ophthalmic surgical care standards internationally. Why it matters: AI-based surgical skill assessment could standardize training and improve patient outcomes in the region and globally.
Researchers have developed robotic path-planning and control algorithms for minimally invasive surgery (MIS) that steer flexible needles, incorporating teleoperation and haptic feedback. An AI algorithm was designed to predict target motion due to respiratory movement, improving needle placement accuracy. GANs were used to generate synthetic images visualizing organ and tumor motion. Why it matters: This research demonstrates the potential of AI and robotics to enhance precision and adaptability in MIS, potentially reducing patient trauma and improving recovery times in the region and beyond.
Dr. Laurent A. Lantieri delivered a keynote address at KAUST on April 17, 2017, discussing microsurgical procedures. The address included a brief history of microsurgery. The event took place in the University Auditorium. Why it matters: Such events expose the KAUST community to advances in specialized medical fields and potential research applications.
Researchers from MBZUAI introduced RP-SAM2, a method to improve surgical instrument segmentation by refining point prompts for more stable results. RP-SAM2 uses a novel shift block and compound loss function to reduce sensitivity to point prompt placement, improving segmentation accuracy in data-constrained settings. Experiments on the Cataract1k and CaDIS datasets show that RP-SAM2 enhances segmentation accuracy and reduces variance compared to SAM2, with code available on GitHub.
Michael Yu Wang, Chair Professor and Founding Dean of the School of Engineering at Great Bay University, argues for combining "good old fashioned engineering" (GOFE) with learning-based approaches like LLMs for robot skill acquisition, particularly in manipulation. He suggests a modular framework that integrates engineering principles with learning, drawing inspiration from human hand-eye coordination and tactile perception. Wang emphasizes the need to address engineering features of robot tactile sensors, such as spatial and temporal resolutions, to achieve human-like robot manipulation skills. Why it matters: This perspective highlights the importance of hybrid approaches combining traditional engineering with modern AI for advancing robotics, especially in complex manipulation tasks relevant to industries in the GCC region.
KAUST's Workshop Core Lab has upgraded its scientific glassblowing workshop with advanced equipment like precision lathes and coating machines. The facility produces bespoke scientific glass equipment for KAUST researchers using borosilicate and quartz glass. Senior glassblowers Ernest Neil Davison and Emilio Harina create intricate designs from prototype sketches. Why it matters: This enhances KAUST's research capabilities by providing high-quality, specialized glassware that Davison claims rivals that of other top universities globally.
MBZUAI Professor Sami Haddadin and his team developed a new framework called Tactile Skills to teach robots manual skills through touch and trial and error. This framework aims to address the gap in robots' ability to learn basic physical tasks compared to AI's advancements in language and image generation. The research, published in Nature Machine Intelligence, focuses on enabling robots to perform manipulation skills at industrial levels with low energy and compute demands. Why it matters: This research could lead to robots capable of performing household maintenance, industrial tasks, and even assisting in medical or rehabilitation settings, potentially solving labor shortages in various sectors in the region and beyond.
MBZUAI researchers have developed "Tactile Skills," a new embodied AI framework enabling robots to rapidly learn complex tactile tasks. The framework combines expert process knowledge with reusable tactile control and adaptation components, reducing reliance on extensive datasets. Tested on 28 industrial tasks, the robots achieved nearly 100% success, demonstrating adaptability to changing conditions. Why it matters: This breakthrough offers a practical and scalable approach to robotic automation, potentially transforming robots into adaptable assistants across diverse industries in the GCC.