In percutaneous pelvic trauma surgery, accurate placement of Kirschner wires (K-wires) is crucial to ensure effective fracture fixation and avoid complications due to breaching the cortical bone along an unsuitable trajectory. Surgical navigation via mixed reality (MR) can help achieve precise wire placement in a low-profile form factor. Current approaches in this domain are as yet unsuitable for real-world deployment because they fall short of guaranteeing accurate visual feedback due to uncontrolled bending of the wire. To ensure accurate feedback, we introduce StraightTrack, an MR navigation system designed for percutaneous wire placement in complex anatomy. StraightTrack features a marker body equipped with a rigid access cannula that mitigates wire bending due to interactions with soft tissue and a covered bony surface. Integrated with an Optical See-Through Head-Mounted Display (OST HMD) capable of tracking the cannula body, StraightTrack offers real-time 3D visualization and guidance without external trackers, which are prone to losing line-of-sight. In phantom experiments with two experienced orthopedic surgeons, StraightTrack improves wire placement accuracy, achieving the ideal trajectory within $5.26 \pm 2.29$ mm and $2.88 \pm 1.49$ degree, compared to over 12.08 mm and 4.07 degree for comparable methods. As MR navigation systems continue to mature, StraightTrack realizes their potential for internal fracture fixation and other percutaneous orthopedic procedures.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided.

Richard Ljuhar, CEO and Co-founder of IB Lab, "the study confirms that for the vast majority of analyzed images the AI based method comes to essentially the same measures as the ones obtained by trained experts – just much faster and thus considerably cheaper." In detail, the team UTSW Medical Center used radiological images of 256 hips. From each image 6 measurements were taken: Lateral center-edge angle, caput-collum-diaphyseal angle, pelvic obliquity, Tönnis angle, Sharp's angle and femoral head coverage, either by HIPPO or by three trained experts. When comparing the results obtained by either method, they showed good to excellent correlations, ranging in average from 0.6 to 0.98 (with 1 being identical results). Even better results were achieved when the clinically most widely used measurements (lateral center-edge and Tönnis angle) were compared. Here, the correlation was within 0.71 to 0.86 and 0.82 to 0.90, respectively.

Dr. Boris Kovalenko, an orthopedic surgeon, stands June 30 with a robot that assists with knee replacements at St. Mary's Regional Medical Center in Lewiston. A robot can probably help with that. Robotics-assisted technology is becoming more common in operating rooms across Maine, with St. Mary's Regional Medical Center in Lewiston recently joining the list of hospitals employing the new methods. "It kind of is a fundamentally different way of doing a knee replacement," Dr. Boris Kovalenko, an orthopedic surgeon at St. Mary's, said earlier this summer. Kovalenko was standing in an operating room at St. Mary's, holding what looked more like a tricked-out hot glue gun attached to a rolling TV cart than an advanced piece of surgical technology.

Stuart Russell, a leading artificial-intelligence (AI) researcher at the University of California, Berkeley, and author of the book Human Compatible (Penguin Random House, October 2019), sits down with McKinsey Global Institute chairman James Manyika to discuss our future as AI transforms our world. In this broad conversation, they explore the immense benefits ahead and what our role will be as AI becomes more pervasive. They also delve into potential challenges we may face with our current approach to AI, and how we can redefine AI to ensure it helps humanity achieve its full potential. James Manyika: When you look at the AI field today and you see all these announcements and breakthroughs, what excites you the most? Stuart Russell: With today's technology, delivering high-quality education to everybody on Earth is just the beginning. Even fairly simple AI tutoring tools have been shown to be very effective. So that can only get better if we figure out how to roll it out to the people who really need it.