Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF.

The popular view is that McIlroy suffers from a build-up of mental scar tissue, first triggered by that Augusta capitulation in 2011. Although he quickly started collecting his quartet of majors, post 2014 there have been many instances where further setbacks have added to the malaise. Most recent was last year's US Open where he was beaten by to the title by Bryson DeChambeau despite leading by two with five holes to play. "It is possible for people to build something up in their heads and say this is the one that I struggle with because previously something bad happened," chartered psychologist Stephen Smith told BBC Sport. "That memory hasn't been processed properly out of what we call the short term executive memory into your long term memory. Smith has worked with athletes across a wide range of sports and says bad memories can influence physical performance. "The wire that carries the performance movement message from your brain to your body gets that wonderful swing going in a connected way," he said. "And that's why you make more mistakes on that hole or on that course or in that tournament, because you're just literally not able to send the signal to the muscles.

Background: Late Gadolinium Enhancement (LGE) imaging is the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV) LGE extent predicting major adverse cardiac events (MACE). Despite its importance, routine LGE-based LV scar quantification is hindered by labor-intensive manual segmentation and inter-observer variability. Methods: We propose ScarNet, a hybrid model combining a transformer-based encoder from the Medical Segment Anything Model (MedSAM) with a convolution-based U-Net decoder, enhanced by tailored attention blocks. ScarNet was trained on 552 ischemic cardiomyopathy patients with expert segmentations of myocardial and scar boundaries and tested on 184 separate patients. Results: ScarNet achieved robust scar segmentation in 184 test patients, yielding a median Dice score of 0.912 (IQR: 0.863--0.944), significantly outperforming MedSAM (median Dice = 0.046, IQR: 0.043--0.047) and nnU-Net (median Dice = 0.638, IQR: 0.604--0.661). ScarNet demonstrated lower bias (-0.63%) and coefficient of variation (4.3%) compared to MedSAM (bias: -13.31%, CoV: 130.3%) and nnU-Net (bias: -2.46%, CoV: 20.3%). In Monte Carlo simulations with noise perturbations, ScarNet achieved significantly higher scar Dice (0.892 \pm 0.053, CoV = 5.9%) than MedSAM (0.048 \pm 0.112, CoV = 233.3%) and nnU-Net (0.615 \pm 0.537, CoV = 28.7%). Conclusion: ScarNet outperformed MedSAM and nnU-Net in accurately segmenting myocardial and scar boundaries in LGE images. The model exhibited robust performance across diverse image qualities and scar patterns.

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis and treatment planning, e.g. allowing surgical revascularization, or to determine the risk of adverse cardiovascular events in the future. Fine-grained analysis of the myocardium and its surrounding anatomical structures can be performed by combining the information obtained from complementary medical imaging techniques. In this work, we use late gadolinium enhanced (LGE) magnetic resonance (MR), T2-weighted (T2) MR and balanced steady-state free precession (bSSFP) cine MR in order to semantically segment the left and right ventricle, healthy and scarred myocardial tissue, as well as edema. To this end, we propose the Multi-Sequence Cascading Refinement CNN (MS-CaRe-CNN), a 2-stage CNN cascade that receives multi-sequence data and generates predictions of the anatomical structures of interest without considering tissue viability at Stage 1. The prediction of Stage 1 is then further refined in Stage 2, where the model additionally distinguishes myocardial tissue based on viability, i.e. healthy, scarred and edema regions. Our proposed method is set up as a 5-fold ensemble and semantically segments scar tissue achieving 62.31% DSC and 82.65% precision, as well as 63.78% DSC and 87.69% precision for the combined scar and edema region. These promising results for such small and challenging structures confirm that MS-CaRe-CNN is well-suited to generate semantic segmentations to assess the viability of myocardial tissue, enabling downstream tasks like personalized therapy planning.

By employing artificial intelligence (AI) and robotics to formulate therapeutic proteins, a team led by Rutgers researchers has successfully stabilized an enzyme able to degrade scar tissue resulting from spinal cord injuries and promote tissue regeneration. The study, recently published in Advanced Healthcare Materials, details the team's ground-breaking stabilization of the enzyme Chondroitinase ABC, (ChABC) offering new hope for patients coping with spinal cord injuries. "This study represents one of the first times artificial intelligence and robotics have been used to formulate highly sensitive therapeutic proteins and extend their activity by such a large amount. It's a major scientific achievement," says Adam Gormley, the project's principal investigator and an assistant professor of biomedical engineering at Rutgers School of Engineering (SOE) at Rutgers University-New Brunswick. Gormley expresses that his research is also motivated, in part, by a personal connection to spinal cord injury.

When the present is scary, the future can be virtually unthinkable. But it's at times of great change and uncertainty--and 2020 surely qualifies--that it is most important to try to look ahead, to think about how decisions made right now can reverberate. This year, Future Tense Fiction--a partnership of Future Tense and Arizona State University's Center for Science and the Imagination--published 12 stories that take very different looks at the years to come. In the case of Max Barry's "It Came From Cruden Farm," that future is very near--it's set on Inauguration Day 2021, when a new president learns that the U.S. government has custody of an alien, and it's complicated. Other futures are more distant; as part of our package of three stories on artificial intelligence and governance, "The State Machine," by Yudhanjaya Wijeratne, follows a graduate student trying to learn about the very earliest days of his country being run by A.I. Tobias S. Buckell's "Scar Tissue," Holli Mintzer's "Legal Salvage," and Karl Schroeder's "The Suicide of Our Troubles" all grapple, in very different ways, with legal rights for nonhumans.

The author of Robots Are People Too responds to Tobias Buckell's "Scar Tissue." "Scar Tissue" is an emotionally resonant tale about healing through robot rearing--but it was only half of the story about Rob's creator, Advent Robotics. What we read was the part Cory could see: Rob as a growing child, nurtured by Cory. But in the story, Tobias Buckell offers us a little hint about everything happening at Advent: "Every time [Advent's robots] get on that charger, they're not just powering up their onboard battery--they're taking in their experiences and uploading data to our servers to have it examined." That's the part that worries me, as artificial intelligence applications may be able to leverage the data to manipulate Cory and other people--just as technology, PR, and marketing companies try to do in our lives today.

Doctors have used a robot to perform extremely delicate surgical operations on breast cancer patients in the first human trial of the technology. Eight women had the robot-assisted procedure at Maastricht University Medical Center, in the Netherlands, to alleviate a common complication of breast cancer surgery. The robot helped a specialist surgeon divert thread-like lymphatic vessels, as narrow as 0.3mm, around scar tissue in the patients' armpits, and connect them to nearby blood vessels. The operation, which requires immense care and precision, is offered to some breast cancer patients to reduce swelling in the arms that builds up when the lymphatic system cannot drain properly. Because the vessels are so small, surgeons need exceptionally steady hands to perform the operation well.