Abstract Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction at both lesion and patient level from invasive coronary angiography. AngioGraphCAD is evaluated across two clinical cohorts at the lesion level and one datatset at the patient level, achieving superior performance compared to clinical measures. This is the first study that highlights the importance of geometry information in advancing future events prediction from invasive coronary angiography. Given the significance of the clinical question and the innovative nature of the proposed methodology, this work could pave the way for the development of an AI framework fueled by patient-specific data in cardiology, potentially revolutionizing personalized decision-making in managing coronary artery diseases for individual patients.

This paper presents a neurosymbolic framework to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described using temporal logic formulas such as Bounded Linear Temporal Logic (BLTL) and co-safe LTL (scLTL) to capture complex tasks. The proposed framework trains Neural Network (NN)-based planners that enjoy strong correctness guarantees when applying to unseen tasks, i.e., the exact task (including workspace, temporal logic formula, and errors in the dynamical models of the robot) is not available during the training of NNs. Our approach to achieving theoretical guarantees and computational efficiency is based on two insights. First, we incorporate a symbolic model into the training of NNs such that the resulting NN-based planner inherits the interpretability and correctness guarantees of the symbolic model. Moreover, the symbolic model serves as a discrete “memory”, which is necessary for satisfying temporal logic formulas. Second, we train a library of neural networks offline and combine a subset of the trained NNs into a single NN-based planner at runtime when a task is revealed. In particular, we develop a novel constrained NN training procedure, named formal NN training, to enforce that each neural network in the library represents a “symbol” in the symbolic model. As a result, our neurosymbolic framework enjoys the scalability and flexibility benefits of machine learning and inherits the provable guarantees from control-theoretic and formal-methods techniques. We demonstrate the effectiveness of our framework in both simulations and on an actual robotic vehicle and show that our framework can generalize to unseen tasks where state-of-the-art meta-reinforcement learning techniques fail.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Heterogeneous microprocessors integrate a CPU and GPU on the same chip, providing fast CPU-GPU communication and enabling cores to compute on data “in place.” This permits exploiting a finer granularity of parallelism on the integrated GPUs, and enables the use of GPUs for accelerating more complex and irregular codes. One challenge, however, is exposing enough parallelism such that both the CPU and GPU are effectively utilized to achieve maximum gain. In this article, we propose exploiting nested parallelism for integrated CPU-GPU chips. We look for loop structures in which one or more regular data parallel loops are nested within a parallel outer loop that can contain irregular code (e.g., with control divergence). By scheduling the outer loop on multiple CPU cores, multiple dynamic instances of the inner regular loop(s) can be scheduled on the GPU cores. This boosts GPU utilization and parallelizes the outer loop. We find that such nested MIMD-SIMD parallelization provides greater levels of parallelism for integrated CPU-GPU chips, and additionally there is ample opportunity to perform such parallelization in OpenMP programs. Our results show nested MIMD-SIMD parallelization provides a 16.1x and 8.67x speedup over sequential execution on a simulator and a physical machine, respectively. Our technique beats CPU-only parallelization by 4.13x and 2.40x, respectively, and GPU-only parallelization by 2.74x and 2.26x, respectively. Compared to the next-best scheme (either CPU- or GPU-only parallelization) per benchmark, our approach provides a 1.46x and 1.23x speedup for the simulator and physical machine, respectively.

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial states such that a robot trajectory starting from these initial states is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of safe initial states. To mathematically model the imaging function, that maps the robot position to the LiDAR image, we introduce the notion of imaging-adapted partitions of the workspace in which the imaging function is guaranteed to be affine. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) non-linearity, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space of feasible ReLU assignments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with the increasing complexity of the neural network controller.

We propose an efficient multi-robot motion planning algorithm for missions captured by linear temporal logic (LTL) specifications, in the presence of bounded disturbances and denial-of-service (DoS) attacks against the communication between robots and base stations. Given an LTL formula Ψ, our goal is to construct robot trajectories, and associated control strategies, to satisfy Ψ and continuously establish communication paths between robots and base stations despite the DoS attacks and the disturbances on the robot states. Our approach combines and extends results from robust control and efficient motion planning via satisfiability modulo convex programming (SMC). We first compute a feedback controller that rejects the disturbance together with a perturbation of the DoS-free workspace that accounts for the worst-case disturbance scenario. On the perturbed workspace, we formulate the planning problem as a feasibility problem over Boolean and convex constraints, respectively capturing the DoS-resilient mission constraints and the constraints on the nominal, disturbance-free, robot dynamics. Numerical results show the effectiveness of our algorithm in providing DoS-resilient plans that are robust to disturbances and support the execution of complex missions.

OBJECTIVE: Using numeric laboratory data and administrative data from hospital electronic health record (EHR) systems, to develop an inpatient mortality predictive model. METHODS: Using EHR data of 1,428,824 adult discharges from 70 hospitals in 2006-2007, we developed the Acute Laboratory Risk of Mortality Score (ALaRMS) using age, gender, and initial laboratory values on admission as candidate variables. We then added administrative variables using the Agency for Healthcare Research and Quality (AHRQ)'s clinical classification software (CCS) and comorbidity software (CS) as disease classification tools. We validated the model using 770,523 discharges in 2008. RESULTS: Mortality predictors with ORs >2.00 included age, deranged albumin, arterial pH, bands, blood urea nitrogen, oxygen partial pressure, platelets, pro-brain natriuretic peptide, troponin I, and white blood cell counts. The ALaRMS model c-statistic was 0.87. Adding the CCS and CS variables increased the c-statistic to 0.91. The relative contributions were 69% (ALaRMS), 25% (CCS), and 6% (CS). Furthermore, the integrated discrimination improvement statistic demonstrated a 127% (95% CI 122% to 133%) overall improvement when ALaRMS was added to CCS and CS variables. In contrast, only a 22% (CI 19% to 25%) improvement was seen when CCS and CS variables were added to ALaRMS. CONCLUSIONS: EHR data can generate clinically plausible mortality predictive models with excellent discrimination. ALaRMS uses automated laboratory data widely available on admission, providing opportunities to aid real-time decision support. Models that incorporate laboratory and AHRQ's CCS and CS variables have utility for risk adjustment in retrospective outcome studies.