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Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria misses many acute occlusion myocardial infarctions (OMI) and triggers unnecessary acute angiographies. Here, we present a trained and validated deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model has a C-statistic of ≥0.95 for OMI and ≥0.87 for non-OMI infarctions and can localize culprit lesions in the three main coronary branches, which can guide the angiographer. Performance is similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for the identification and localization of OMI has the potential to shorten the time to reperfusion of an acute coronary occlusion and save resources. Because human oversight of OMI detection on the ECG is limited, randomized clinical trials with patient-relevant outcomes are warranted.

Picking manipulators are task specific robots, with fewer degrees of freedom compared to general-purpose manipulators, and are heavily used in industry. The efficiency of the picking robots is highly dependent on the path planning solution, which is commonly based on sampling-based multi-query methods. The planner is robustly able to solve the problem, but its heavy use of collision-detection limits the planning capabilities for online use. We approach this problem by presenting a novel implicit obstacle representation for path planning, a neural signed configuration distance function (nSCDF), which allows us to form collision-free balls in the configuration space. We use the ball representation to re-formulate a state of the art multi-query path planner, i.e., instead of points, we use balls in the graph. Our planner returns a collision-free corridor, which allows us to use convex programming to produce optimized paths. From our numerical experiments, we observe that our planner produces paths that are close to those from an asymptotically optimal path planner, in significantly less time.

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major challenges. Here we present CODE-II, a large-scale real-world dataset of 2,735,269 12-lead ECGs from 2,093,807 adult patients collected by the Telehealth Network of Minas Gerais (TNMG), Brazil. Each exam was annotated using standardized diagnostic criteria and reviewed by cardiologists. A defining feature of CODE-II is a set of 66 clinically meaningful diagnostic classes, developed with cardiologist input and routinely used in telehealth practice. We additionally provide an openly available subset: CODE-II-open, a public subset of 15,000 patients, and the CODE-II-test, a non-overlapping set of 8,475 exams reviewed by multiple cardiologists for blinded evaluation. A neural network pre-trained on CODE-II achieved superior transfer performance on external benchmarks (PTB-XL and CPSC 2018) and outperformed alternatives trained on larger datasets.

The current alarming loss of biodiversity requires improved techniques for mitigation. The ability to predict the expected effect of interventions affecting biodiversity ahead in time, with confidence limits, will therefore be central for efficient policy making in the future. This work develops new techniques for modeling of the biodiversity response that follow an abrupt intervention, like clear-cutting of forest. The modeling quantifies the dynamics of biodiversity metrics like species richness and geometric mean abundance with differential equations, where the unknown parameters are estimated with prediction error system identification techniques. Since the data is scarce in time per clear-cutting location, a minimum amount of only four parameters is estimated for all locations in a geographical region. The parameters are estimated by aggregation over multiple clear-cutting locations to obtain an improved statistical significance based on an assumption of independence between locations. Since the associated loss function is highly asymmetric around the estimate, the bootstrap technique is applied to the data to obtain an asymmetric confidence region that is statistically correct. The method is applied to data from the Swedish forest inventory, and shown to perform as desired. From a biodiversity perspective the results are in line with previous knowledge. The methodology also leads to the formulation of a number of new research questions. A specific advantage is that the data compression of the algorithm into a low order model allows statistically confident estimates to be obtained also from very small sets of data, here only a few tens of samples per region. This allows assessment of data sets that are far smaller and below the operating range of machine learning algorithms.

BACKGROUND: Chagas disease (ChD), a neglected cardiovascular condition, affects 7.5 to 10.5 million people worldwide. Opportunistic screening during routine electrocardiography may allow earlier detection of unrecognized infections, enabling timely antiparasitic therapy or optimized cardiac care. This study prospectively evaluated the feasibility and diagnostic accuracy of an artificial intelligence (AI)-enabled ECG model enriched with 3 epidemiological questions (AI-ECG-EPI model) as an opportunistic screening for ChD in endemic regions of Brazil. METHODS: We conducted a prospective study embedded in the Telehealth Network of Minas Gerais, Brazil, across 107 municipalities in hyperendemic and endemic regions. From October 2023 to September 2024, adults undergoing routine tele-ECGs were eligible. The primary exposure was the output of the AI-ECG-EPI model: all positive individuals and a 3:1 sample of negative individuals were invited for serology (index test-dependent sampling), the reference standard. Weighted analyses (inverse probability) accounted for this sampling design. Diagnostic metrics included sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristics curve, and area under the precision recall curve. The AI-ECG-EPI model was compared with both an epidemiological questions model and an AI ECG-only model. RESULTS: Among 75 779 eligible ECGs, the AI-ECG-EPI model was available for 59 154 individuals; 6812 (11.5%) screened positive. A total of 7804 selected were invited for serology, and 3509 completed testing (2517 positive; 992 negative). ChD was confirmed in 36.8% of positive and 9.3% of negative individuals. The weighted prevalence of ChD was 12.2%. Sensitivity was 34.3%, specificity 91.6%, harmonic mean of precision and recall score 0.52, and diagnostic odds ratio 5.69. The AI-ECG-EPI model showed higher discrimination than both the epidemiological questions model and AI ECG-only model (area under the receiver operating characteristics curve, 77.6% versus 70.0% and 64.5%, respectively; P<0.001). Precision recall curves showed higher precision across most recall levels. Exploratory analysis suggested improved risk reclassification (net reclassification improvement, 0.503). Performance was higher in women and in individuals with Chagas cardiomyopathy. CONCLUSIONS: In this community-based study, an AI-ECG-EPI model integrated into a public telecardiology network enabled feasible opportunistic screening for ChD in primary care. It showed acceptable diagnostic accuracy, with higher discrimination than epidemiological questions and AI ECG-only models, and identified many previously undiagnosed patients in endemic regions, supporting its potential role in scalable screening strategies.REGISTRATION: URL: ; Unique identifier: NCT02646943.

Industrial manipulators typically operate in cluttered environments, where safe motion planning is critical. However, model uncertainties further complicate this task, which leads to conservative speed limits to reduce the influence of disturbances. Hence, there is a need for control methods that can guarantee safe motions that are executed fast. We address this by suggesting a novel model predictive control (MPC) solution for manipulators, where our two main components are a robust tube MPC and a corridor planning algorithm to obtain collision-free motion. Our solution results in a convex MPC formulation, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a six DOF industrial robot operating in cluttered environments with uncertain model parameters. We outperform benchmark methods by tolerating higher levels of model uncertainty while achieving faster motion.

Robotic pick and place solutions in workspaces shared with humans provide a challenge in obtaining high throughput while at the same time guaranteeing safety. A common simplification in motion planning is to decouple the spatial and temporal dimension, which works well in static settings. However, in dynamic environments this is no longer true since these two dimensions now exhibit strong dependencies. Here, we develop a model predictive control (MPC) solution capable of performing the required joint spatial and temporal planning. We use a recent construction, a configuration space corridor capable of representing a collision-free region in the configuration space. We show how the predicted future movement of the obstacles can be incorporated in the planning, by rapidly reshaping this corridor along the prediction horizon. We also propose a novel corridor planner that guides our MPC to the goal in a safe and efficient manner. From the numerical experiments, we observe higher success rates compared to decoupled motion planning, while also being faster and safer. Finally, we present statistics of our computation time, demonstrating real-time capability at a control rate of 20 Hz.