Neurological outcome prediction in patients with subarachnoid hemorrhage using a model based on initial CT scan, clinical data and neural networks [Abstract]

Oral e-Poster Presentations - Booth 1: Vascular A (Aneurysms), September 25, 2023, 1:00 PM - 2:30 PM Background: Subarachnoid hemorrhage(SAH) entails high morbidity and mortality. Several risk factors have been identified as mortality and functional outcome estimators. Artificial intelligence(AI) enables handling high-dimensional and complex data. Neural networks (NN), an automated machine learning technique, can be trained with images and/or data to perform accurate predictions. This study aims to predict the functional outcome of SAH patients at three months using a NN-based algorithm that processes initial CT scan images and clinical features. Methods: Clinical features and CT scans of a multicentric retrospective cohort of SAH patients were analyzed. AUCMEDI, an open-source Python library, was used to create and train two different NNs: one based solely on images and the other incorporating clinical features (age and WFNS). The output variable was a dichotomized modified Rankin scale at 3 months(mRS): Good Outcome=mRS<4; Bad Outcome= mRS<4. The initial dataset was randomly split into training, validation, and test cohorts at a ratio of 70%-10%-20%. Results: Images and data from 219 patients were processed. 52.5% were female patients with a mean age of 57. 18.3% were idiopathic SAH. Median WFNS on admission was 2, and mortality was 28.8%. 54.3% of patients presented a good outcome at 3 months follow-up. Predicting neurological outcome, the model exclusively based on CT scan images (Accuracy=86%, F1=86% and AUC=0.89) outperformed the one based on images and clinical data (Accuracy=79%, F1=78% and AUC=0.87). Explainable Artificial Intelligence maps were built to highlight the areas the algorithm accounts on the CT scan in order to classify patients. Conclusions: Modern image processing techniques based on AI and NN make possible to predict neurological outcome in SAH patients with high accuracy using CT scan images as the only input. Models might be optimized by including more data and patients, therefore improving their performance on tasks beyond the skills of conventional clinical knowledge

Weights of hypergraph model trained on normalised GTEx data

This file contains the weights of the Pytorch hypergraph model trained on normalised GTEx data </p

GTEx v8 metadata

The GTEx dataset is a public resource that has generated a broad collection of gene expression data collected from a diverse set of human tissues. Here we share the processed GTEx data used in Hypergraph factorisation for multi-tissue gene expression imputation (Vinas Torne et al., 2023). We processed the data following the GTEx eQTL discovery pipeline. If you use this data for your research, please cite the GTEx consortium paper: GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. DOI: 10.1126/science.aaz1776</p

Processed GTEx v8 data

The GTEx dataset is a public resource that has generated a broad collection of gene expression data collected from a diverse set of human tissues. Here we share the processed GTEx data used in Hypergraph factorisation for multi-tissue gene expression imputation (Vinas Torne et al., 2023). We processed the data following the GTEx eQTL discovery pipeline. If you use this data for your research, please cite the GTEx consortium paper: GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. DOI: 10.1126/science.aaz1776</p

Crustal structure beneath the Rif Cordillera, North Morocco, from the RIFSIS wide-angle reflection seismic experiment

The different geodynamic models proposed since the late 1990s to account for the complex evolution of the Gibraltar Arc System lack definite constraints on the crustal structure of the Rif orogen. Here we present the first well-resolved P-wave velocity crustal models of the Rif Cordillera and its southern continuation toward the Atlas made using controlled-source seismic data. Two 300+ km-long wide-angle reflection profiles crossed the Rif along NS and EW trends. The profiles recorded simultaneously five land explosions of 1Tn each using ~850 high frequency seismometers. The crustal structure revealed from 2-D forward modeling delineates a complex, laterally varying crustal structure below the Rif domains. The most surprising feature, seen on both profiles, is a ∼50 km deep crustal root localized beneath the External Rif. To the east, the crust thins rapidly by 20 km across the Nekkor fault, indicating that the fault is a crustal scale feature. On the NS profile the crust thins more gradually to 40 km thickness beneath Middle Atlas and 42 km beneath the Betics. These new seismic results are in overall agreement with regional trends of Bouguer gravity and are consistent with recent receiver function estimates of crustal thickness. The complex crustal structure of the Rif orogen in the Gibraltar Arc is a consequence of the Miocene collision between the Iberian and African plates. Both the abrupt change in crustal thickness at the Nekkor fault and the unexpectedly deep Rif crustal root can be attributed to interaction of the subducting Alboran slab with the North African passive margin at late Oligocene-early Miocene times

Informe sobre l'activitat del grup Nedas : IES Bernat Metge

Este documento no está publicadoSe recogen las actas de las sesiones del grupo Nedas. Este grupo reflexiona sobre como llevar a cabo nuevos entornos de aprendizaje para el alumnado de secundaria y desarrolla el proyecto europeo SCHOOL +.CataluñaES