Deep learning method for aortic root detection

Background: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior. Methods: A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance. Results: The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. Conclusions: From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentationThis work was partially financed by Consellería de Cultura, Educación e Universidade (reference 2019–2021, ED431C 2018/19)S

Barrier height prediction by machine learning correction of semiempirical calculations

Different machine learning (ML) models are proposed in the present work to predict DFT-quality barrier heights (BHs) from semiempirical quantum-mechanical (SQM) calculations. The ML models include multi-task deep neural network, gradient boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors (MAEs) are similar or slightly better than previous models considering the same number of data points. Unlike other ML models employed to predict BHs, entropic effects are included, which enables the prediction of rate constants at different temperatures. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in Combustion Chemistry or in Astrochemistry. Finally, our results show that 70% of the bespoke predictors are amongst the features with the highest impact on model output. This custom-made set of predictors could be employed by future delta-ML models to improve the quantitative prediction of other reaction properties

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

The purpose of this study was to evaluate the diagnostic performance of a computer-aided diagnosis (CAD) system, on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images, by using two different MDCT scanners. The computerized scheme was based on the iris filter. We have collected CT cases of patients with pulmonary nodules. We have included in the study one hundred and thirty-two calcified and noncalcified nodules, measuring 4-30 mm in diameter. CT examinations were performed by using two different equipments: a CT scanner (SOMATOM Emotion 6), and a dual-source computed tomography system (SOMATOM Definition) (Siemens Medical System, Forchheim, Germany), with the following parameters: collimation, 6x1.0mm (Emotion 6); and 64×0.6mm (Definition); 100-130 kV; 70-110 mAs. Data were reconstructed with a slice thickness of 1.25mm (Emotion 6) and 1mm (Definition). True positive cases were determined by an independent interpretation of the study by three experienced chest radiologists, the panel decision being used as the reference standard. Free-response Receiver Operating Characteristic curves, sensitivity and number of false-positive per scan, were calculated. Our CAD scheme, for the test set of the study, yielded a sensitivity of 80%, with an average of 5.2 FPs per examination. At an average false positive rate of 9 per scan, our CAD scheme achieved sensitivities of 94% for all nodules, 94.5% for solid, 80% for non-solid, 84% for spiculated, and 97% for non-spiculated nodules. These encouraging results suggest that our CAD system, advocated as a second reader, may help radiologists in the detection of lung nodules in MDCTThis work has been partially supported by the Xunta de Galicia (expte. nº PGIDIT06BTF20802PR), and by the FIS (expte. nº PI060058) and (expte. nº PI080072)S

Digital synthesis of microcalcifications on digital mammograms

We propose a model to simulate clustered microcalcifications on digital mammograms. The simulation model is based on the gray-level, size and number of microcalcifications per cluster. All the parameters describing the individual microcalcifications and clusters were randomly sampled within a wide range of values, the exception being the center of the cluster; this was interactively positioned to ensure the location of all the microcalcifications inside the breast. Subsequently, a database of clustered microcalcifications was created. These clusters of microcalcifications from this database were tested from indistinguishability from real ones. Two radiologists and one physicist were asked to indicate wether the microcalcifications were either real or simulated. Results ( χ 2 test) indicate that there was not statistical and significant difference between real and simulated clustered microcalcifications

AutoMeKin2021: an open-source program for automated reaction discovery

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKinThis work was partially supported by the Ministerio de Ciencia e Innovacion (Grant # PID2019-107307RB-I00). G. L. B. gratefully acknowledges support from the National Science Foundation under grant No. 1763652S