CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types. Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained and validated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN was used to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From this dataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. The performance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification, lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation was done on an independent set of 500 patients from India. 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres: 184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derived lesion volumes showed a mean difference of 0·86 mL (95% CI -5·23 to 6·94) for intraparenchymal haemorrhage, 1·83 mL (-12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (-9·38 to 13·56) for perilesional oedema, and 0·07 mL (-1·00 to 1·13) for intraventricular haemorrhage. We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagic lesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of lesion burden and progression, with potential applications for personalised treatment strategies and clinical research in TBI. European Union 7th Framework Programme, Hannelore Kohl Stiftung, OneMind, NeuroTrauma Sciences, Integra Neurosciences, European Research Council Horizon 2020.

In this paper, we present a collection of aspect-oriented refactorings covering both the extraction of aspects from object-oriented legacy code and the subsequent tidying up of the resulting aspects. In some cases, this tidying up entails the replacement of the original implementation with a different, centralized design, made possible by modularization. The collection of refactorings includes the extraction of common code in various aspects into abstract superaspects. We review the traditional object-oriented code smells in the light of aspect-orientation and propose some new smells for the detection of crosscutting concerns. In addition, we propose a new code smell that is specific to aspects.

Context: Code smells are associated to poor design and programming style, which often degrades code quality and hampers code comprehensibility and maintainability. Goal: identify published studies that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the stated goal. Results: The SLR selected studies from July 2007 to September 2017, which analyzed the source code of open source software projects and several code smells. Based on evidence of 16 studies covered in this SLR, we conclude that 24 code smells are more influential in the occurrence of bugs relative to the remaining smells analyzed. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques, and procedures that should be applied to analyze the influence of the smells. Conclusions: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.

An increasing number of elderly patients are being affected by traumatic brain injury (TBI) and a significant proportion are on pre-hospital antithrombotic therapy for cardio- or cerebrovascular indications. We have quantified the impact of antiplatelet/anticoagulant (APAC) agents on radiological lesion progression in acute TBI, using a novel, semi-automated approach to volumetric lesion measurement, and explored the impact of use on clinical outcomes in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. We used a 1:1 propensity-matched cohort design, matching controls to APAC users based on demographics, baseline clinical status, pre-injury comorbidities, and injury severity. Subjects were selected from a pool of patients enrolled in CENTER-TBI with computed tomography (CT) scan at admission and repeated within 7 days of injury. We calculated absolute changes in volume of intraparenchymal, extra-axial, intraventricular, and total intracranial hemorrhage (ICH) between scans, and compared volume of hemorrhagic progression, proportion of patients with significant degree of progression (>25% of initial volume), proportion with new ICH on follow-up CT, as well as clinical course and outcomes. A total of 316 patients were included (158 APAC users; 158 controls). The mean volume of progression was significantly higher in the APAC group for extra-axial (3.1 vs. 1.3 mL, p = 0.01), but not intraparenchymal (3.8 vs. 4.6 mL, p = 0.65), intraventricular (0.2 vs. 0.0 mL, p = 0.79), or total intracranial hemorrhage (ICH; 7.0 vs. 6.0 mL, p = 0.08). More patients had significant hemorrhage growth (54.1 vs. 37.0%, p = 0.003) and delayed ICH (4 of 18 vs. none; p = 0.04) in the APAC group compared with controls, but this was not associated with differences in length of stay (LOS), rates of neurosurgical intervention, mortality or Glasgow Outcome Scale Extended (GOS-E) score at 6 months. Pre-injury use of antithrombotic agents was associated with greater expansion of extra-axial lesions, higher rates of significant hemorrhagic progression, and higher risk of delayed traumatic ICH, but this was not associated with worse clinical course or functional outcomes.

Guidelines for refactoring are meant to improve software systems internal quality and are widely acknowledged as among software's best practices. However, such guidelines remain mostly qualitative in nature. As a result, judgments on how to conduct refactoring processes remain mostly subjective and therefore non-automatable, prone to errors and unrepeatable. The detection of the Long Method code smell is an example. To address this problem, this paper proposes a technique to detect Long Method objectively and automatically, using a Binary Logistic Regression model calibrated by expert's knowledge. The results of an experiment illustrating the use of this technique are reported.

In this paper, we present a collection of well-known high-level concurrency patterns and mechanisms, coded in AspectJ. We discuss benefits of these implementations relative to plain Java implementations of the same concerns. We detect benefits from using AspectJ in all the cases presented, in the form of higher modularity, reuse, understandability and unpluggability. For most of the implementations, two alternatives can be used: one based on traditional pointcut interfaces and one based on annotations.