The rising rate of preprints and publications, combined with persistent inadequate reporting practices and problems with study design and execution, have strained the traditional peer review system. Automated screening tools could potentially enhance peer review by helping authors, journal editors, and reviewers to identify beneficial practices and common problems in preprints or submitted manuscripts. Tools can screen many papers quickly, and may be particularly helpful in assessing compliance with journal policies and with straightforward items in reporting guidelines. However, existing tools cannot understand or interpret the paper in the context of the scientific literature. Tools cannot yet determine whether the methods used are suitable to answer the research question, or whether the data support the authors’ conclusions. Editors and peer reviewers are essential for assessing journal fit and the overall quality of a paper, including the experimental design, the soundness of the study’s conclusions, potential impact and innovation. Automated screening tools cannot replace peer review, but may aid authors, reviewers, and editors in improving scientific papers. Strategies for responsible use of automated tools in peer review may include setting performance criteria for tools, transparently reporting tool performance and use, and training users to interpret reports.
Background: In this study, we explored whether the proposed short-echo-time magnitude (setMag) image derived from quantitative susceptibility mapping (QSM) could resemble NM-MRI image in substantia nigra (SN), by quantitatively comparing the spatial similarity and diagnosis performances for Parkinson's disease (PD). Methods: QSM and NM-MRI were performed in 18 PD patients and 15 healthy controls (HCs). The setMag images were calculated using the short-echo-time magnitude images. Bilateral hyperintensity areas of SN (SN hyper) were manually segmented on setMag and NM-MRI images by two raters in a blinded manner. The inter-rater reliability was evaluated by the intraclass correlation coefficients (ICC) and the Dice similarity coefficient (DSC). Then the intermodality (i.e. setMag and NM-MRI) spatial similarity was quantitatively assessed using DSC and volume of the consensual voxels identified by both of two raters. The performances of mean SN hyper volume for PD diagnosis on setMag and NM-MRI images were evaluated using receiver operating characteristic (ROC) analysis. Results: The SN hyper segmented by two raters showed substantial to excellent inter-rater reliability for both setMag and NM-MRI images. The DSCs of SN hyper between setMag and NM-MRI images showed substantial to excellent voxel-wise overlap in HCs (0.80~0.83) and PD (0.73~0.76), and no significant difference was found between the SN hyper volumes of setMag and NM-MRI images in either HCs or PD (p > 0.05). The mean SN hyper volume was significantly decreased in PD patients in comparison with HCs on both setMag images (77.61 mm 3 vs 95.99 mm 3 , p < 0.0001) and NM-MRI images (79.06 mm 3 vs 96.00 mm 3 , p < 0.0001). Areas under the curve (AUCs) of mean SN hyper volume for PD diagnosis were 0.904 on setMag and 0.906 on NM-MRI images. No significant difference was found between the two curves (p = 0.96). Conclusions: SN hyper on setMag derived from QSM demonstrated substantial spatial overlap with that on NM-MRI and provided comparable PD diagnostic performance, providing a new QSM-based multi-contrast imaging strategy for future PD studies.
This paper presented a novel complex network with one-way ANOVA F-test feature selection to diagnose early-stage Parkinson’s disease (PD) on quantitative susceptibility mapping (QSM). Experimental results on QSM images of 30 early-stage PD patients and 27 healthy controls (HC) proved that the F-test feature selection scheme was effective and achieved good classification results. The accuracy, AUC, sensitivity and specificity of our method were 0.96, 0.97, 0.99 and 0.95, respectively, which were improved by 15%, 4%, 29% and 2%, respectively by comparison with the commonly used region of interest (ROI) based method. Meanwhile, according to the feature importance, the potential brain regions affected by PD were arranged orderly. The affected regions were distributed as follows: 61% of them are located in right hemisphere and 39% in the left hemisphere. Particularly, frontal lobe, parietal lobe, temporal lobe and occipital lobe accounted for 24%, 20%, 5% and 14%, respectively, and striatum and the dorsal thalamus accounted for 16%. It concludes that the complex network with one-way ANOVA F-test feature selection can greatly improve the diagnostic performance of early-stage PD based on QSM, as well as provide a new way to study the effect of PD on brain in the future.
Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. Further analysis reveals that these graphical integrity issues are significantly more prevalent in some research fields, such as psychology and computer science, and some regions of the globe. Additionally, we find no temporal and seniority trends in violations. Finally, apart from openly releasing our large annotated dataset and method, we discuss how computational research integrity could be part of peer-review and the publication processes.
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