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Background:  With the widespread use of direct oral anticoagulants (DOACs), there is an urgent need for a rapid assay to exclude clinically relevant plasma levels. Accurate and rapid determination of DOAC levels would guide medical decision-making to (1) determine the potential contribution of the DOAC to spontaneous or trauma-induced hemorrhage; (2) identify appropriate candidates for reversal, or (3) optimize the timing of urgent surgery or intervention., Methods and Results:  The DOAC Dipstick test uses a disposable strip to identify factor Xa- or thrombin inhibitors in a urine sample. Based on the results of a systematic literature search followed by an analysis of a simple pooling of five retrieved clinical studies, the test strip has a high sensitivity and an acceptably high negative predictive value when compared with levels measured with liquid chromatography tandem mass spectrometry or calibrated chromogenic assays to reliably exclude plasma DOAC concentrations ≥30 ng/mL., Conclusion:  Based on these data, a simple algorithm is proposed to enhance medical decision-making in acute care indications useful primarily in hospitals not having readily available quantitative tests and 24/7. This algorithm not only determines DOAC exposure but also differentiates between factor Xa and thrombin inhibitors to better guide clinical management., Competing Interests: J.H. is general manager and founder of DOASENSE GmbH, J.D. is CEO of Qualiblood SA. All other authors declare no conflicts of interest., (Thieme. All rights reserved.)

Introduction: Pen-on-paper pain drawing are an easily administered self-reported measure that enables patients to report the spatial distribution of their pain. The digitalization of pain drawings has facilitated the extraction of quantitative metrics, such as pain extent and location. This study aimed to assess the reliability of pen-on-paper pain drawing analysis conducted by an automated pain-spot recognition algorithm using various scanning procedures., Methods: One hundred pain drawings, completed by patients experiencing somatic pain, were repeatedly scanned using diverse technologies and devices. Seven datasets were created, enabling reliability assessments including inter-device, inter-scanner, inter-mobile, inter-software, intra- and inter-operator. Subsequently, the automated pain-spot recognition algorithm estimated pain extent and location values for each digitized pain drawing. The relative reliability of pain extent analysis was determined using the intraclass correlation coefficient, while absolute reliability was evaluated through the standard error of measurement and minimum detectable change. The reliability of pain location analysis was computed using the Jaccard similarity index., Results: The reliability analysis of pain extent consistently yielded intraclass correlation coefficient values above 0.90 for all scanning procedures, with standard error of measurement ranging from 0.03% to 0.13% and minimum detectable change from 0.08% to 0.38%. The mean Jaccard index scores across all dataset comparisons exceeded 0.90., Conclusions: The analysis of pen-on-paper pain drawings demonstrated excellent reliability, suggesting that the automated pain-spot recognition algorithm is unaffected by scanning procedures. These findings support the algorithm's applicability in both research and clinical practice., Competing Interests: Disclosures and Acknowledgments No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks., Methods: We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1)., Results: In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s., Conclusion: In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline., (© 2023. The Author(s).)

Systematic reviews rely on identification of studies, initially through electronic searches yielding potentially thousands of studies, and then reviewer-led screening studies for inclusion. This standard method is time- and resource-intensive. We designed and applied an algorithm written in Python involving computer-aided identification of keywords within each paper for an exemplar systematic review of arm impairment after stroke. The standard method involved reading each abstract searching for these keywords. We compared the methods in terms of accuracy in identification of keywords, abstracts' eligibility, and time taken to make a decision about eligibility. For external validation, we adapted the algorithm for a different systematic review, and compared eligible studies using the algorithm with those included in that review. For the exemplar systematic review, the algorithm failed on 72 out of 2,789 documents retrieved (2.6%). Both methods identified the same 610 studies for inclusion. Based on a sample of 21 randomly selected abstracts, the standard screening took 1.58 ± 0.26 min per abstract. Computer output screening took 0.43 ± 0.14 min per abstract. The mean difference between the two methods was 1.15 min ( P  < 0.0001), saving 73% per abstract. For the other systematic review, use of the algorithm resulted in the same studies being identified. One study was excluded based on the interpretation of the comparison intervention. Our purpose-built software was an accurate and significantly time-saving method for identifying eligible abstracts for inclusion in systematic reviews. This novel method could be adapted for other systematic reviews in future for the benefit of authors, reviewers and editors., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

The amount of available scientific literature is increasing, and studies have proposed various methods for evaluating document-document similarity in order to cluster or classify documents for science mapping and knowledge discovery. In this paper, we propose hybrid methods for bibliographic coupling (BC) and linear evaluation of text or content similarity: We combined BC with BM25, Cosine, and PMRA to compare their performances with single methods in paper recommendation tasks using TREC Genomics Track 2005datasets. For paper recommendation, BC and text-based methods complement each other, and hybrid methods were better than single methods. The combinations of BC with BM25 and BC with Cosine performed better than BC with PMRA. The performances were best when the weights of BM25, Cosine, and PMRA were 0.025, 0.2, and 0.2, respectively, in hybrid methods. For paper recommendation, the combinations of BC with text-based methods were better than BC or text-based methods used alone. The choice of method should depend on the actual data and research needs. In the future, the underlying reasons for the differences in performance and the specific part or type of information they complement in text clustering or recommendation need to be examined.

In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering.

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design., Competing Interests: The author declares that he has no conflicts of interest., (Copyright © 2022 Xingping Li.)

Introduction: Participation from racial and ethnic minorities in clinical trials has been burdened by issues surrounding mistrust and access to healthcare. There is emerging use of machine learning (ML) in clinical trial recruitment and evaluation. However, for individuals from groups who are recipients of societal biases, utilisation of ML can lead to the creation and use of biased algorithms. To minimise bias, the design of equitable ML tools that advance health equity could be guided by community engagement processes. The Howard University Partnership with the National Institutes of Health for Equitable Clinical Trial Participation for Racial/Ethnic Communities Underrepresented in Research (HoPeNET) seeks to create an ML-based infrastructure from community advisory board (CAB) experiences to enhance participation of African-Americans/Blacks in clinical trials., Methods and Analysis: This triphased cross-sectional study (24 months, n=56) will create a CAB of community members and research investigators. The three phases of the study include: (1) identification of perceived barriers/facilitators to clinical trial engagement through qualitative/quantitative methods and systems-based model building participation; (2) operation of CAB meetings and (3) development of a predictive ML tool and outcome evaluation. Identified predictors from the participant-derived systems-based map will be used for the ML tool development., Ethics and Dissemination: We anticipate minimum risk for participants. Institutional review board approval and informed consent has been obtained and patient confidentiality ensured., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as "single AI") to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called "focus length" (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents.

Background: Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal., Methods: In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal., Results: The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively., Conclusions: The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Zemblys et al. (Behavior Research Methods, 51(2), 840-864, 2019) reported on a method for the classification of eye-movements ("gazeNet"). I have found three errors and two problems with that paper that are explained herein. Error 1: The gazeNet classification method was built assuming that a hand-scored dataset from Lund University was all collected at 500 Hz, but in fact, six of the 34 recording files were actually collected at 200 Hz. Of the six datasets that were used as the training set for the gazeNet algorithm, two were actually collected at 200 Hz. Problem 1 has to do with the fact that even among the 500 Hz data, the inter-timestamp intervals varied widely. Problem 2 is that there are many unusual discontinuities in the saccade trajectories from the Lund University dataset that make it a very poor choice for the construction of an automatic classification method. Error 2 The gazeNet algorithm was trained on the Lund dataset, and then compared to other methods, not trained on this dataset, in terms of performance on this dataset. This is an inherently unfair comparison, and yet nowhere in the gazeNet paper is this unfairness mentioned. Error 3 arises out of the novel event-related agreement analysis employed by the gazeNet authors. Although the authors intended to classify unmatched events as either false positives or false negatives, many are actually being classified as true negatives. True negatives are not errors, and any unmatched event misclassified as a true negative is actually driving kappa higher, whereas unmatched events should be driving kappa lower.

An accurate diagnosis of clinically distinct subgroups of aggressive mature B cell lymphomas is crucial for the choice of proper treatment. Presently, precise recognition of these disorders relies on the combination of morphological, immunophenotypical, and cytogenetic/molecular features. The diagnostic workup in such situations implies the application of costly and time-consuming analyses, which are not always required, since an intensified treatment option is reasonably reserved to fit patients. The Italian Group of Haematopathology proposes herein a practical algorithm for the diagnosis of aggressive mature B cell lymphomas based on a stepwise approach, aimed to select cases deserving molecular analysis, in order to optimize time and resources still assuring the optimal management for any patient.

Children form a specific group of patients, as there are significant differences between children and adults in both anatomy and physiology. Difficult airway may be unanticipated or anticipated. Difficulties encountered during intubation may cause hypoxia, hypoxic brain injury and, in extreme situations, may result in the patient's death. There are few paediatric difficult-airway guidelines available in the current literature, and some of these have significant limitations. This position paper, intended for unanticipated difficult airway, was elaborated by the panel of specialists representing the Polish Society of Anaesthesiology and Intensive Care as well as the Polish Neonatal Society. It covers both elective intubation and emergency situations in children in all age groups. An integral part of the paper is an algorithm. The paper describes in detail all stages of the algorithm considering some modification in specific age groups, i.e. neonates.

Discourse analysis enables the identification of what social actors say and do but also of what they represent in terms of values and motivation Such an analysis can also unearth ideological representations which legitimize the reproduction of social structures, irrespective of whether such representations reflect some objective reality or not. In their turn, such structures are contingent and incomplete, and are subject to changes. Discourse analysis examines their political and historical construction and functioning. What this approach does is help us understand how certain facts and non-facts make it to the political agenda whilst others do not, and how they are interpreted within the public sphere., (Copyright © 2019. Published by Elsevier B.V.)

Background: Commissioning of treatment planning systems (TPS) and beam delivery for scanned light ion beams is an important quality assurance task. This requires measurement of large sets of high quality dosimetric data in anthropomorphic phantoms to benchmark the TPS and dose delivery under realistic conditions., Method: A novel measurement setup is described, which allows for an efficient collection of a large set of accurate dose data in complex phantom geometries. This setup allows dose measurements based on a set of 24 small volume ionization chambers calibrated in dose to water and mounted in a holder, which can be freely positioned in a water phantom with various phantoms mounted in front of the water tank. The phantoms can be scanned in a CT and a CT-based treatment planning can be performed for a direct benchmark of the dose calculation algorithm in various situations., Results: The system has been used for acceptance testing in scanned light ion beam therapy at Heidelberg Ion Beam Therapy Center for scanned proton and carbon ion beams. It demonstrated to be useful to collect large amounts of high quality data for comparison with the TPS calculation using various phantom geometries., Conclusion: The setup is an efficient tool for commissioning and verification of treatment planning systems. It is especially suited for dynamic beam delivery, as many data points can be obtained during a single plan delivery, but can be adapted also for other dynamic therapies, like rotational IMRT.

Objectives: To develop and evaluate methods for assembling radiopaque printed paper sheets to realistic patient phantoms for CT dose and image quality testing., Methods: CT images of two patients were radiopaque printed with aqueous potassium iodide solution (0.6 g/ml) on paper. Two methods were developed for assembling the paper sheets to head and neck phantoms. (1) Printed sheets were fed to a paper-based 3D printer along with corresponding 3D printable STL files. (2) Paper stacks of 5-mm thickness were glued with toner, cut to the patient shape and assembled to a phantom. In a sample application study, both phantoms were examined with five different tube current settings. Images were reconstructed using filtered-back projection (FBP) and iterative reconstruction (AIDR 3D) with three strength levels. Dose length product (DLP), signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNRs) were analysed. Data were analysed using 2-way analysis of variance (ANOVA)., Results: Both methods achieved anthropomorphic phantoms with detailed patient anatomy. The 3D printer yielded a precise reproduction of the external patient shape, but caused visible glue artefacts. Gluing with toner avoided these artefacts and yielded more flexibility with regard to phantom size. In the sample application study, non-inferior SNR and CNR and up to 83.7% lower DLP were achieved on the phantoms with AIDR 3D compared with FBP., Conclusions: Two methods for assembling radiopaque printed paper sheets to phantoms of individual patients are presented. The sample application demonstrates potential for simulation of patient imaging and systematic CT dose and image quality assessment., Key Points: • Two methods were developed to create realistic CT phantoms of individual patients from radiopaque printed paper sheets. • Analysis of five tube current and four reconstruction settings on two radiopaque 3D printed patient phantoms yielded non-inferior SNR and CNR and up to 83.7% lower dose with iterative reconstruction in comparison with filtered back projection. • Radiopaque 3D printed phantoms can simulate patients and allow systematic analysis of CT dose and image quality parameters.

Objective: To evaluate the design characteristics of studies that evaluated the performance of artificial intelligence (AI) algorithms for the diagnostic analysis of medical images., Materials and Methods: PubMed MEDLINE and Embase databases were searched to identify original research articles published between January 1, 2018 and August 17, 2018 that investigated the performance of AI algorithms that analyze medical images to provide diagnostic decisions. Eligible articles were evaluated to determine 1) whether the study used external validation rather than internal validation, and in case of external validation, whether the data for validation were collected, 2) with diagnostic cohort design instead of diagnostic case-control design, 3) from multiple institutions, and 4) in a prospective manner. These are fundamental methodologic features recommended for clinical validation of AI performance in real-world practice. The studies that fulfilled the above criteria were identified. We classified the publishing journals into medical vs. non-medical journal groups. Then, the results were compared between medical and non-medical journals., Results: Of 516 eligible published studies, only 6% (31 studies) performed external validation. None of the 31 studies adopted all three design features: diagnostic cohort design, the inclusion of multiple institutions, and prospective data collection for external validation. No significant difference was found between medical and non-medical journals., Conclusion: Nearly all of the studies published in the study period that evaluated the performance of AI algorithms for diagnostic analysis of medical images were designed as proof-of-concept technical feasibility studies and did not have the design features that are recommended for robust validation of the real-world clinical performance of AI algorithms., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2019 The Korean Society of Radiology.)

Background: After several neutral telehealth trials, the positive findings and subsequent Food and Drug Administration approval of an implantable pulmonary arterial pressure monitor (PAPM) led to renewed interest in remote patient monitoring (RPM). Here we seek to provide contemporary guidance on the appropriate use of RPM technology., Results: Although early trials of external RPM devices suggested benefit, subsequent multicenter trials failed to demonstrate improved outcomes. Monitoring features of cardiac implantable electronic devices (CIEDs) also did not deliver improved HF outcomes, newer, multisensor algorithms may be better. Earlier technologies using direct pressure measurement via implanted devices failed to show benefit owing to complications or failure. Recently, 1 PAPM showed benefit in a randomized controlled trial. Although not showing cost reduction, cost-benefit analysis of that device suggests that it may meet acceptable standards. Additional research is warranted and is in progress. Consumer-owned electronic devices are becoming more pervasive and hold hope for future benefit in HF management. Practical aspects around RPM technology include targeting of risk populations, having mechanisms to ensure patient adherence to monitoring, and health care team structures that act on the data., Conclusions: Based on available evidence, routine use of external RPM devices is not recommended. Implanted devices that monitor pulmonary arterial pressure and/or other parameters may be beneficial in selected patients or when used in structured programs, but the value of these devices in routine care requires further study. Future research is also warranted to better understand the cost-effectiveness of these devices., (Copyright © 2018. Published by Elsevier Inc.)

Conservative activism around the purported influence of Critical Race Theory (CRT) on K-12 education has swept the country in recent years. While others have documented the sources of these messages, how school districts have responded to these critiques has not yet been investigated. Drawing on research on how social media algorithms elevate polarizing information and activate emotions, we analyze public social media posts on school/district Facebook pages mentioning the phrase "critical race" to examine how educators address the claim of teaching CRT and how the local community responds. We use sentiment analysis to examine the emotions of these posts and how they are distributed across states. We also explore the sentiment of subsequent community reactions reflected in the comments of each post, including negative emotions such as anger and fear, and positive emotions such as trust. This study has implications for how school districts can help to stop cycles of fearful rhetoric and engage with stakeholders in ways that unite a school community around shared priorities.

The ACR Incidental Findings Committee presents recommendations for managing pituitary findings that are incidentally detected on CT, MRI and 18 F-fluorodeoxyglucose PET. The Pituitary Subcommittee, which included radiologists practicing neuroradiology and an endocrinologist, developed this algorithm. The recommendations draw from published evidence and expert opinion and were finalized by informal iterative consensus. Algorithm branches successively categorize pituitary findings on the basis of imaging features. They terminate with an ascertainment of an indolent lesion (with sufficient confidence to discontinue follow-up) or a management recommendation. The algorithm addresses most, but not all, pathologies and clinical scenarios. The goal is to improve the quality of care by providing guidance on how to manage incidentally detected pituitary findings., (Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

In this paper, we propose an end-to-end approach to addressing QRS complex detection and measurement of Electrocardiograph (ECG) paper using convolutional neural networks (CNNs). Unlike conventional detection solutions that convert images to digital data, our method can directly detect QRS complex in images using Faster-RCNN, then the R-peak can be located and measured through a CNN. Validated by clinical ECG data in the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database and real ECG paper from Peking University People's Hospital, the proposed method can achieve the recall of 98.32%, the precision of 99.01% in detecting and 0.012 mv of mean absolute error in measuring. Experimental results demonstrate the superior performance of our method over conventional solutions, which would pave the way to detect and measure ECG paper using CNNs.

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical.

Background: In naturally occurring numbers the frequencies of digits 1-9 in the leading position are counterintuitively distributed because the frequencies of occurrence are unequal. Benford-Newcomb's law describes the expected distribution of these frequencies. It was previously shown that known fraudulent articles consistently violated this law., Objective: To compare the features of 12 known fraudulent articles from a single Japanese author to the features of 13 articles in the same research field from other Japanese authors, published during the same time period and identified with a Medline database search., Results: All 25 articles were assessed to determine whether the data violated the law. Formulas provided by the law were used to determine the frequencies of occurrence for the first two leading digits in manually extracted numbers. It was found that all the known fraudulent papers violated the law and 6 of the 13 articles used for comparison followed the law. Assuming that the articles in the comparison group were not falsified or fabricated, the sensitivity of assessing articles with Benford-Newcomb's law was 100% (95% confidence interval CI: 73.54-100%) but the specificity was only 46.15% (95% CI: 19.22-74.87%) and the positive predictive value was 63.16% (95% CI: 38.36-83.71%)., Conclusion: All 12 of the known falsified articles violated Benford-Newcomb's law, which indicated that this analysis had a high sensitivity. The low specificity of the assessment may be explained by the assumptions made about the articles identified for comparison. Violations of Benford-Newcomb's law about the frequencies of the leading digits cannot serve as proof of falsification but they may provide a basis for deeper discussions between the editor and author about a submitted work.

With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]

The ACR Incidental Findings Committee presents recommendations for managing adrenal masses that are incidentally detected on CT or MRI. These recommendations represent an update to the adrenal component of the JACR 2010 white paper on managing incidental findings in the adrenal glands, kidneys, liver, and pancreas. The Adrenal Subcommittee, constituted by abdominal radiologists and an endocrine surgeon, developed this algorithm. The algorithm draws from published evidence coupled with expert subspecialist opinion and was finalized by a process of iterative consensus. Algorithm branches categorize incidental adrenal masses on the basis of patient characteristics and imaging features. For each specified combination, the algorithm concludes with characterization of benignity or indolence (sufficient to discontinue follow-up) and/or a subsequent management recommendation. The algorithm addresses many, but not all, possible pathologies and clinical scenarios. Our goal is to improve the quality of patient care by providing guidance on how to manage incidentally detected adrenal masses., (Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

The ACR Incidental Findings Committee (IFC) presents recommendations for managing pancreatic cysts that are incidentally detected on CT or MRI. These recommendations represent an update from the pancreatic component of the JACR 2010 white paper on managing incidental findings in the adrenal glands, kidneys, liver, and pancreas. The Pancreas Subcommittee-which included abdominal radiologists, a gastroenterologist, and a pancreatic surgeon-developed this algorithm. The recommendations draw from published evidence and expert opinion, and were finalized by informal iterative consensus. Algorithm branches successively categorize pancreatic cysts based on patient characteristics and imaging features. They terminate with an ascertainment of benignity and/or indolence (sufficient to discontinue follow-up), or a management recommendation. The algorithm addresses most, but not all, pathologies and clinical scenarios. Our goal is to improve quality of care by providing guidance on how to manage incidentally detected pancreatic cysts., (Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Purpose: The aim of this work is to develop a method to solve the ill-posed inverse problem of accelerated image reconstruction while correcting forward model imperfections in the context of subject motion during MRI examinations., Methods: The proposed solution uses a Bayesian framework based on deep generative diffusion models to jointly estimate a motion-free image and rigid motion estimates from subsampled and motion-corrupt two-dimensional (2D) k-space data., Results: We demonstrate the ability to reconstruct motion-free images from accelerated two-dimensional (2D) Cartesian and non-Cartesian scans without any external reference signal. We show that our method improves over existing correction techniques on both simulated and prospectively accelerated data., Conclusion: We propose a flexible framework for retrospective motion correction of accelerated MRI based on deep generative diffusion models, with potential application to other forward model corruptions., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this paper, an unsupervised deep learning-based motion artifact correction method for turbo-spin echo MRI is proposed using the deep image prior framework., Theory and Methods: The proposed approach takes advantage of the high impedance to motion artifacts offered by the neural network parameterization to remove motion artifacts in MR images. The framework consists of parameterization of MR image, automatic spatial transformation, and motion simulation model. The proposed method synthesizes motion-corrupted images from the motion-corrected images generated by the convolutional neural network, where an optimization process minimizes the objective function between the synthesized images and the acquired images., Results: In the simulation study of 280 slices from 14 subjects, the proposed method showed a significant increase in the averaged structural similarity index measure by 0.2737 in individual coil images and by 0.4550 in the root-sum-of-square images. In addition, the ablation study demonstrated the effectiveness of each proposed component in correcting motion artifacts compared to the corrected images produced by the baseline method. The experiments on real motion dataset has shown its clinical potential., Conclusion: The proposed method exhibited significant quantitative and qualitative improvements in correcting rigid and in-plane motion artifacts in MR images acquired using turbo spin-echo sequence., (© 2024 International Society for Magnetic Resonance in Medicine.)

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction., (© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness., (© 2024 John Wiley & Sons Ltd.)