Heterogeneous Events Research Articles

Unsupervised Security Threats Identification for Heterogeneous Events

As cyberattacks targeting industrial control systems continue to evolve, the development of sophisticated technologies to detect these security threats becomes increasingly essential. In addition, it is necessary to update adversarial information constantly. However, this process is complicated by the deployment of heterogeneous equipment, which increases the number of indicators and characteristics that must be analyzed by security administrators. Furthermore, security operation centers often struggle to respond promptly to adversaries because of the high number of false alerts caused by unreliable system labels. These challenges make it difficult to construct reliable detection systems. To address these issues, we propose a robust unsupervised threat-identification method. Our approach involves applying a preprocessing technique tailored to the various data types pertinent to alerts, followed by classifying unlabeled alerts using an autoencoder (AE) model. Despite the presence of numerous false positives, we verified that the proposed model could effectively distinguish between different attack types and identify their relationships with only one round of training in homogeneous and heterogeneous environments within industrial control systems. Moreover, our model can filter and display data classified as actual attacks and generate relational tables.

Are sanctions costly for the energy industry of sanctioning states? A difference‐in‐differences approach to sanctions during the Russia–Ukraine war

AbstractThis study investigates the economic effects of states' implementing sanctions against Russia during the early stages of the Russia–Ukraine conflict in 2022. It specifically examines the effect of these sanctions on the stock returns of energy firms in a sample of 57 countries between August 2021 and October 2022, employing Difference‐in‐Differences imputation estimators to analyse heterogeneous event studies. The results show that sanctions initially lead to a short‐term increase in energy stock returns in the treatment countries, but this effect diminishes over time. The study also reveals that sanctions affect energy stock returns in NATO countries and flawed democracies significantly more than non‐NATO and fully democratic nations. The research includes robustness tests using stricter sanctions dates and an expanded sample, providing valuable insights for policymakers and investors navigating geopolitical events and their economic consequences.

Reasoning subevent relation over heterogeneous event graph

Reasoning subevent relation over heterogeneous event graph

Endotypes of Exacerbation in Bronchiectasis: An Observational Cohort Study.

Rationale: Bronchiectasis is characterized by acute exacerbations, but the biological mechanisms underlying these events are poorly characterized. Objectives: To investigate the inflammatory and microbial characteristics of exacerbations of bronchiectasis. Methods: A total of 120 patients with bronchiectasis were enrolled and presented with acute exacerbations within 12 months. Spontaneous sputum samples were obtained during a period of clinical stability and again at exacerbation before receipt of antibiotic treatment. A validated rapid PCR assay for bacteria and viruses was used to classify exacerbations as bacterial, viral, or both. Sputum inflammatory assessments included label-free liquid chromatography-tandem mass spectrometry and measurement of sputum cytokines and neutrophil elastase activity. 16 s rRNA sequencing was used to characterize the microbiome. Measurements and Main Results: Bronchiectasis exacerbations showed profound molecular heterogeneity. At least one bacterium was identified in 103 samples (86%), and a high bacterial load (total bacterial load > 107 copies/g) was observed in 81 patients (68%). Respiratory viruses were identified in 55 (46%) patients, with rhinovirus being the most common virus (31%). PCR testing was more sensitive than culture. No consistent change in the microbiome was observed at exacerbation. Exacerbations were associated with increased neutrophil elastase, proteinase-3, IL-1β, and CXCL8. These markers were particularly associated with bacterial and bacterial plus viral exacerbations. Distinct inflammatory and microbiome profiles were seen between different exacerbation subtypes, including bacterial, viral, and eosinophilic events in both hypothesis-led and hypothesis-free analysis using integrated microbiome and proteomics, demonstrating four subtypes of exacerbation. Conclusions: Bronchiectasis exacerbations are heterogeneous events with contributions from bacteria, viruses, and inflammatory dysregulation.

Multifractal and chaotic characteristics of geomagnetic inter-storm interval over eight solar cycles

The structure and characteristics of geomagnetic inter-storm intervals, the sequence of time differences between two successive storm events, can provide information about the origin and drivers of the storms compared to analyzing the entire data set. In this study, the inter-storm event was investigated using the Kp index over 8 solar cycles (17–24). Three nonlinear approaches were considered: burstiness, multifractal detrended fluctuation analysis, and chaos analysis. The burstiness analysis reveals that the inter-storm intervals are heterogeneous events with irregular time intervals. The multifractal strength of inter-storm intervals ranges from 0.23 to 0.65 for each of the solar cycles, compared to 0.38 for the entire period. Long-range correlations largely drove the multifractality across most solar cycles. Chaos analysis confirmed the low predictability of inter-storm intervals across each solar cycle. The results will be relevant to the prediction and understanding of drivers of storm events across different solar cycles.

Predicting financial distress using current reports: A novel deep learning method based on user-response-guided attention

Effective financial distress prediction (FDP) can discover a company's potential financial risks and support relevant decisions in a timely manner. Previous studies on FDP have mostly focused on using financial indicators and periodic reports. Compared with periodic reports, current reports disclose major events in a timelier manner. But leveraging the information in current reports involves the critical challenges of capturing the complex semantics and measuring the importance of heterogeneous events. To this end, we propose a novel deep learning method, a user-response-guided deep attention network (URGDAN), to predict financial distress using current reports. In the proposed method, we construct a deep learning architecture to integrate financial indicators, current report texts, and user responses. URGDAN leverages the user responses to current reports to guide the semantic feature representation of the reports, it also identifies event information that has a significant correlation with company financial distress. Empirical evaluation shows that URGDAN significantly improves predictive performance and can accurately determine the importance of different current reports. Our work provides practical implications for creditors and investors.

Multistructure Contrastive Learning for Pretraining Event Representation.

Event representation aims to transform individual events from a narrative event chain into a set of low-dimensional vectors to help support a series of downstream applications, e.g., similarity differentiation and missing event prediction. Traditional event representation models tend to focus on single modeling perspectives and thus are incapable of capturing physically disconnected yet semantically connected event segments. We, therefore, propose a heterogeneous event graph model (HeterEvent) to explicitly represent such event segments. Furthermore, another challenge in traditional event representation models is inherited from the datasets themselves. Data sparsity and insufficient labeled data are commonly encountered in event chains, easily leading to overfitting and undertraining. Therefore, we extend HeterEvent with a multistructure contrastive learning framework (MulCL) to alleviate the training risks from two structural perspectives. From the sequential perspective, a sequential-view contrastive learning component (SeqCL) is designed to facilitate the acquisition of sequential characteristics. From the graph perspective, a graph-view contrastive learning component (GraCL) is proposed to enhance the robustness of graph training by comparing different corrupted graphs. Experimental results confirm that our proposed MulCL [W+E] model outperforms state-of-the-art baselines. Specifically, compared with the previously proposed supervised model HeterEvent [W+E] [Zheng et al. (2020)], MulCL [W+E] shows an average improvement of 5.3% in terms of accuracy for the inference-ability-based tasks. For the representation-ability-based tasks, MulCL [W+E] achieves an average improvement of 2.7% in terms of accuracy for the hard similarity tasks and an improvement of 4.1% in terms of the Spearman's correlation for the transitive sentence similarity task, respectively.

AUTOBIOGRAPHIES BASED ON MODELS OF FICTION LITERATURE PLOTS IN RUSSIAN MEMOIRISTICS OF THE 18TH CENTURY

The article identifies thе alternative changes to the traditional plot pattern for autobiographical narration. Memoirs of the 18th century are based on the cumulative principle presuming that heterogeneous events (both nation-wide and those of public and private life of the memoirist himself) are described in parallel and in a strict chronological sequence. In contrast to the previous tradition of memoir-making, Shakhovskoy conceptualizes his life as moving from happiness to unhappiness and focuses on the cumulative plot of an adventure novel. He describes only official activity and builds up the plot of the autobiography as a number of series, consisting of structurally homogeneous microplots or plot situations. Happiness alternates with unhappiness both within the series of microplots and in the very sequence of the series (loss of patrons — victories over opponents — victories of opponents — success in the service of Her Majesty). Unlike other autobiographies, The Adventures of Ensign Klimov is based not on the cumulative but on the cyclic pattern of the plot (forced abandonment of the Fatherland and the family — suffering calamities in exile — return). The plot of Klimov’s memoirs is semantically based on the story of cruel fate and God’s will. The memoirs of Shakhovsky and Klimov actually represent two possible options for transferring the plot structure of epic genres into memoir narration: specifically, the cumulative plot of an adventure novel in one case, and the cyclic one of the parable of the prodigal son and ancient Russian stories dating back to it in the other. The use of models of fiction literature plots is predetermined by the authors’ conceptual understanding of their own life history, in contrast to most memoirs of the 18th century, where events are simply recorded as a chronicle and do not line up in a semantically determined plot series.

An enhanced energy efficient protocol for large-scale IoT-based heterogeneous WSNs

There is increasing attention, recently, to optimizing energy consumption in IoT-based large-scale networks. Extending the lifetime of battery-powered nodes is a key challenge in such systems and their various application scenarios. This paper proposes a new zone-based and event-driven protocol for saving energy in large-scale heterogeneous WSNs called TESEES (Threshold Enabled Scalable and Energy Efficient Scheme). The proposed protocol is designed to support network scenarios deploying higher levels of heterogeneity with more than three types of sensor nodes (i.e., four, five, and more). TESEES is a reactive version of the proactive SEES protocol, in which we leverage a novel state-of-the-art thresholding model on the zone-based hierarchical deployments of heterogeneous nodes to regulate the data reporting process, avoiding unnecessary frequent data transmission and reducing the amount of energy dissipation of the sensing nodes and the entire system. With this model, we present a general technique for formulating distinct thresholds for network nodes in each established zone. This mechanism allows for individually configuring the nodes with transmission settings tailored to their respective roles, independent of the heterogeneity levels, total node count, or initial energy. This approach ensures that each node operates optimally within the network. In addition, we present an improved hybrid TMCCT (Threshold-based Minimum Cost Cross-layer Transmission) algorithm that operates at the node level and ensures effective data transmission control by considering current sensor values, heterogeneous event thresholds, and previous data records. Instead of periodical data transmission, this hybridization mechanism, integrated with a grid of energy-harvesting relay nodes, keeps the zone member nodes in the energy-saving mode for maximum time and allows for reactive data transmission only when necessary. This results in a reduced data-reporting frequency, less traffic load, minimized energy consumption, and thus a greater extension of the network’s lifetime. Moreover, unlike the traditional cluster-head election in the weighted probability-based protocols, TESEES relies on an efficient mechanism for zone aggregators’ election that runs at the zone level in multiple stages and employs various static and dynamic parameters based on their generated weights of importance. This leads to selecting the best candidate nodes for the aggregation task and, hence, fairly rotating the role among the zones’ alive nodes. The simulation results show significant improvements in the total energy saving, the lifetime extension, and the transmitted data reduction, reaching 29%, 68%, and 26% respectively, compared to the traditional SEES protocol. Also, the average energy consumption per single round has decreased by 36%.

AMiner: A Modular Log Data Analysis Pipeline for Anomaly-based Intrusion Detection

Cyber attacks are omnipresent and their rapid detection is crucial for system security. Signature-based intrusion detection monitors systems for attack indicators and plays an important role in recognizing and preventing such attacks. Unfortunately, it is unable to detect new attack vectors and may be evaded by attack variants. As a solution, anomaly detection employs techniques from machine learning to detect suspicious log events without relying on predefined signatures. While visibility of attacks in network traffic is limited due to encryption of network packets, system log data is available in raw format and thus allows fine-granular analysis. However, system log processing is difficult as it involves different formats and heterogeneous events. To ease log-based anomaly detection, we present the AMiner, an open-source tool in the AECID toolbox that enables fast log parsing, analysis, and alerting. In this article, we outline the AMiner’s modular architecture and demonstrate its applicability in three use-cases.

Transportan 10 Induces Perturbation and Pores Formation in Giant Plasma Membrane Vesicles Derived from Cancer Liver Cells.

Continuous progress has been made in the development of new molecules for therapeutic purposes. This is driven by the need to address several challenges such as molecular instability and biocompatibility, difficulties in crossing the plasma membrane, and the development of host resistance. In this context, cell-penetrating peptides (CPPs) constitute a promising tool for the development of new therapies due to their intrinsic ability to deliver therapeutic molecules to cells and tissues. These short peptides have gained increasing attention for applications in drug delivery as well as for their antimicrobial and anticancer activity but the general rules regulating the events involved in cellular uptake and in the following processes are still unclear. Here, we use fluorescence microscopy methods to analyze the interactions between the multifunctional peptide Transportan 10 (TP10) and the giant plasma membrane vesicles (GPMVs) derived from cancer cells. This aims to highlight the molecular mechanisms underlying functional interactions which bring its translocation across the membrane or cytotoxic mechanisms leading to membrane collapse and disruption. The Fluorescence Lifetime Imaging Microscopy (FLIM) method coupled with the phasor approach analysis proved to be the winning choice for following highly dynamic spatially heterogeneous events in real-time and highlighting aspects of such complex phenomena. Thanks to the presented approach, we were able to identify and monitor TP10 translocation into the lumen, internalization, and membrane-induced modifications depending on the peptide concentration regime.

Manganous water column in the Tethys Ocean during the Permian-Triassic transition

Ocean anoxia was one of the key killing mechanisms responsible for the end-Permian mass extinction (~252 Ma). However, the temporal evolution and the triggering mechanisms of the end-Permian anoxia are controversial, with the current view being that the water column deoxygenation was a spatially and temporally heterogeneous event. Here, we use cerium-anomalies, uranium contents and rare earth element and yttrium (REY) compositions measured on the carbonate fraction of samples from two marine sections in Armenia and South China to constrain the evolution of end-Permian marine anoxia. In particular, we attempt to identify intervals of manganous redox conditions (i.e., Mn2+-rich water column). These are characterized by the reduction of Mn(IV)-oxides at a redox potential lower than of nitrate and higher than of Fe(III)-oxide reduction. Most of our samples yield REY patterns with seawater characteristics suggesting the dominance of hydrogenous REYs. While decreasing U concentrations in the C. yini Zone suggest an overall intensification of global marine anoxia in the latest Permian, Ce-anomalies shift from negative to positive above the C. yini Zone, implying a deterioration in the oxygen content of the local water columns. Highly positive Ce-anomalies (Ce/Ce* > 1.3) are firstly reported for the P-T interval, indicating enrichment of Ce in the water column and the establishment of manganous redox conditions at both locations. This change coincided globally with climatic warming, the onset of the marine extinction interval as well as locally with the disappearance of sponge spicules and radiolarians (South China) and the appearance of microbialites (Armenia).

Burst and Memory-aware Transformer: capturing temporal heterogeneity.

Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model, designed to explicitly address temporal heterogeneity. The BMT model embeds the burstiness and memory coefficient into the self-attention module, enhancing the learning process with insights derived from the bursty patterns. Furthermore, we employed a novel loss function designed to optimize the burstiness and memory coefficient values, as well as their corresponding discretized one-hot vectors, both individually and jointly. Numerical experiments conducted on diverse synthetic and real-world datasets demonstrated the outstanding performance of the BMT model in terms of accurately predicting event times and intensity functions compared to existing models and control groups. In particular, the BMT model exhibits remarkable performance for temporally heterogeneous data, such as those with power-law inter-event time distributions. Our findings suggest that the incorporation of burst-related parameters assists the Transformer in comprehending heterogeneous event sequences, leading to an enhanced predictive performance.

Intravenous Iron Supplementation for the Treatment of Chemotherapy-Induced Anemia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Background: The pathophysiology of cancer-related anemia is multifactorial, including that of chemotherapy-induced anemia (CIA). The guidelines are not consistent in their approach to the use of intravenous (IV) iron in patients with cancer as part of the clinical practice. Materials and methods: All randomized controlled trials that compared IV iron with either no iron or iron taken orally for the treatment of CIA were included. We excluded trials if erythropoiesis-stimulating agents (ESAs) were used. The primary outcome was the percentage of patients requiring a red blood cell (RBC) transfusion during the study period. The secondary outcomes included the hematopoietic response (an increase in the Hb level by more than 1 g/dL or an increase above 11 g/dL), the iron parameters and adverse events. For the dichotomous data, risk ratios (RRs) with 95% confidence intervals (Cis) were estimated and pooled. For the continuous data, the mean differences were calculated. A fixed effect model was used, except in the event of significant heterogeneity between the trials (p < 0.10; I2 > 40%), in which we used a random effects model. Results: A total of 8 trials published between January 1990 and July 2021 that randomized 1015 patients fulfilled the inclusion criteria. Of these, 553 patients were randomized to IV iron and were compared with 271 patients randomized to oral iron and 191 to no iron. IV iron decreased the percentage of patients requiring a blood transfusion compared with oral iron (RR 0.72; 95% CI 0.55–0.95) with a number needed to treat of 20 (95% CI 11–100). IV iron increased the hematopoietic response (RR 1.23; 95% CI 1.01–1.5). There was no difference with respect to the risk of adverse events (RR 0.97; 95% CI 0.88–1.07; 8 trials) or severe adverse events (RR 1.09; 95% CI 0.76–1.57; 8 trials). Conclusions: IV iron resulted in a decrease in the need for RBC transfusions, with no difference in adverse events in patients with CIA. IV iron for the treatment of CIA should be considered in clinical practice.

Some features of the construction of theatrical artistic reality (analysis of the production of "Eugene Onegin" by Rimas Tuminas)

The article presents two plans for the analysis of the famous production of Rimas Tuminas "Eugene Onegin". The first discusses the problems and difficulties of translating Pushkin's poetic work into the language of the theater. At the same time, the author uses the analysis of this production carried out by Alexander Minkin in the book "Mute Onegin". In particular, a number of techniques are considered (splitting the heroes, Onegin and Lensky, into two persons, giving historical events a modern form of existence, which implies rethinking them); they, according to Minkin, were used in his production by Tuminas. The author provides a voice to Alexander Sergeevich, who does not recognize his work in the production of Tuminas, assuming that he solved problems far from those that interested Pushkin. In the second part, a reconstruction of the logic is proposed, in accordance with which Tuminas most likely acted, creating his own statement. The author shows that, firstly, Tuminas is not trying to present Pushkin's brilliant work in a new, modern way, but creates a new work, his own; secondly, the artistic reality of this new work is made up of very different events, including reflexive ones. He raises the question of how these heterogeneous events create a whole. If in the traditional aesthetics the artist depicts the world and life existing outside of a person (or inside him), which are considered as universally significant for everyone, then in the new one the world and events of consciousness of the personality of the artist and the viewer are described and constituted.

О методике исследования доз вредных производственных воздействий

To assess the sanitary and hygienic working conditions for a number of factors in the working environment, exposure dose measurements are required. In contrast to the direct measurement of levels, the dose is determined by several measurements, followed by the calculation of the result. This requires a special measurement methodology. The scope of regular, rigidly defined methodologies is limited to permanent workplaces, where the work shift can be presented in the form of clearly defined long time intervals with a stable nature of the impact. In general cases of non-permanent workplaces with shifts of heterogeneous events, continuous measurements are required during the working day, which, as a rule, turns out to be unjustified for practical reasons. The paper discusses a methodology for determining the doses of exposure to production factors for a work team, in which the employees randomly move between different production areas, and harmful factors turn on at random times and act with random intensity. Employee movements can be viewed as a series of Bernoulli trials with a certain exposure probability. The probability of the distribution is described by the binomial law. In addition to the average dose values, the probabilistic approach allows to estimate type A and B uncertainties. The reliability of the results is checked by comparing the average value of the measurement results with the standard deviation. If the first is less than the second, the measurement results can be trusted.

Panel Sequencing for Clinically Oriented Variant Screening and Copy Number Detection in Chronic Lymphocytic Leukemia Patients.

According to current guidelines, in chronic lymphocytic leukemia (CLL), only the TP53 molecular status must be evaluated prior to every treatment’s initiation. However, additional heterogeneous genetic events are known to confer a proliferative advantage to the tumor clone and are associated with progression and treatment failure in CLL patients. Here, we describe the implementation of a comprehensive targeted sequencing solution that is suitable for routine clinical practice and allows for the detection of the most common somatic single-nucleotide and copy number variants in genes relevant to CLL. We demonstrate that this cost-effective strategy achieves variant detection with high accuracy, specificity, and sensitivity. Furthermore, we identify somatic variants and copy number variations in genes with prognostic and/or predictive value, according to the most recent literature, and the tool provides evidence about subclonal events. This next-generation sequencing (NGS) capture-based target assay is an improvement on current approaches in defining molecular prognostic and/or predictive variables in CLL patients.

Modeling and analyzing attacker behavior in IoT botnet using temporal convolution network (TCN)

Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral analysis of attackers is shown to be effective against a wide variety of attack types. Previous works, however, focus solely on anomalies in network traffic to detect bots and botnet. In this work we take a more robust approach of analyzing the heterogeneous events including network traffic, file download events, SSH logins and chain of commands input by attackers in a compromised host. We have deployed several honeypots to simulate Linux shells and allowed attackers access to the shells. We have collected a large dataset of heterogeneous threat events from the honeypots. We have then combined and modeled the heterogeneous threat data to analyze attacker behavior. Then we have used a deep learning architecture called a Temporal Convolutional Network (TCN) to do sequential and predictive analysis on the data. A prediction accuracy of 85−97% validates our data model as well as our analysis methodology. In this work, we have also developed an automated mechanism to collect and analyze these data. For the automation we have used CYbersecurity information Exchange (CYBEX). Finally, we have compared TCN with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and have showed that TCN outperforms LSTM and GRU for the task at hand.

Public Health Surveillance for Adverse Events Following COVID-19 Vaccination in Africa.

Local, national, and international health agencies have advocated multi-pronged public health strategies to limit infections and prevent deaths. The availability of safe and effective vaccines is critical in the control of a pandemic. Several adverse events have been reported globally following reception of different vaccines, with limited or no data from Africa. This cross-sectional epidemiological study investigated adverse events following COVID-19 vaccination in Africans from April–June, 2021 using a structured online questionnaire. Out of 1200 participants recruited, a total of 80.8% (n = 969) respondents from 35 countries, including 22 African countries and 13 countries where Africans live in the diaspora, reported adverse events. Over half of the vaccinees were male (53.0%) and frontline healthcare workers (55.7%), respectively. A total of 15.6% (n = 151) reported previous exposure to SARS-CoV-2, while about one-fourth, 24.8% (n = 240), reported different underlying health conditions prior to vaccination. Fatal cases were 5.1% (n = 49), while other significant heterogenous events were reported in three categories: very common, common, and uncommon, with the latter including enlarged lymph nodes 2.4% (n = 23), menstrual disorder 0.5% (n = 5), and increased libido 0.2% (n = 2). The study provided useful data for concerned authorities and institutions to prepare plans that will address issues related to COVID-19 vaccines.

Near infrared indocyanine green fluorescent cholangiography versus intraoperative cholangiography to improve safety in laparoscopic cholecystectomy for gallstone disease\u2014a systematic review protocol

BackgroundLaparoscopic cholecystectomy has become the standard surgical approach in the treatment of cholelithiasis. Diverse surgical techniques and different imaging modalities have been described to evaluate the biliary anatomy and prevent or early detect bile duct injuries. X-ray intraoperative cholangiography (IOC) and near infrared indocyanine green fluorescent cholangiography (NIR-ICG) are safe and feasible techniques to assess biliary anatomy. The aim of this systematic review will be to evaluate if NIR-ICG can visualize extrahepatic biliary anatomy more efficiently and safer than IOC in minimally invasive cholecystectomy for gallstone disease.MethodsLiterature search will be performed via MEDLINE (PubMed), Embase, Scopus, the Cochrane Central Register of Controlled Trials, and Web of Science Core Collection from 2009 to present. All randomized controlled clinical trials and prospective non-randomized controlled trials which report on comparison of NIR-ICG versus IOC will be included. All patients over 18 years old who require elective or urgent minimally invasive cholecystectomy (undergoing NIR-ICG during this procedure) due to gallstone disease both acute and chronic will be included. Since BDI has a low incidence, the primary outcome will be the ability to visualize extrahepatic biliary anatomy and the time to obtain relevant images of these structures.Two researchers will individually screen the identified records, according to a list of inclusion and exclusion criteria. Bias of the studies will be evaluated with the Newcastle-Ottawa score for non-randomized studies and with The Cochrane Risk of Bias Tool for randomized controlled trials. Quality of evidence for all outcomes will be determined with the GRADE system. The data will be registered in a predesigned database. If selected studies are sufficiently homogeneous, we will perform a meta-analysis of reported results. In the event of a substantial heterogeneity, a narrative synthesis will be provided. Subgroup analysis will be used to investigate possible sources of heterogeneity.DiscussionUnderstanding the benefits of this technique is critical to ensuring policymakers can make informed decisions as to where preventive efforts should be focused regarding specific imaging techniques. If ICG is proven to be faster and non-invasive, its routine use could be encouraged.Systematic review registrationPROSPERO CRD42020177991.