Global Image Research Articles

3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.

Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neural network (3-LbNets) to identify no glaucoma, glaucoma suspect, and glaucoma cases in global fundus images. 3-LbNets extracts important features from 3 different labeling modals and puts them into an artificial neural network (ANN) to find the final result. The method was effective, with an AUC of 98.66% for no glaucoma, 97.54% for glaucoma suspect, and 97.19% for glaucoma when analysing 206 fundus images evaluated with unanimous agreement from 3 well-trained ophthalmologists (3/3). When analysing 178 difficult to interpret fundus images (with majority agreement (2/3)), this method had an AUC of 80.80% for no glaucoma, 69.52% for glaucoma suspect, and 82.74% for glaucoma cases.Clinical relevance-This establishes a robust global fundus image screening network based on the ensemble method that can optimize glaucoma screening to alleviate the toll on those with glaucoma and prevent glaucoma suspects from developing the disease.

Element-removal global digital image correlation for accurate discontinuous deformation field measurement in fracture mechanics

We propose an element-removal (ER) global digital image correlation (DIC) method to improve the measurement accuracy of discontinuous deformation fields, such as crack propagation. The occurrence of cracks in materials or structures inevitably deteriorates the tracking accuracy, and, consequently, the strain field accuracy obtained by regular subset and global DIC. The proposed ER-global-DIC algorithm iteratively identifies and removes all the elements covering the crack, during the updating of displacement fields. In the remaining elements, the continuous shape function is applicable for accurate deformation measurement. In principle, although elements that contain the cracks are removed, the algorithm preserves the same number of nodes since the nodes are retained by the remaining elements. Synthetically deformed images based on analytical discontinuous displacement fields validate the effectiveness and accuracy of the proposed method. The ER-global-DIC is further applied to measure the discontinuous displacement fields containing a crack deflection, generated from a finite element model with a cohesive zone model. The results demonstrate the potential of the proposed method for discontinuous deformation measurement on advanced materials, e.g., fiber-reinforced composites.

Longitudinal association between self-compassion and intuitive eating: Testing emotion regulation and body image flexibility as mediating variables.

Growing evidence suggests that intuitive eating is associated with numerous positive mental health and well-being constructs. Understanding factors that predict intuitive eating is necessary for identifying practical targets to enhance this style of eating, yet research identifying such predictors is scarce. Self-compassion is one variable that could enhance intuitive eating because it involves the practice of healthy emotion regulation skills that may disrupt the tendency to turn to food to cope during distressing situations. The present study tested for a longitudinal association between self-compassion and intuitive eating. We also tested whether this association was mediated by indices of emotion regulation (i.e., global emotion regulation skill scores and body image flexibility). Adult women (n = 3039) were invited to completed study measures at baseline (T1), 4-month follow-up (T2), and 8-month follow-up (T3). Path analyses were computed to test hypothesized indirect effects. A direct path from T1 self-compassion to T3 intuitive eating emerged, such that higher self-compassion levels predicted increased intuitive eating over time. However, this association was not mediated by T2 emotion regulation skills nor body image flexibility. Findings suggest that self-compassion may facilitate an intuitive eating style, which does not appear to be explained by certain emotion regulation skills.

Context-Based Novel Histogram Bin Stretching Algorithm for Automatic Contrast Enhancement

This article presents CHBS, a novel context-based histogram bin stretching method that enhances the contrast by increasing the range of gray levels and randomness among the gray levels. It comprises image spatial contextual information and discrete cosine transform (DCT). It constitutes the global enhancement with the context-based histogram bin stretching and local details with the DCT. First, it uses the spatial similarities among surrounding pixels to generate random numbers. Unlike the other methods, the similarity map is generated based on the neighboring pixels’ mutual relationship. Intensity values are distributed among the available dynamic range to generate a global contrast-enhanced image. Second, the DCT is further applied to the previous contrast-enhanced image to adjust its local details automatically. Several experiments are conducted on the different levels of contrast degraded images. Both subjective and objective assessment outcomes validate that the projected approach is better or comparable with several state-of-the-art approaches in terms of brightness preservation, richer details, and natural appearance.

EVENT TOURISM IN GERMANY: EXPERIENCE FOR UKRAINE

The article describes and analyzes the event tourism in Germany, in particular its component and geographical structure. It is also possibilities of using German experience for Ukraine are highlighted.
 Germany, which suffered a total economy destruction after the Second World War, rebuilt one of the world's largest industries of tourism. This experience is extremely important and relevant for modern Ukraine, which is confidently heading towards EU membership.
 The analysis of researched topic latest publications was carried out and it were established insufficient coverage of this issue in domestic scientific literature. Therefore, the purpose of the article is to study the experience of organizing event tourism in Germany.
 The main tasks of the article are formulated as follows: to characterize the current state of event tourism in Germany; to study the factors influencing the state and development of event tourism in Germany; to find out the problems and outline the prospects for the development of German’s event tourism; to describe the possibility of using the conducted research in tourism industry practice and science.
 Note that in the article understand event tourism as a trip with the purpose of participating in or observing a certain event (carnival, festival, holiday, ceremony, sports competition or natural phenomenon, etc.), which requires the availability of appropriate tourist resources and special training.
 Event tourism plays an important role in the development of the country's economy and the creation of new job places. The current research is based on international statistics, in particular UNWTO statistics. Thus, it was established that Germany is one of the leaders among European countries in tourism industry development, including event tourism.
 The COVID-19 pandemic had a negative impact on the event tourism development dynamics. Nowadays, there are a number of restrictions, but the industry is rapidly recovering.
 External and internal factors that positively and negatively affect the development of event tourism in Germany have been studied.
 The German’s event tourism SWOT-analysis were carried out to determine development prospects, based on advantages and disadvantages
 A PEST-analysis was used for a detailed parsing of the external factors influence. The degree of these factors impact was determined by the method of expert evaluation on a 5-point scale. In particular, it was established factors that have the greatest impact on the event tourism development. They are:
 - political: restriction of movement across borders, introduction of visa regime and consequences of COVID-19;
 - economic: low level of income of the population due to the pandemic;
 - social: low educational level, culture and level of employment;
 - technological: introduction of a remote form of service provision.
 Thus, event tourism has not only cultural, but also socio-economic significance for the development of tourism in Germany. The most popular are music, gastronomic and film festivals, as well as sports events. The country's government actively supports the development of this tourism type, as it leads to an increase in revenues to local budgets and is an effective tool for forming local and global positive image.
 Summarizing the results of the study are presented in the article, confirm the importance of Germany experience for Ukraine and can be used in practice.
 Keywords: tourism, event tourism, experience, Ukraine, Germany

Adaptive Critical Region Extraction Net via relationship modeling for bone age assessment

Bone age assessment is an efficient clinical practice to diagnose children’s endocrine, genetic, and growth disorders. Guided by clinical diagnostic methods, local critical regions cropped from original hand radiography images have significant biological identification meaning in skeletal maturity analysis. However, existing methods crop local regions without maintaining the relationship between cropped regions and the global image, which results in discarding essential information. To address this problem, we propose an Adaptive Critical Region Extraction Net (ACRE-Net) for bone age assessment, which can automatically locate local critical regions from global images and calibrate features by relationship modeling. Unlike existing two-stage methods, which crop global images in the first stage and analyze cropped parts in the second stage separately, our ACRE-Net maintains the relationships between global images and cropped regions and further integrates such relationship information into relationship-guided features. With the relationship-guided features, we propose a novel cross-graph feature calibration module which is consisted of a bigraph structure to supplementary and calibrate features extracted from cropped parts by also modeling the relationship between cropped parts. Meanwhile, we offer a novel localization strategy in the patch-based region localization module, which aggregates and leverages multi-scale attention maps to locate patches of interest as critical regions to be cropped. In practical application, our ACRE-Net can free clinicians from time-consuming manual annotations on local critical regions and automatically assess bone age through radiography images. Extensive experiments illustrate that our ACRE-Net achieves state-of-the-art performance with Mean Absolute Error (MAE) 3.78 months on the public RSNA2017 dataset.

Women in cardiovascular imaging: a call for action to address ongoing challenges.

The EACVI Scientific Initiatives Committee and the EACVI women's taskforce conducted a global survey to evaluate the barriers faced by women in cardiovascular imaging (WICVi). In a prospective international survey, we assessed the barriers faced at work by WICVi. Three hundred fourteen participants from 53 countries responded. The majority were married (77%) and had children (68%), but most reported no flexibility in their work schedule during their pregnancy or after their maternity leave. More than half of the women reported experiencing unconscious bias (68%), verbal harassment (59%), conscious bias (51%), anxiety (70%), lack of motivation (60%), imposter syndrome (54%), and burnout (61%) at work. Furthermore, one in five respondents had experienced sexual harassment, although this was rarely reported formally. The majority reported availability of mentorship (73%), which was mostly rated as 'good' or 'very good'. While more than two-thirds of respondents (69%) now reported being well trained and qualified to take on leadership roles in their departments, only one-third had been afforded that opportunity. Despite the issues highlighted by this survey, >80% of the participating WICVi would still choose cardiovascular imaging if they could restart their career. The survey has highlighted important issues faced by WICVi. While progress has been made in areas such as mentorship and training, other issues including bullying, bias, and sexual harassment are still widely prevalent requiring urgent action by the global cardiovascular imaging community to collectively address and resolve these challenges.

Constructing a Vishwaguru (world teacher): Hindu nationalism, populism and the domestic consumption of Narendra Modi’s global image

Narendra Modi is a prime example of a right-wing nationalist populist, in his case, trying to create a new India that rejects India’s traditional secular liberalism in favour of a Hindu state. Modi has gained a reputation amongst his supporters as a visionary who is improving India’s standing on the world stage as a great power and making a revolutionary change in India. But Modi has come under increasing international concern and condemnation for his majoritarian nationalist authoritarianism, which has seen India slide on many democracy indicators, a reality which his supporters reject. We argue, thus, that there is a fundamental discrepancy between the image of the Modi regime abroad and at home, that has gone unexplored in scholarship. We also contend that this discrepancy is implicated in post-truth politics. Modi’s populist project is one of asserting a true Hindu Indian identity, the global criticisms of which, under conditions of post-truth, are either irrelevant or, ironically, contribute to its strength.

Hashtag #TaiwanCanHelp as a Strategy of Taiwan Warm Power Diplomacy

This research intends to analyse diplomacy strategy using the hashtags #TaiwanCanHelp to enhance Taiwan’s “warm power” in the international community. This paper is qualitative-descriptive research and is analysed by using the concept of digital diplomacy. As a ghost island, Taiwan struggles to be present and seen in the international community. Taiwan’s success story in combating the COVID-19 pandemic has changed its global image. Taiwan has actively utilized this situation into political sentiment by campaigning the hashtags #TaiwanCanHelp on digital platforms to spreads its warm power and gain international recognition. The use of social media by government actors and activists has engaged awareness of Taiwan’s situation from more people. It was begun as a campaign to sound Taiwan’s exclusion from WHO and WHA, it later has been used as a slogan to construct Taiwan’s image in global presence as a warm, helpful, and friendly country.

Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm.

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.

Foreign Policy: Character of Nigeria’s President in Foreign Relationship (Case of Dr. Goodluck Ebele Jonathan)

This piece of write-up is aimed at examining whether or not Nigeria’s foreign policy under former President Goodluck Jonathan contributed to improving the image of Nigeria globally. The study reveals that Nigeria’s foreign policy over the years has been greatly sabotaged by inconsistencies and ambiguities. The study also shows that more than external challenges, it is the internal challenges that are dampening Nigeria’s attainment of positive international image and national transformation. Some of those challenges include leadership crisis, corruption, insurgency and terrorism amongst others. It is therefore recommended that major reorganization of the foreign services, such that positions in the diplomatic corps and other key positions will no longer be politicized and the government should integrate professional international public relations practitioners in its interactions with the international community at large, in order to effectively boost the global image of Nigeria.

Context-aware lightweight remote-sensing image super-resolution network.

In recent years, remote-sensing image super-resolution (RSISR) methods based on convolutional neural networks (CNNs) have achieved significant progress. However, the limited receptive field of the convolutional kernel in CNNs hinders the network's ability to effectively capture long-range features in images, thus limiting further improvements in model performance. Additionally, the deployment of existing RSISR models to terminal devices is challenging due to their high computational complexity and large number of parameters. To address these issues, we propose a Context-Aware Lightweight Super-Resolution Network (CALSRN) for remote-sensing images. The proposed network primarily consists of Context-Aware Transformer Blocks (CATBs), which incorporate a Local Context Extraction Branch (LCEB) and a Global Context Extraction Branch (GCEB) to explore both local and global image features. Furthermore, a Dynamic Weight Generation Branch (DWGB) is designed to generate aggregation weights for global and local features, enabling dynamic adjustment of the aggregation process. Specifically, the GCEB employs a Swin Transformer-based structure to obtain global information, while the LCEB utilizes a CNN-based cross-attention mechanism to extract local information. Ultimately, global and local features are aggregated using the weights acquired from the DWGB, capturing the global and local dependencies of the image and enhancing the quality of super-resolution reconstruction. The experimental results demonstrate that the proposed method is capable of reconstructing high-quality images with fewer parameters and less computational complexity compared with existing methods.

GOMPS: Global Attention-based Ophthalmic Image Measurement and Postoperative Appearance Prediction System

Accurate measurements of ophthalmic parameters and postoperative appearance prediction are essential for the diagnosis and treatment of many ophthalmic diseases. Nevertheless, it remains challenging due to (1) inconsistent ophthalmic image sampling standards, including ocular-camera distance, facial angle, and patient number, (2) complicated ocular morphology, such as subconjunctival hemorrhage, ocular movements, lighting effects, and morphological aging. It is difficult for a model to measure parameters and make predictions in variable sampling methods and morphology conditions. Therefore, the Global attention-based Ophthalmic Image Measurement and Postoperative Appearance Prediction System (GOMPS) is proposed, which quantifies ophthalmic image parameters to diagnose disease and simultaneously predict postoperative appearance of blepharoptosis. By perceiving the global structure of the ophthalmic image, GOMPS makes logical inference predictions of the sclera and cornea morphology, to overcome the above difficulties. Concretely, a global attention unit (GAU) and a novel global attention structure-aware network (GASA-Net) are designed to enhance GOMPS’s global structure awareness ability to perform logical reasoning. Extensive experimental results on our collected ophthalmic dataset for diagnosis & prediction (OD2P) demonstrate that GOMPS surpasses the state-of-the-art methods in segmentation accuracy and achieves the current optimal performance in measurement and postoperative prediction under many clinical scenes.

Analysis of Surface Temperature in Buru District Using Cloud Computing on Google Earth Engine

Monitoring land surface temperature in Buru Regency using Google Earth Engine cloud computing-based geospatial technology can help in understanding global climate and weather change, as well as provide important information for scientists, governments, and non-governmental organizations in making decisions related to climate change mitigation and natural disaster management. This research aims to analyze land surface temperature in Buru Regency using MODIS satellite image data based on the cloud computing google earth engine. This research uses Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Land Surface Temperature and Emissivity 8-Day Global image data analyzed on Google Earth Engine. The results show that the lowest land surface temperature value in Buru Regency is 12, 7438ᵒ C, and the highest value is 31, 9582ᵒ C. The area that has a land surface temperature (LST) in the very high class has an area of 96,604.46 ha or 19.90%, the LST area in the high class is 139,606.47 ha or 28.76%, the LST area in the medium class is 140,853.38 ha or 29.02%, the LST area in the low class is 79,896.56 ha or 16.46% and the LST area in the very low class is 28,458.57 ha or 5.86%. The land surface temperature analysis in Buru Regency can provide important information for the local government in making policies and planning for sustainable regional development.

Dynamic Weighting Network for Person Re-Identification.

Recently, hybrid Convolution-Transformer architectures have become popular due to their ability to capture both local and global image features and the advantage of lower computational cost over pure Transformer models. However, directly embedding a Transformer can result in the loss of convolution-based features, particularly fine-grained features. Therefore, using these architectures as the backbone of a re-identification task is not an effective approach. To address this challenge, we propose a feature fusion gate unit that dynamically adjusts the ratio of local and global features. The feature fusion gate unit fuses the convolution and self-attentive branches of the network with dynamic parameters based on the input information. This unit can be integrated into different layers or multiple residual blocks, which will have varying effects on the accuracy of the model. Using feature fusion gate units, we propose a simple and portable model called the dynamic weighting network or DWNet, which supports two backbones, ResNet and OSNet, called DWNet-R and DWNet-O, respectively. DWNet significantly improves re-identification performance over the original baseline, while maintaining reasonable computational consumption and number of parameters. Finally, our DWNet-R achieves an mAP of 87.53%, 79.18%, 50.03%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets. Our DWNet-O achieves an mAP of 86.83%, 78.68%, 55.66%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets.

Magic realism and the feminine in Encanto

The Oscar-winning Disney film Encanto (2021) was hailed for its authentic representation of Colombian culture, but it also sparked a discussion on film’s ability to capture aspects of a culture and even change the global image of a country. This essay does not attempt to evaluate the merit of Encanto as a conveyor of Colombian culture, but investigates the representation of the narrative mode, magic realism, as it is translated from Gabriel García Márquez’s One Hundred Years of Solitude into Disney animation to be presented to a global audience. Based on the current notion of the feminine in modern animated Disney films, we explore how it may function paratextually to guide interpretation of the feminine connection with magic. Viewers of a Disney film in 2021 may have responded differently according to their cultural background and knowledge of languages, and in this case, their familiarity with Disney productions and/or with García Márquez’s literary universe. We examine how Norwegian reviewers received the film, particularly with regard to Disney conventions, such as magical elements, and the representation of female characters.

Challenges and solutions of environmental scanning electron microscopy characterisation of biomaterials: Application to hygro‐expansion of paper

AbstractMost methodologies to measure the moisture‐induced deformation (hygro‐expansion) of paper microconstituents, including fibres and interfibre bonds, are low resolution or time‐consuming. Hence, here, a novel method is proposed and validated to measure high‐resolution full‐field strain maps of paper microconstituents during hygro‐expansion, based on environmental scanning electron microscopy (ESEM). To this end, a novel climate stage enables accurate control of the relative humidity (RH) near the specimen in the ESEM from 0%–100%. The fibre surface, which is decorateda prioriwith a microparticle pattern, is captured during RH change. Subsequently, correlating the fibre surface using a dedicated global digital image correlation algorithm enables high‐resolution hygro‐expansion strain maps. Method optimisation involved performing contrast enhancement, scan‐correction to reduce ESEM artefacts and a background correction, resulting in a strain resolution of . Method validation revealed that the fibres' crystallinity is affected by the electron beam, even for minimal invasive electron beam settings. Interestingly, however, the fibres consistently exhibit conventional hygro‐expansion behaviour during the drying slopes. Using the optimised procedure, hygro‐expansion characterisation of two interfibre bonds and four interfibre bond cross‐sections revealed the competition between the low longitudinal and large transverse fibre hygro‐expansion in the bonded area.

Opportunities and challenges of trilateral South‒South cooperation for transforming development assistance for health: evidence from a DRC–UNICEF–China maternal, newborn, and child health project

BackgroundTrilateral South‒South cooperation is envisioned as an equal and empowering partnership model but still faces certain challenges. This study addresses whether and how trilateral South‒South cooperation can transform traditional development assistance for health (DAH) and explores the opportunities and challenges of trilateral South‒South cooperation for transforming future DAH, in the theme of “the emerging development partner’s DAH transformation facilitated by a multilateral organization”.MethodsWe evaluate a maternal, newborn, and child health (MNCH) project involving the Democratic Republic of Congo (DRC), the United Nations Children’s Fund (UNICEF), and China (hereinafter referred to as the “DRC–UNICEF–China project”). We analyze data from project documents and seventeen semi-structured interviews using a pragmatic analytical framework based on the DAH program logic model and the OECD’s trilateral cooperation framework.ResultsEvidence from the DRC–UNICEF–China MNCH project suggests that trilateral South‒South cooperation facilitated by a multilateral organization can provide transformative opportunities for emerging development partners’ DAH to generate and deliver context-based, demand-oriented solutions, harmonize rules and procedures, institutionalize mutual learning and knowledge sharing, and increase the visibility of emerging development partners as sources for South‒South development experience transfer. However, the project revealed some challenges, including the neglect of key stakeholders in the complex governance structure, the high transaction costs needed to ensure transparency, and the harm local absence of the emerging development partner poses to long-term DAH engagement.ConclusionsThis study echoes some of the findings in trilateral SSC literature that claim power structures and philanthropic, normative justification for health equity are often juxtaposed in trilateral SSC partnerships. The opportunities offered by the DRC–UNICEF–China project align with China’s cognitive learning process for strengthening international engagement and global image building. However, challenges may arise as a result of complex governance structures and the entrustment of facilitating partners, which can threaten the effectiveness of trilateral cooperation. We call for strengthening the beneficiary partner’s ownership at all levels, engaging the emerging development partner to better understand the beneficiary partner’s local context(s) and needs, and ensuring available resources to support programmatic activities and long-term partnerships for the health and well-being of the beneficiaries.

A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas

ObjectivesDentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for periapical granulomas. Therefore, an automated tool to aid clinical decision making is needed. MethodsA deep learning framework was developed using panoramic images of 80 radicular cysts and 72 periapical granulomas located in the mandible. Additionally, 197 normal images and 58 images with other radiolucent lesions were selected to improve model robustness. The images were cropped into global (affected half of the mandible) and local images (only the lesion) and then the dataset was split into 90% training and 10% testing sets. Data augmentation was performed on the training dataset. A two-route convolutional neural network using the global and local images was constructed for lesion classification. These outputs were concatenated into the object detection network for lesion localization. ResultsThe classification network achieved a sensitivity of 1.00 (95% C.I. 0.63–1.00), specificity of 0.95 (0.86–0.99), and AUC (area under the receiver-operating characteristic curve) of 0.97 for radicular cysts and a sensitivity of 0.77 (0.46–0.95), specificity of 1.00 (0.93–1.00), and AUC of 0.88 for periapical granulomas. Average precision for the localization network was 0.83 for radicular cysts and 0.74 for periapical granulomas. ConclusionsThe proposed model demonstrated reliable diagnostic performance for the detection and differentiation of radicular cysts and periapical granulomas. Using deep learning, diagnostic efficacy can be enhanced leading to a more efficient referral strategy and subsequent treatment efficacy. Clinical significanceA two-route deep learning approach using global and local images can reliably differentiate between radicular cysts and periapical granulomas on panoramic imaging. Concatenating its output to a localizing network creates a clinically usable workflow for classifying and localizing these lesions, enhancing treatment and referral practices.

Health-related quality of life and use of medication with anticholinergic activity in patients with multiple myeloma.

Verify the association between anticholinergic burden and health-related quality of life of patients with multiple myeloma. Cross-sectional study with multiple myeloma outpatient from a state capital city in southeastern Brazil. Sociodemographic, clinical, and pharmacotherapeutic variables were collected by interview. Clinical data were complemented by medical records. Drugs with anticholinergic activity were identified with Brazilian Anticholinergic Activity Drug Scale. Health-related quality of life scores were obtained using QLQ-C30 and QLQ-MY20 instruments. Mann-Whitney was used to compare the median of the health-related quality of life scale scores and the independent variables. Multivariate linear regression was performed to verify the association between independent variables and health-related quality of life scores. Two hundred thirteen patients were included, 56.3% had multi-morbidities, and 71.8% used polypharmacy. In all health-related quality of life domains, there were differences between the medians of the polypharmacy variable. A significant difference was identified between the ACh burden and QLQ-C30 and QLQ-MY20 scores. Linear regression identified an association between the use of drugs with anticholinergic activity and the reduction of global status scores (QLQ-C30), functional scale (QLQ-C30), body image (QLQ-MY20), and future perspective (QLQ-MY20). Drugs with anticholinergic activity were associated with increased symptom scores (QLQ-C30 and QLQ-MY20). Polypharmacy was associated with reduction of functioning score and increase of symptom score (QLQ-C30). Anticholinergic burden in MM patients is associated with lower scores in quality of life domains: global health and symptoms (QLQ-C30) and functional (QLQ-C30 and QLQ-MY20). The presence of polypharmacy is also associated with lower scores for functional scales and symptom scales (QLQ-C30).