Target Segment Research Articles

Deep learning-based multimodal integration of imaging and clinical data for predicting surgical approach in percutaneous transforaminal endoscopic discectomy.

For cases of multilevel lumbar disc herniation (LDH), selecting the surgical approach for Percutaneous Transforaminal Endoscopic Discectomy (PTED) presents significant challenges and heavily relies on the physician's judgment. This study aims to develop a deep learning (DL)-based multimodal model that provides objective and referenceable support by comprehensively analyzing imaging and clinical data to assist physicians. This retrospective study collected imaging and clinical data from patients with multilevel LDH. Each segmental MR scan was concurrently fed into a multi-input ResNet 50 model to predict the target segment. The target segment scan was then input to a custom model to predict the PTED approach direction. Clinical data, including the patient's lower limb sensory and motor functions, were used as feature variables in a machine learning (ML) model for prediction. Bayesian optimization was employed to determine the optimal weights for the fusion of the two models. The predictive performance of the multimodal model significantly outperformed the DL and ML models. For PTED target segment prediction, the multimodal model achieved an accuracy of 93.8%, while the DL and ML models achieved accuracies of 87.7% and 87.0%, respectively. Regarding the PTED approach direction, the multimodal model had an accuracy of 89.3%, significantly higher than the DL model's 87.8% and the ML model's 87.6%. The multimodal model demonstrated excellent performance in predicting PTED target segments and approach directions. Its predictive performance surpassed that of the individual DL and ML models.

Promoting camouflaged object detection through novel edge-target interaction and frequency-spatial fusion

The realm of camouflage object detection endeavors to precisely discern concealed targets that blend seamlessly into their surroundings. Recently, numerous research endeavors have converged on unraveling the disguise tactics employed by such objects, striving for efficient and accurate detection results. While these methods have garnered notable advancements in unveiling camouflaged targets, they still grapple with challenges posed by the high similarity between objects and their backgrounds, resulting in misidentifications, detection oversights, and the loss of intricate details. In light of these challenges, this paper introduces an innovative EFNet, aimed at elevating the integrity of target recognition and the finesse of detail capture through deepened interactions between Edge-target features and Frequency-spatial information. Notably, recognizing that the edges of camouflaged objects harbor a wealth of intrinsic details and serve as crucial delimiters distinguishing them from their surroundings, we have devised a dual-path architecture, where an auxiliary Edge Detection (ED) branch collaborates seamlessly with the primary Object Segmentation (OS) branch. Within the OS branch, we meticulously crafted the Edge-induced Segmentation Refinement module (ESR), which ingeniously harnesses the refined edge information provided by the ED branch to bolster target segmentation accuracy and augment detail fidelity. Moreover, in the supporting ED branch, we propose the Structure-induced Edge Enhancement module (SEE), designed to accentuate edge information while suppressing irrelevant noise with the assistance of the OS branch, thereby ensuring the precise extraction of edge information. To further enrich the information on dual-path, we innovatively introduce a Frequency Decomposition Module (FDM), which decomposes images into low- and high-frequency components, tailored to enhance target segmentation and edge detection, respectively. Additionally, to ensure seamless integration of frequency-domain and spatial-domain information, we devised the versatile Frequency-Spatial Domain Mixer (FSDM), achieving precise information alignment and fusion of these two domains. Quantitative and qualitative experimental results demonstrate that our proposed EFNet significantly outperforms existing state-of-the-art methods on four widely used benchmark datasets.

Are Argentinians ready for plant-based meat alternatives? A case study on awareness and willingness for consumption.

Meat alternatives' popularity is increasing worldwide. Nevertheless, the attitudes and motivations underpinning their adoption still evolve and may differ by region. In Argentina, where meat consumption per capita is among the highest globally, the market for plant-based meat alternatives (PBMAs) is just beginning to develop. Few reports attempt to explain the behavior of the Argentinian population regarding these products. This study explored the knowledge, attitudes, and perceptions of meat analogues and hybrid products through a national online survey (N=2339). Also, barriers and drivers to their consumption as well as the target segments were identified. Participants were classified as omnivores 73.8%, flexitarians 19.5%, vegetarians 6%, and vegans 0.7%. Despite the significant lack of awareness about PBMAs, there is a strong willingness to adopt them, with females, younger respondents, and those with lower incomes more open to these options. The main drivers for consumption were health concerns, animal welfare, and environmental awareness. Familiarity was a key factor influencing the adoption, as evidenced by the openness of well-informed omnivores. Lack of available options represented the main limitation for consumption and distrust regarding their origin and sensory appeal were the primary reasons for rejection. This study reveals a potential market for PBMAs in Argentina, highlighting that guaranteeing nutritional and sensory quality is crucial. Educating consumers is also essential for their widespread acceptance.

BGFE-Net: A Boundary-Guided Feature Enhancement Network for segmentation of targets with fuzzy boundaries

BGFE-Net: A Boundary-Guided Feature Enhancement Network for segmentation of targets with fuzzy boundaries

CGNet: Few-shot learning for Intracranial Hemorrhage Segmentation.

In recent years, with the increasing attention from researchers towards medical imaging, deep learning-based image segmentation techniques have become mainstream in the field, requiring large amounts of manually annotated data. Annotating datasets for Intracranial Hemorrhage(ICH) is particularly tedious and costly. Few-shot segmentation holds significant potential for medical imaging. In this work, we designed a novel segmentation model CGNet to leverage a limited dataset for segmenting ICH regions, we propose a Cross Feature Module (CFM) enhances the understanding of lesion details by facilitating interaction between feature information from the query and support sets and Support Guide Query (SGQ) refines segmentation targets by integrating features from support and query sets at different scales, preserving the integrity of target feature information while further enhancing segmentation detail. We first propose transforming the ICH segmentation task into a few-shot learning problem. We evaluated our model using the publicly available BHSD dataset and the private IHSAH dataset. Our approach outperforms current state-of-the-art few-shot segmentation models, outperforming methods of 3% and 1.8% in Dice coefficient scores, respectively, and also exceeds the performance of fully supervised segmentation models with the same amount of data.

Image Navigation System for Thoracoscopic Surgeries Driven by Nuclear Medicine Utilizing Channel R-CNN

Breast cancer, a prevalent and significant cause of cancer-related mortality in women, often necessitates precise detection through nuclear medicine techniques. Despite the utility of computer-aided navigation in thoracoscopic surgeries like mastectomy, challenges persist in accurately locating and tracking target tissues amidst intricate surgical scenarios. This study introduces a novel system employing a channel R-CNN model to automatically segment target regions in thoracoscopic images and provide precise cutting curve indications for surgeons. By integrating a Detection Network Head and Thorax Network Head, this multi-channel framework outperforms existing single-task models, marking a pioneering effort in cutting curve indication for thoracoscopic procedures. Utilizing a specialized dataset, the model achieves a notable region segmentation mIOU of 79.4% and OPA of 83.2%. In cutting path planning, it attains an mIOU of 68.6% and OPA of 77.5%. The system operates at an average speed of 23.6 frames per second in videos, meeting the real-time response needs of surgical navigation systems. This research underscores the potential of advanced imaging and AI-driven solutions in enhancing precision and efficacy in thoracoscopic surgeries.

AI model for automatic 3D reconstruction of ossicular chain and bony labyrinth from High-Resolution CT

Abstract Background Three-dimensional (3D) reconstruction of ossicular chain and bony labyrinth based on temporal bone high resolution computed tomography (HRCT) is useful for diagnosis and treatment guidance of middle and inner ear diseases. However, these structures are small and irregular, making manual reconstruction time-consuming. Purpose To develop and validate an artificial intelligence (AI) model based on semi-supervised learning for automated 3D reconstruction of ossicular chain and bony labyrinth on HRCT images. Methods HRCT images from 304 ears of consecutive 152 patients retrospectively collected from a single center were randomly divided into training (246 ears), validation (28 ears) and internal test (30 ears) cohorts for model development. A novel semi-supervised ear bone segmentation framework was used to train the AI model, and its performance was evaluated by Dice similarity coefficients. The trained algorithm was applied to a temporally independent test dataset of 30 ears of 15 patients from the same center for comparison with manual 3D reconstruction for processing time, target volume and visual assessment of segmentation. Results The AI model demonstrated a Dice score of 0.948 (95% CI: 0.940, 0.955) for the internal and 0.979 (95% CI: 0.973, 0.986) for the temporally independent test sets. In the latter dataset, the AI model required 2% or less processing time of manual 3D reconstruction for each ear (17.7 seconds ± 10.1 vs 1080.5 seconds ± 149.8; P ＜ .001), and had an accuracy comparable to human experts in the volume and visual assessment of segmentation targets (P = .237-1.000). In a subgroup analysis, the model achieved accurate segmentation (Dice scores of 0.98-0.99) across various diseases (e.g. otitis media, mastoiditis, otosclerosis, middle and inner ear malformations, and Ménière’s disease). Conclusion The AI model enables robust, efficient and accurate 3D reconstruction for the small structures such as ossicular chain and bony labyrinth on HRCT images.

A vision model for automated frozen tuna processing

Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing. TunaVision incorporates enhancements in instance segmentation through YOLOv8m-FusionSeg, improving the segmentation of small and complex targets by increasing channel depth and optimizing feature fusion. Additionally, the YOLOv8s RSF model improves feature extraction speed and accuracy, ensuring each fish is correctly identified and localized before segmentation and pose estimation. Furthermore, TunaVision employs a vector-based approach for pose estimation, utilizing detection and segmentation results to determine fish posture and orientation. Experiments show that YOLOv8m-FusionSeg achieves an mAP@0.5 of 93.3%, while YOLOv8s RSF achieves an mAP@0.5 of 96.1%, with a mean absolute error (MAE) of 1.81 degrees in angle estimation, significantly outperforming other methods. These findings highlight TunaVision’s effectiveness in segmenting, detecting, and estimating poses of frozen tuna, offering valuable insights for the development of automated processing systems.

Gyümölcsöt ajándékba? A zöldség-gyümölcs ajándékozás lehetséges aspektusai

Giving fruit and vegetables as gifts is not a widespread custom in Hungary, while a healthy lifestyle is always considered a good buzzword. While abroad, special vegetable and fruit baskets already offer unique gift options, at home the classic books, chocolates, flowers, and the provision of services and experiences are preferred. Some special fruits are sold for millions on the market, but there are also cheaper, yet unique products. Although the gifting of fruit and vegetables has historical traditions all over the world, nowadays their sale as gifts has declined. Consumers who are environmentally and health-conscious, open to a green lifestyle, or just curious could be realistic target segments for this niche area, which could be brought to success with an appropriate marketing strategy.JEL codes: I12, M31, Q13

Innovative segmentation technique for aerial power lines via amplitude stretching transform

Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power system.The aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected. The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). The average values of evaluation indicators PPA, MMPA and MMIoU of the image segmentation results of the proposed algorithm reach 0.96, 0.96 and 0.95 respectively, and the average time lag of detection is less than 0.2s, indicating that the accuracy and real-time performance of the segmentation results of the proposed algorithm are significantly better than those of the above algorithms.

Political ideology drives the integration of country-of-origin and offshoring sustainability impact information toward globally prosocial product decisions

Purpose We investigate how white Western consumers make prosocial decisions on domestically branded products manufactured in countries with a developing economy (CDEs). Citizens in these countries manufacture products routinely sold in Western markets under Western companies and brands as part of an offshore supply chain. Design/methodology/approach We propose that prosocial decision-making about these products can be traced to an interactive effect between consumers’ perceptions of the social sustainability impact of offshoring on CDE nations and social dominance orientation (SDO)-Dominance (SDO-D), an ideological preference for higher-status groups to forcefully oppress lower-status groups. In three studies with white Western consumers, this paper measured SDO-D, measured or experimentally manipulated the harmful versus beneficial impact of offshoring on CDEs, and presented assessed participants’ willingness to purchase domestically branded and CDE-manufactured products across a series of decision trials. Findings White Western consumers with low SDO-D prosocially displayed weaker purchase intentions toward products with a domestic brand manufactured in a CDE (“DCDE products”) when offshoring was perceived as harming those countries, and stronger intentions when offshoring was perceived as beneficial. This effect was mediated by consumers’ trust in domestic firms to treat workforce issues appropriately. Conversely, high SDO-Ds’ intentions were unaffected by the perceived harmful or beneficial impact of offshoring. Research limitations/implications Future work in this area will ideally use field purchasing situations while testing for brand familiarity – as a covariate or moderator. Practical implications Firms that market or sell these products are advised to understand if their target segments have lower or higher levels of SDO-D, and how they evaluate the impact of the offshored supply chain on CDE citizens. Our research also suggests Western low SDO-Ds attributed the responsibility of foreign worker treatment to their own domestic companies, rather than foreign governments or offshoring agencies, when CDE workers were harmed by offshoring. Originality/value These findings have broad implications for the various markets and policies behind the sale and positioning of DCDE products and help identify the consumers who demand socially sustainable supply chains and offshore operations.

Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images

Drainage pipeline construction projects are vulnerable to a range of defects, such as branch concealed joints, variable diameter, two pipe mouth significances, foreign object insertion, pipeline rupture, and pipeline end disconnection, generated during long-term service in a complex environment. This paper proposes two enhancements to multiple attention learning to detect and segment multiple defects. Firstly, we collected numerous samples of drainage pipeline sonar defect videos. Then, our multiple attention segmentation network was used for target segmentation. The test precision and accuracy of MAP@50 reach 96.0% and 90.9%, respectively, in the segmentation prediction. Compared to the coordinate attention and convolutional block attention module attention models, it had a significant precision advantage, and the weight file size is merely 7.0 MB, which is far smaller than the Yolov9 model segmentation weight size. The multiple attention method proposed in this paper was adopted for detection, instance segmentation, and pose detection in different public datasets, especially in the object detection of the coco128-seg dataset under the same condition. Map@50:95 has increased by 13.0% assisted by our multiple attention mechanism. The results indicated the memory efficiency and high precision of the integration of the multiple attention model on several public datasets.

AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net.

The task of nucleus segmentation plays an important role in medical image analysis. However, due to the challenge of detecting small targets and complex boundaries in datasets, traditional methods often fail to achieve satisfactory results. Therefore, a novel nucleus segmentation method based on the U-Net architecture is proposed to overcome this issue. Firstly, we introduce a Weighted Feature Enhancement Unit (WFEU) in the encoder decoder fusion stage of U-Net. By assigning learnable weights to different feature maps, the network can adaptively enhance key features and suppress irrelevant or secondary features, thus maintaining high-precision segmentation performance in complex backgrounds. In addition, to further improve the performance of the network under different resolution features, we designed a Double-Stage Channel Optimization Module (DSCOM) in the first two layers of the model. This DSCOM effectively preserves high-resolution information and improves the segmentation accuracy of small targets and boundary regions through multi-level convolution operations and channel optimization. Finally, we proposed an Adaptive Fusion Loss Module (AFLM) that effectively balances different lossy targets by dynamically adjusting weights, thereby further improving the model's performance in segmentation region consistency and boundary accuracy while maintaining classification accuracy. The experimental results on 2018 Data Science Bowl demonstrate that, compared to state-of-the-art segmentation models, our method shows significant advantages in multiple key metrics. Specifically, our model achieved an IOU score of 0.8660 and a Dice score of 0.9216, with a model parameter size of only 7.81 M. These results illustrate that the method proposed in this paper not only excels in the segmentation of complex shapes and small targets but also significantly enhances overall performance at lower computational costs. This research offers new insights and references for model design in future medical image segmentation tasks.

The propagation laws of hydraulic fractures under the influence of natural fracture zones

This paper uses the finite-discrete element method (FDEM) to establish a fluid–solid coupling model for the propagation of multi-cluster hydraulic fractures. The validity of the proposed model is verified using the analytical solution and onsite microseismic data. The results show that with the approach angle increases, the propagation of hydraulic fracture transitions from a single capture mode to the mixed modes of capture, crossing and blocking. The total length of hydraulic fractures is negatively correlated with the approach angle and cohesion of natural fracture, and positively correlated with the distribution density and bandwidth of natural fracture. The length of shear fractures initially increases and then decreases with the increase in approach angle, showing negatively, positively, and positively correlated with cohesion, length, and bandwidth, respectively, while the trend of tensile fracture length is opposite to it. The tortuosity of hydraulic fractures (which is used to characterize the complexity of fracture morphology) initially decreases and then increases with the increase in the approach angle, negatively correlated with the cohesion of natural fracture, and positively correlated with the distribution density and bandwidth of natural fractures. Comparatively, the fracture propagation morphology is the most complex at the approach angle of 45°, which is more conducive to full reservoir enhancement. Conversely, under conditions of high cohesion, narrow width, and low-density distribution of natural fractures, the fracture morphology is relatively simple, all of which are unfavorable for reservoir enhancement in the target segment.

Automated Patient-specific Quality Assurance for Automated Segmentation of Organs at Risk in Nasopharyngeal Carcinoma Radiotherapy.

Precision radiotherapy relies on accurate segmentation of tumor targets and organs at risk (OARs). Clinicians manually review automatically delineated structures on a case-by-case basis, a time-consuming process dependent on reviewer experience and alertness. This study proposes a general process for automated threshold generation for structural evaluation indicators and patient-specific quality assurance (QA) for automated segmentation of nasopharyngeal carcinoma (NPC). The patient-specific QA process for automated segmentation involves determining the confidence limit and error structure highlight stage. Three expert physicians segmented 17 OARs using computed tomography images of NPC and compared them using the Dice similarity coefficient, the maximum Hausdorff distance, and the mean distance to agreement. For each OAR, the 95% confidence interval was calculated as the confidence limit for each indicator. If two or more evaluation indicators (N2) or one or more evaluation indicators (N1) exceeded the confidence limits, the structure segmentation result was considered abnormal. The quantitative performances of these two methods were compared with those obtained by artificially introducing small/medium and serious errors. The sensitivity, specificity, balanced accuracy, and F-score values for N2 were 0.944 ± 0.052, 0.827 ± 0.149, 0.886 ± 0.076, and 0.936 ± 0.045, respectively, whereas those for N1 were 0.955 ± 0.045, 0.788 ± 0.189, 0.878 ± 0.096, and 0.948 ± 0.035, respectively. N2 and N1 had small/medium error detection rates of 97.67 ± 0.04% and 98.67 ± 0.04%, respectively, with a serious error detection rate of 100%. The proposed automated patient-specific QA process effectively detected segmentation abnormalities, particularly serious errors. These are crucial for enhancing review efficiency and automated segmentation, and for improving physician confidence in automated segmentation.

Application of Decomposition Expression in Digital Video Object Segmentation

In the field of actual Video Object Segmentation (VOS), traditional techniques have poor adaptability and insufficient segmentation results. Therefore, based on existing problems, an Unsupervised Video Object Segmentation (UVOS) technique based on convolutional networks is proposed. Firstly, the method of decomposing expressions is used to handle the spatiotemporal relationship between the reference frame and the target frame, and video object reconstruction is achieved through similarity calculation. For target segmentation in motion scenes, a Single Linear Bottleneck Operator (SLBO) is introduced for feature extraction, and pooling compensation is used to optimize feature information loss. For general scene segmentation, a spatiotemporal similarity segmentation technique is introduced to achieve target video segmentation for complex scenes. In the foreground segmentation test of sports scenes, the Change Detection Benchmark Dataset 2014 (CDNet.20I4SM) dataset was selected to test the model's loss performance in different scenarios. In adverse weather scenario training, the proposed model tends to converge after 40 iterations, with a loss value of 0.276, which is superior to the Foreground image Segmentation (FgSegNet_), the Convolutional Networks for Biomedical Image Segmentation (MU Net), Cascade Convolutional Neural Network (Cascade CNN) models; In the accuracy test, the proposed FS-LBPC model tended to converge after 50 iterations, with a precision P-value of 0.963. It performed the best among the four segmentation models the FgSegNet_, MU Net, Cascade CNN, and a real-time Foreground Segmentation network based on single Linear Bottleneck and Pooling Compensation (FS-LBPC). Usually, the Densely Annotated VIdeo Segmentation (DAVIS16) dataset is selected for video scene segmentation, which has the best segmentation performance in horse racing and animal flight scenes, with segmentation accuracy of 0.976 and 0.965, respectively. In summary, the VOS technology has excellent application effects in practical scenarios, providing important technical references for the improvement of image and video processing and segmentation technology

The value of semiquantitative analysis of [99mTc]MDP SPECT/CT in localizing responsible lesions for suspected fresh osteoporotic vertebral compression fractures in patients with contraindications to MRI.

The aim of this study was to evaluate the effectiveness of single-photon emission computed tomography/computed tomography (SPECT/CT) in detecting and localizing the causative vertebra in cases of suspected fresh osteoporotic vertebral compression fractures (OVCFs) in patients with contraindications to MRI. A total of 21 patients with severe back pain with 31 suspected OVCF segments and contraindications to MRI were initially identified through radiographs and the back pain-inducing test (BPIT). The responsible vertebral bodies were determined using [99mTc]MDP SPECT/CT before percutaneous vertebroplasty (PVP). Clinical outcomes and radiographic parameters, including basic demographics, injury duration, focal kyphosis (FK), loss of vertebral height (LVH), and relative intensity values (RIVs) for target and nontarget segments in the SPECT/CT were measured. SPECT/CT identified 14 old and 26 fresh OVCFs. Of these, 18 patients with 26 fresh OVCF segments underwent PVP, and achieved satisfactory outcomes. Significant differences were observed in the RIV, FK, and LVH values between the fresh and old OCVF groups (all p < 0.05). RIVs showed a significant negative correlation with injury duration (r = -0.57, p < 0.05), and a positive correlation with LVH (r = 0.43, p < 0.05). Multiple linear regression analysis confirmed that injury duration was an independent predictor of RIVs (p < 0.05). SPECT/CT is useful for detecting and differentiating fresh fractures from old fractures in patients having OVCF who cannot undergo MRI after BPIT validation, allowing these patients to achieve excellent clinical outcomes following PVP. The RIV serves as a valuable parameter for assessing the duration of fresh versus old OVCFs. ChiCTR, ChiCTR 2300077570. Retrospectively registered, http://www.chictr.org.cn/showproj.html?proj=206192.

INTEGRATION OF DIGITAL TECHNOLOGIES INTO MARKETING PROCESSES: CHALLENGES AND PROSPECTS

This article examines the challenges and prospects of integrating digital technologies into marketing processes, a critical topic given the increasing competitiveness of the global market. The study underscores the importance of developing effective strategies for adopting digital tools in marketing to retain existing customers, attract new target segments, and ensure long-term business growth. Additionally, it explores the potential of digital transformation in marketing to provide practical recommendations for optimizing resource use and enhancing profitability. By analyzing both theoretical frameworks and practical applications, the research highlights the dual significance of this topic: advancing academic understanding and offering actionable insights for improving organizational competitiveness in dynamic market environments. The findings of the study reveal that digital technologies, encompassing big data, AI, cloud computing, mobile applications, social media, the Internet of Things (IoT), and blockchain, offer multifaceted benefits for marketing. These benefits range from operational optimization and cost reduction to increased productivity and competitive positioning. The ability to automate routine processes reduces reliance on manual labor, accelerates order processing, and improves inventory management. Additionally, personalized customer interaction becomes feasible through advanced data analytics, enabling businesses to tailor marketing strategies to specific audience segments, thereby enhancing customer loyalty and boosting sales. A significant focus of the study is on the integration of digital tools with traditional marketing methods to achieve synergy rather than replacement. This approach ensures a balanced evolution of marketing strategies, leveraging the strengths of both digital and conventional techniques. The ethical and privacy implications of big data utilization also demand attention, requiring a nuanced approach to safeguard consumer trust while exploiting data-driven opportunities. In conclusion, this research highlights the critical role of digital technology integration in transforming marketing practices. By leveraging innovative tools and strategies, businesses can improve operational efficiency, enhance customer experiences, and secure a competitive advantage in the digital age. The insights presented here offer valuable guidance for academics and practitioners aiming to navigate the complexities of digital transformation in marketing.

Research of the role of charitable foundations in the provision of medicines to the population

Introduction. Despite the positive developments in providing patients with orphan diseases, achieved through the implementation of a set of federal and regional programs, it has not yet been possible to achieve full and timely satisfaction of the target segment's need for medicines, and patients are forced to look for additional sources of funding for drug therapy, one of which is the budgets of charitable foundations. Objective: research the role and place of charitable foundations in the system of drug provision for patients with orphan diseases. Material and methods. Сontent analysis of legislative and regulatory acts of the Russian Federation in the field of healthcare and circulation of medicines, open data from the official website of the All-Russian Society of Orphan Diseases, statistical data of the Ministry of Health of the Russian Federation, open official reports of the Circle of Good Foundation and official websites of 57 charitable foundations. Results and discussion. The elaboration of mechanisms for including charitable foundations in the list of organizations that have the right to work with narcotic drugs and psychotropic substances in exceptional cases in terms of patients with orphan diseases seems to be another way to improve the quality of drug provision for privileged categories of patients. Conclusion. The elaboration of mechanisms for including charitable foundations in the list of organizations that have the right to work with narcotic drugs and psychotropic substances in exceptional cases in terms of patients with orphan diseases seems to be another way to improve the quality of drug provision for privileged categories of patients.

Unsupervised Bayesian generation of synthetic CT from CBCT using patient-specific score-based prior.

Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings. This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT. The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics, including mean absolute error (MAE), non-uniformity (NU), and structural similarity index measure (SSIM). Additionally, the proposed algorithm was benchmarked against other unsupervised learning-based CBCT correction algorithms. The proposed method significantly reduced various kinds of CBCT artifacts in the studies of H&N, pancreatic, and lung cancer patients. In the lung stereotactic body radiation therapy (SBRT) patient study, the MAE, NU, and SSIM were improved from 47 HU, 45 HU, and 0.58 in the original CBCT images to 13 HU, 14dB, and 0.67 in the generated sCT images. Compared to other unsupervised learning-based algorithms, the proposed method demonstrated superior performance in artifact reduction. The proposed unsupervised method can generate sCT from CBCT with reduced artifacts and precise HU values, enabling CBCT-guided segmentation and replanning for online ART.