Beyond microroughness: novel approaches to navigate osteoblast activity on implant surfaces.

Considering the biological activity of osteoblasts is crucial when devising new approaches to enhance the osseointegration of implant surfaces, as their behavior profoundly influences clinical outcomes. An established inverse correlation exists between osteoblast proliferation and their functional differentiation, which constrains the rapid generation of a significant amount of bone. Examining the surface morphology of implants reveals that roughened titanium surfaces facilitate rapid but thin bone formation, whereas smooth, machined surfaces promote greater volumes of bone formation albeit at a slower pace. Consequently, osteoblasts differentiate faster on roughened surfaces but at the expense of proliferation speed. Moreover, the attachment and initial spreading behavior of osteoblasts are notably compromised on microrough surfaces. This review delves into our current understanding and recent advances in nanonodular texturing, meso-scale texturing, and UV photofunctionalization as potential strategies to address the "biological dilemma" of osteoblast kinetics, aiming to improve the quality and quantity of osseointegration. We discuss how these topographical and physicochemical strategies effectively mitigate and even overcome the dichotomy of osteoblast behavior and the biological challenges posed by microrough surfaces. Indeed, surfaces modified with these strategies exhibit enhanced recruitment, attachment, spread, and proliferation of osteoblasts compared to smooth surfaces, while maintaining or amplifying the inherent advantage of cell differentiation. These technology platforms suggest promising avenues for the development of future implants.

ConcVAE: Conceptual Representation Learning.

Disentangled representation learning aims at obtaining an independent latent representation without supervisory signals. However, the independence of a representation does not guarantee interpretability to match human intuition in the unsupervised settings. In this article, we introduce conceptual representation learning, an unsupervised strategy to learn a representation and its concepts. An antonym pair forms a concept, which determines the semantically meaningful axes in the latent space. Since the connection between signifying words and signified notions is arbitrary in natural languages, the verbalization of data features makes the representation make sense to humans. We thus construct Conceptual VAE (ConcVAE), a variational autoencoder (VAE)-based generative model with an explicit process in which the semantic representation of data is generated via trainable concepts. In visual data, ConcVAE utilizes natural language arbitrariness as an inductive bias of unsupervised learning by using a vision-language pretraining, which can tell an unsupervised model what makes sense to humans. Qualitative and quantitative evaluations show that the conceptual inductive bias in ConcVAE effectively disentangles the latent representation in a sense-making manner without supervision. Code is available at https://github.com/ganmodokix/concvae.

Factors associated with an improvement in extracellular water-to-total body water ratio in older adults with hip fractures: A decision tree analysis.

BACKGROUND & AIMS: The extracellular water-to-total body water ratio (ECW/TBW) increases with age and after fractures. A high ECW/TBW may hinder improvements in physical function and skeletal muscle mass. However, the effects of ECW/TBW improvement have not been properly investigated. The aim of this study was to investigate the factors associated with ECW/TBW improvement in older adults with hip fractures. METHODS: This retrospective cohort study included 203 patients with hip fractures who were admitted to a convalescent rehabilitation ward. ECW/TBW and skeletal muscle mass index (SMI) were measured using bioelectrical impedance analysis. The patients were classified into two groups: those with an improvement in ECW/TBW (n = 123) and those without an improvement (n = 80). Decision tree analysis was performed to examine the factors associated with ECW/TBW improvement. As a secondary objective, a multiple regression analysis was performed to identify the factors associated with SMI gain. RESULTS: Decision tree analysis identified rehabilitation volume and protein intake as the first and second factors most significantly associated with an improvement in ECW/TBW, respectively. Multiple regression analysis showed that improved ECW/TBW (ß: 0.400, p < 0.001) was significantly associated with SMI gain. CONCLUSIONS: Rehabilitation volume and protein intake are clinically important for improving ECW/TBW in older adults with hip fractures.

Nanofeatured surfaces in dental implants: contemporary insights and impending challenges.

Dental implant therapy, established as standard-of-care nearly three decades ago with the advent of microrough titanium surfaces, revolutionized clinical outcomes through enhanced osseointegration. However, despite this pivotal advancement, challenges persist, including prolonged healing times, restricted clinical indications, plateauing success rates, and a notable incidence of peri-implantitis. This review explores the biological merits and constraints of microrough surfaces and evaluates the current landscape of nanofeatured dental implant surfaces, aiming to illuminate strategies for addressing existing impediments in implant therapy. Currently available nanofeatured dental implants incorporated nano-structures onto their predecessor microrough surfaces. While nanofeature integration into microrough surfaces demonstrates potential for enhancing early-stage osseointegration, it falls short of surpassing its predecessors in terms of osseointegration capacity. This discrepancy may be attributed, in part, to the inherent "dichotomy kinetics" of osteoblasts, wherein increased surface roughness by nanofeatures enhances osteoblast differentiation but concomitantly impedes cell attachment and proliferation. We also showcase a controllable, hybrid micro-nano titanium model surface and contrast it with commercially-available nanofeatured surfaces. Unlike the commercial nanofeatured surfaces, the controllable micro-nano hybrid surface exhibits superior potential for enhancing both cell differentiation and proliferation. Hence, present nanofeatured dental implants represent an evolutionary step from conventional microrough implants, yet they presently lack transformative capacity to surmount existing limitations. Further research and development endeavors are imperative to devise optimized surfaces rooted in fundamental science, thereby propelling technological progress in the field.

Sarcopenia and its components suppress the improvement of depression symptoms in patients with stroke.

Background: Recent studies have reported an association between sarcopenia and depression symptoms. To date, no reports have investigated the association between sarcopenia and depression symptoms evaluated using the Geriatric Depression Screening Scale (GDS)-15 in patients with stroke. Therefore, this study aimed to investigate the association between sarcopenia and its components and the improvement of depression symptoms in patients with stroke admitted to a convalescent rehabilitation ward. Methods: Patients with stroke aged ≥65 years admitted to a convalescent rehabilitation ward were included in the study. Participants were categorized into sarcopenia and non-sarcopenia groups based on the 2019 Asian Working Group for Sarcopenia. Here, depression symptoms were evaluated using the GDS-15, in addition to demographic characteristics. This study's primary outcome was the GDS change from admission to discharge. Multiple regression analysis was performed to investigate the association between GDS change and sarcopenia and its components. Results: Overall, 118 participants were included, with a mean age of 78.7±8.1, and 58 (49%) were classified in the sarcopenia group. Multiple regression analysis showed that sarcopenia (ß: -0.283, 95% confidence interval [CI]: -1.140 to -0.283, p < 0.001) and handgrip strength (ß: -0.317, 95% CI: -0.162 to -0.014, p = 0.021) were independently associated with GDS change. Conclusion: Sarcopenia and handgrip strength were significantly associated with improved depression symptoms in patients with stroke admitted to a convalescent rehabilitation ward. However, further prospective studies should investigate the association between sarcopenia and depression symptoms in patients with stroke.

Reduced trunk muscle mass in patients with stroke is associated with reduced balance function recovery.

BACKGROUND & AIMS: Skeletal muscles are essential for postural retention and balance function. However, the relationship between trunk muscle mass (TMM) and balance function has not yet been clarified. This study aimed to examine the impact of TMM on the change in balance function in patients with stroke from admission to a rehabilitation hospital to discharge. METHODS: This retrospective observational study included patients aged ≥65 years with cerebral infarction admitted to our rehabilitation hospital from May 2018 to July 2022. The trunk muscle mass index (TMI) was calculated at admission and discharge using bioelectrical impedance analysis. Patients were divided into low and high TMI groups according to the median TMI at admission. The primary outcome was the change in the Berg Balance Scale (BBS) score (BBS score at discharge - BBS score at admission). RESULTS: A total of 315 patients (mean age, 78.9 ± 8.0 years; 172 men and 143 women) were included. The median TMI was 7.31 kg/m2 in men and 6.30 kg/m2 in women. Among male patients, the high TMI group had higher BBS scores at admission (31.2 ± 18.2 vs. 23.4 ± 17.7, P = 0.005) and discharge (43.0 ± 15.3 vs. 33.4 ± 19.0, P < 0.001) than those in the low TMI group. After adjusting for confounding factors, TMI at admission was independently associated with the change in BBS score (ß = 0.587, P = 0.002). CONCLUSIONS: Reduced TMM negatively influences balance function recovery in patients after stroke. A strategy aimed at increasing TMM could have beneficial effects on balance function.

A Case of Hemi-ablation with High-intensity Focused Ultrasound for a Localized Reducted Solitary Lesion in the Prostate.

Recently, effectiveness of local treatment for oncological outcomes for patients with metastatic prostate cancer (PC) has been reported. We performed hemi-ablation with high-intensity focused ultrasound (HIFU) for a patient with a localized reducted solitary lesion in the prostate, which was diagnosed with magnetic resonance imaging (MRI)-transrectal ultrasound fusion image-guided target biopsy with PSA level of 0.24 ng/mL, after androgen receptor signaling inhibitors (ARSIs) and chemotherapy for metastatic PC. Prostate specific antigen levels decreased to 0.01ng/mL at 1 month after the treatment, and cancer suspicious lesion disappeared on MRI. During the follow-up of 24 months, there was no elevation of PSA level with no severe complication related to the treatment. HIFU has possibility to be an effective and minimally invasive treatment as a local treatment for the localized reducted solitary lesion in the prostate after ARSIs and chemotherapy for metastatic PC.

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.

This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper.

Optimizing implant osseointegration, soft tissue responses, and bacterial inhibition: A comprehensive narrative review on the multifaceted approach of the UV photofunctionalization of titanium.

Titanium implants have revolutionized restorative and reconstructive therapy, yet achieving optimal osseointegration and ensuring long-term implant success remain persistent challenges. In this review, we explore a cutting-edge approach to enhancing implant properties: ultraviolet (UV) photofunctionalization. By harnessing UV energy, photofunctionalization rejuvenates aging implants, leveraging and often surpassing the intrinsic potential of titanium materials. The primary aim of this narrative review is to offer an updated perspective on the advancements made in the field, providing a comprehensive overview of recent findings and exploring the relationship between UV-induced physicochemical alterations and cellular responses. There is now compelling evidence of significant transformations in titanium surface chemistry induced by photofunctionalization, transitioning from hydrocarbon-rich to carbon pellicle-free surfaces, generating superhydrophilic surfaces, and modulating the electrostatic properties. These changes are closely associated with improved cellular attachment, spreading, proliferation, differentiation, and, ultimately, osseointegration. Additionally, we discuss clinical studies demonstrating the efficacy of UV photofunctionalization in accelerating and enhancing the osseointegration of dental implants. Furthermore, we delve into recent advancements, including the development of one-minute vacuum UV (VUV) photofunctionalization, which addresses the limitations of conventional UV methods as well as the newly discovered functions of photofunctionalization in modulating soft tissue and bacterial interfaces. By elucidating the intricate relationship between surface science and biology, this body of research lays the groundwork for innovative strategies aimed at enhancing the clinical performance of titanium implants, marking a new era in implantology.

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis.

This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.

Mismatch between Augmented Reality Navigation Images and Actual Location of a Cauda Equina Tumor:A Case Report.

BACKGROUND: Augmented reality navigation is the one of the navigation technologies that allows computer-generated virtual images to be projected onto a real-world environment. Augmented reality navigation can be used in spinal tumor surgery. However, it is unknown if there are any pitfalls when using this technique. CASE PRESENTATION: The patient in this report underwent complete resection of a cauda equina tumor at the L2-L3 level using microscope-based augmented reality navigation. Although the registration error of navigation was <1 mm, we found a discrepancy between the augmented reality navigation images and the actual location of the tumor, which we have called "navigation mismatch". This mismatch, which was caused by the mobility of the spinal tumor in the dura mater, seems to be one of the pitfalls of augmented reality navigation for spinal tumors. CONCLUSIONS: Combined use of intraoperative ultrasound and augmented reality navigation seems advisable in such cases. J. Med. Invest. 71 : 174-176, February, 2024.

A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction.

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction.

Expert-Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level Attention Mechanism.

In this study, we propose a classification method of expert-novice levels using a graph convolutional network (GCN) with a confidence-aware node-level attention mechanism. In classification using an attention mechanism, highlighted features may not be significant for accurate classification, thereby degrading classification performance. To address this issue, the proposed method introduces a confidence-aware node-level attention mechanism into a spatiotemporal attention GCN (STA-GCN) for the classification of expert-novice levels. Consequently, our method can contrast the attention value of each node on the basis of the confidence measure of the classification, which solves the problem of classification approaches using attention mechanisms and realizes accurate classification. Furthermore, because the expert-novice levels have ordinalities, using a classification model that considers ordinalities improves the classification performance. The proposed method involves a model that minimizes a loss function that considers the ordinalities of classes to be classified. By implementing the above approaches, the expert-novice level classification performance is improved.

Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information Management.

The advancements in deep learning have significantly enhanced the capability of image generation models to produce images aligned with human intentions. However, training and adapting these models to new data and tasks remain challenging because of their complexity and the risk of catastrophic forgetting. This study proposes a method for addressing these challenges involving the application of class-replacement techniques within a continual learning framework. This method utilizes selective amnesia (SA) to efficiently replace existing classes with new ones while retaining crucial information. This approach improves the model's adaptability to evolving data environments while preventing the loss of past information. We conducted a detailed evaluation of class-replacement techniques, examining their impact on the "class incremental learning" performance of models and exploring their applicability in various scenarios. The experimental results demonstrated that our proposed method could enhance the learning efficiency and long-term performance of image generation models. This study broadens the application scope of image generation technology and supports the continual improvement and adaptability of corresponding models.

Low muscle mass in patients with stroke on admission reduces walking ability at discharge.

BACKGROUND & AIMS: Reduced skeletal muscle mass may negatively influence postural retention and walking function. This study aimed to examine the influence of the skeletal muscle mass index on walking function in patients with stroke. METHODS: This study included patients with cerebral infarction aged ≥65 years. The Asian Working Group for Sarcopenia's skeletal muscle mass index criteria were used to classify the participants into the low and high skeletal muscle mass index groups. The patient characteristics of the two groups were compared. The primary and secondary outcome measures were independent walking and walking speed, respectively. RESULTS: In total, 174 participants were included. There were no significant differences in the length of hospital stay, rehabilitation volume, or functional independence measure score at discharge between the males and females. Multivariate logistic regression analysis revealed that independent walking was independently associated with the skeletal muscle mass index on admission. The SMI, as an explanatory variable, was independently associated with the comfortable and fastest walking speeds. Faster walking was associated with higher skeletal muscle mass indexes on admission for both males and females. CONCLUSIONS: A low skeletal muscle mass index negatively influences walking function improvement in patients with stroke. A strategy aimed at increasing skeletal muscle mass can have beneficial effects on walking function in patients with stroke.

Achieving Complete Human Gingival Fibroblast Collagen Coverage on Implant Abutments through Vacuum Ultraviolet (VUV) Photofunctionalization.

PURPOSE: The formation of a biological seal between implant abutments and the surrounding soft tissue is a preventive strategy against peri-implantitis. The aim of this study is to test the hypothesis that surfaces of prosthetic implant abutments treated with vacuum ultraviolet (VUV) light enhance the growth and function of human gingival fibroblasts. MATERIALS AND METHODS: Implant abutments were treated with 172 nm VUV light for one minute. Untreated abutments were subjected as controls. Their surface properties were characterized using SEM, contact angle measurements, and chemical composition analysis. Human gingival fibroblasts were cultured on both untreated and VUV-treated abutments to evaluate cell attachment, proliferation, distribution, and collagen production. Cell detachment assays were also performed under various mechanical and chemical stimuli. RESULTS: After VUV treatment, implant abutments demonstrated a notable transition from hydrophobic to hydrophilic wettability. Surface element analysis revealed a considerable reduction in surface carbon and increases in oxygen and titanium elements on the VUV-treated surfaces. On day 1 of culture, 3.9 times more fibroblasts attached on VUV-treated abutments than on untreated control abutments. Fibroblastic proliferation increased 1.9-3.1 times on VUV-treated abutments, along with a significant improvement in the distribution of populating cells. Collagen production on VUV-treated abutments increased by 1.5-1.7 times. While untreated abutment surfaces showed voids and limited spread of collagen deposition, dense and full coverage of collagen was observed on VUV-treated abutments, with a great contrast in the challenging axial surface zone. Cell retention against mechanical and chemical detaching stimuli was increased 11.3 and 4.3 times, respectively, by VUV treatment. CONCLUSION: Treatment of implant abutments with VUV light for one minute resulted in a reduction of surface carbon and a transformation of the surface from hydrophobic to hydrophilic. This led to enhanced attachment, proliferation, and retention of human gingival fibroblasts, along with nearly complete collagen coverage on implant abutments. These in vitro results indicate the promising potential of utilizing VUV photofunctionalized implant abutments to enhance soft tissue reaction and sealing mechanisms.

Machine learning-based gait analysis to predict clinical frailty scale in elderly patients with heart failure.

Aims: Although frailty assessment is recommended for guiding treatment strategies and outcome prediction in elderly patients with heart failure (HF), most frailty scales are subjective, and the scores vary among raters. We sought to develop a machine learning-based automatic rating method/system/model of the clinical frailty scale (CFS) for patients with HF. Methods and results: We prospectively examined 417 elderly (≥75 years) with symptomatic chronic HF patients from 7 centres between January 2019 and October 2023. The patients were divided into derivation (n = 194) and validation (n = 223) cohorts. We obtained body-tracking motion data using a deep learning-based pose estimation library, on a smartphone camera. Predicted CFS was calculated from 128 key features, including gait parameters, using the light gradient boosting machine (LightGBM) model. To evaluate the performance of this model, we calculated Cohen's weighted kappa (CWK) and intraclass correlation coefficient (ICC) between the predicted and actual CFSs. In the derivation and validation datasets, the LightGBM models showed excellent agreements between the actual and predicted CFSs [CWK 0.866, 95% confidence interval (CI) 0.807-0.911; ICC 0.866, 95% CI 0.827-0.898; CWK 0.812, 95% CI 0.752-0.868; ICC 0.813, 95% CI 0.761-0.854, respectively]. During a median follow-up period of 391 (inter-quartile range 273-617) days, the higher predicted CFS was independently associated with a higher risk of all-cause death (hazard ratio 1.60, 95% CI 1.02-2.50) after adjusting for significant prognostic covariates. Conclusion: Machine learning-based algorithms of automatically CFS rating are feasible, and the predicted CFS is associated with the risk of all-cause death in elderly patients with HF.

Calculation of trunk muscle mass and trunk muscle quality indices for walking independence in asian patients with hip fractures.

BACKGROUND & AIMS: This study aimed to investigate the association between trunk muscle mass and muscle quality, as evaluated by bioelectrical impedance analysis (BIA), and the ability to walk independently in patients with hip fractures. Cutoff values for quantitative and qualitative indicators of the trunk muscles were also calculated. METHODS: This study included 181 patients with hip fractures who were admitted to a convalescent rehabilitation ward. Trunk muscle mass and phase angle of the participants were evaluated on admission. The phase angle in this study was defined as the trunk muscle quality index (TMQI). Patients were classified into the independent (functional independence measure [FIM]-walk score ≥6; n = 101) and non-independent (FIM-walk score ≤5; n = 80) walking groups according to the FIM mobility scores at discharge. RESULTS: The independent group had a higher FIM gain than the non-independent group (37.0 ± 13.6 vs. 27.1 ± 13.5, p < 0.001). Logistic regression analysis showed that the trunk muscle mass index (TMI) and TMQI were associated with the ability to walk independently. Furthermore, cutoff values of TMI and TMQI for male and female to estimate the ability to walk independently were 6.5 kg/m2 and 5.7 kg/m2, and 4.5° and 3.4°, respectively. CONCLUSION: TMI and TMQI are related to the ability to walk independently in patients with hip fractures. These results suggest the importance of improving trunk muscle mass and muscle quality during rehabilitation of patients with hip fractures.

Impact of increased trunk muscle mass on trunk function and activities of daily living in older individuals with cerebral infarction.

BACKGROUND & AIMS: The association between trunk muscle mass or trunk function or trunk muscle strength and activities of daily living (ADLs) has been reported, but no studies have examined the impact of increased trunk muscle mass on trunk function or ADL. This study aimed to determine whether increased trunk muscle mass is associated with trunk function and ADL in older individuals with cerebral infarction during rehabilitation. METHODS: This retrospective observational study enrolled 158 patients with cerebral infarction aged ≥65 years who were admitted for post-stroke rehabilitation. The trunk muscle mass index (TMI) was calculated at admission and discharge using bioelectrical impedance analysis. Patients were divided into two groups: one with increased TMI (64 participants; 41 %) and the other without TMI increase (94 participants; 59 %). Multiple linear regression analysis was performed with functional assessment for control of trunk (FACT) or functional independence measure (FIM)-motor gain as the objective variable and increased TMI group as the explanatory variable. RESULTS: We analyzed a total of 158 patients (79.5 ± 7.8 years of age, 51.9 % men). TMI increase was independently associated with FACT gain (coefficient = 1.413, 95 % confidence interval = 0.34-2.49, P = 0.010), but not with FIM-motor gain. CONCLUSIONS: The results suggest that increased TMI is independently associated with recovery of trunk function. Further prospective studies are needed to elucidate the relationship between increased TMI and ADL.

Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media.

Text-guided image editing has been highlighted in the fields of computer vision and natural language processing in recent years. The approach takes an image and text prompt as input and aims to edit the image in accordance with the text prompt while preserving text-unrelated regions. The results of text-guided image editing differ depending on the way the text prompt is represented, even if it has the same meaning. It is up to the user to decide which result best matches the intended use of the edited image. This paper assumes a situation in which edited images are posted to social media and proposes a novel text-guided image editing method to help the edited images gain attention from a greater audience. In the proposed method, we apply the pre-trained text-guided image editing method and obtain multiple edited images from the multiple text prompts generated from a large language model. The proposed method leverages the novel model that predicts post scores representing engagement rates and selects one image that will gain the most attention from the audience on social media among these edited images. Subject experiments on a dataset of real Instagram posts demonstrate that the edited images of the proposed method accurately reflect the content of the text prompts and provide a positive impression to the audience on social media compared to those of previous text-guided image editing methods.