A Hybrid Photorealistic Architecture Based on Generating Facial Features and Body Reshaping for Virtual Try-on Applications

Online shopping using virtual try-on technology is becoming popular and widely used for digital transformation because of sustainably sourced materials and enhancing customers’ experience. For practical applicability, the process is required for two main factors: (1) accuracy and reliability, and (2) the processing time. To meet the above requirements, we propose a state-of-the-art technique for generating a user’s visualization of model costumes using only a single user portrait and basic anthropometrics. To start, this research would summarize different methods of most virtual try-on clothes approaches, including (1) Interactive simulation between the 3D models, and (2) 2D Photorealistic Generation. In spite of successfully creating the visualization and feasibility, these approaches have to face issues of their efficiency and performance. Furthermore, the complexity of input requirements and the users’ experiments are leading to difficulties in practical application and future scalability. In this regard, our study combines (1) a head-swapping technique using a face alignment model for determining, segmenting, and swapping heads with only a pair of a source and a target image as inputs (2) a photorealistic body reshape pipeline for direct resizing user visualization, and (3) an adaptive skin color models for changing user’s skin, which ensures remaining the face structure and natural. The proposed technique was evaluated quantitatively and qualitatively using three types of datasets which include: (1) VoxCeleb2, (2) Datasets from Viettel collection, and (3) Users Testing to demonstrate its feasibility and efficiency when used in real-world application

Evaluation of awake prone positioning effectiveness in moderate to severe COVID-19

Evidence mainly from high income countries suggests that lying in the prone position may be beneficial in patients with COVID-19 even if they are not receiving invasive ventilation. Studies indicate that increased duration of prone position may be associated with improved outcomes, but achieving this requires additional staff time and resources. Our study aims to support prolonged (≥ 8hours/day) awake prone positioning in patients with moderate to severe COVID-19 disease in Vietnam. We use a specialist team to support prone positioning of patients and wearable devices to assist monitoring vital signs and prone position and an electronic data registry to capture routine clinical data

The Outcomes of Three Surgical Approaches for Acromioclavicular Dislocation Treatment: Findings from Vietnam

Background: Acromioclavicular (AC) dislocation, one of the most common shoulder joint injuries, can be treated by several surgical methods. However, there are still few records about the treatment quality. This study aims to describe the outcomes of three surgical methods for acromioclavicular dislocation treatment at Viet Duc University Hospital, Vietnam. Methods: A cross-sectional study was conducted on 80 patients diagnosed with AC. We retrospectively collected data in the medical records and re-examined the patients. Results: There was no difference between the three groups of surgical approaches relating to the patient’s characteristics, except for the time from the accident to hospital admission. The median length of stay after surgery was highest in the Hook plate group (median (IQR) = 5(2) days), while it was lowest in the K-wire group (median (IQR) = 3(1) days) (p < 0.05). There is statistical significance in the difference of coracoclavicular distance between pre and post-operation in all three surgical method groups (p < 0.001). Conclusion: All of the methods—Hook plate, K-wire, and TightRope—were associated with optimistic outcomes and restored initial anatomy. While the three surgical methods are both safe and effective, the K-wire method is associated with a shorter length of stay and might be economical

Usefulness of Hospital Admission Chest X-ray Score for Predicting Mortality and ICU Admission in COVID-19 Patients

We aimed to investigate the performance of a chest X-ray (CXR) scoring scale of lung injury in prediction of death and ICU admission among patients with COVID-19 during the 2021 peak pandemic in HCM City, Vietnam. CXR and clinical data were collected from Vinmec Central Park-hospitalized patients from July to September 2021. Three radiologists independently assessed the day-one CXR score consisting of both severity and extent of lung lesions (maximum score = 24). Among 219 included patients, 28 died and 34 were admitted to the ICU. There was a high consensus for CXR scoring among radiologists (κ = 0.90; CI95%: 0.89–0.92). CXR score was the strongest predictor of mortality (tdAUC 0.85 CI95% 0.69–1) within the first 3 weeks after admission. A multivariate model confirmed a significant effect of an increased CXR score on mortality risk (HR = 1.33, CI95%: 1.10 to 1.62). At a threshold of 16 points, the CXR score allowed for predicting in-hospital mortality and ICU admission with good sensitivity (0.82 (CI95%: 0.78 to 0.87) and 0.86 (CI95%: 0.81 to 0.90)) and specificity (0.89 (CI95%: 0.88 to 0.90) and 0.87 (CI95%: 0.86 to 0.89)), respectively, and can be used to identify high-risk patients in needy countries such as Vietnam

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening