Search Results (104)

Background and Objectives: Traditionally, we evaluate the size of a wound by using Opsite Flexigrid transparent film dressing, placing it over the wound, tracing the edges of the wound, and then calculating the area. However, this method is both time-consuming and subjective, often leading to varying results depending on the individual performing the assessment. In this study, our goal is to provide an objective method to calculate the wound size and solve variations in photo-taking distance caused by different medical practitioners or at different times, as these can lead to inaccurate wound size assessments. To evaluate this, we employed K-means clustering and used a QR code as a reference to analyze images of the same wound captured at varying distances, objectively quantifying the areas of 40 wounds. This study aims to develop an objective method for calculating the wound size, addressing variations in photo-taking distance that occur across different medical personnel or time points—factors that can compromise measurement accuracy. By improving consistency and reducing the manual workload, this approach also seeks to enhance the efficiency of healthcare providers. We applied K-means clustering for wound segmentation and used a QR code as a spatial reference. Images of the same wounds taken at varying distances were analyzed, and the wound areas of 40 cases were objectively quantified. Materials and Methods: We employed K-means clustering and used a QR code as a reference to analyze wound photos taken by different medical practitioners in the outpatient consulting room. K-means clustering is a machine learning algorithm that segments the wound region by grouping pixels in an image according to their color similarity. It organizes data points into clusters based on shared features. Based on this algorithm, we can use it to identify the wound region and determine its pixel area. We also used a QR code as a reference because of its unique graphical pattern. We used the printed QR code on the patient’s identification sticker as a reference for length. By calculating the ratio of the number of pixels within the square area of the QR code to its actual area, we applied this ratio to the detected wound pixel area, enabling us to calculate the wound’s actual size. The printed patient identification stickers were all uniform in size and format, allowing us to apply this method consistently to every patient. Results: The results support the accuracy of our algorithm when tested on a standard one-cent coin. The paired t-test comparing the first and second photos shot yielded a p-value of 0.370, indicating no significant difference between the two. Similarly, the t-test comparing the first and third photos shot produced a p-value of 0.179, also showing no significant difference. The comparison between the second and third photos shot resulted in a p-value of 0.547, again indicating no significant difference. Since all p-values are greater than 0.05, none of the test pairs show statistically significant differences. These findings suggest that the three randomly taken photo shots produce consistent results and can be considered equivalent. Conclusions: Our algorithm for wound area assessment is highly reliable, interchangeable, and consistently produces accurate results. This objective and practical method can aid clinical decision-making by tracking wound progression over time. Full article

Deep learning has brought substantial progress to medical imaging, which has resulted in continuous improvements in diagnostic procedures. Through deep learning architecture implementations, radiology professionals achieve automated pathological condition detection, segmentation, and classification with improved accuracy. The research tackles a rarely studied clinical medical imaging issue that involves bullet identification and positioning within X-ray images. The purpose is to construct a sturdy deep learning system that will identify and classify ballistic trauma in images. Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. Research data was developed by replicating controlled bullet damage in chest X-rays while expanding to a wider range of anatomical areas that include the legs, abdomen, and head. Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. Multiple computational systems were used to verify the results, which showcased the effectiveness of the proposed solution. This research provides new perspectives on understanding forensic radiology trauma assessment by developing the first deep learning system that detects and classifies gun-related radiographic injuries automatically. The first system for forensic radiology designed with automated deep learning to classify gunshot wounds in radiographs is introduced by this research. This approach offers new ways to look at trauma which is helpful for work in clinics as well as in law enforcement. Full article

Background: This study introduces a novel quantitative method—groove loss time (GLT)—to objectively assess wound leakage following cataract surgery. Methods: In this prospective, single-center study, 70 eyes of 70 patients undergoing cataract surgery via CCI were enrolled. Wound sealing was evaluated by measuring the GLT, defined as the duration the stromal groove remains visible after corneal hydration. GLT was categorized into five grades: ‘water-tight’ (>10 s), ‘excellent’ (>5 s), ‘good’ (3–5 s), ‘bad’ (1–2 s), and ‘poor’ (<1 s). Intraocular pressure (IOP) was recorded at four time points: preoperatively, immediately post-surgery, 3–4 h postoperatively, and on postoperative day one. In select cases, anterior segment optical coherence tomography (AS-OCT) was used to confirm wound architecture. Results: All patients demonstrated a GLT longer than 5 s, corresponding to water-tight or excellent wound sealing. Mean IOP values were 16.08 ± 3.61 mmHg preoperatively, 29.48 ± 11.13 mmHg immediately post-surgery, 16.38 ± 5.45 mmHg at 3–4 h, and 16.65 ± 4.36 mmHg on the day after surgery. No cases of postoperative endophthalmitis, anterior chamber loss, or hypotony were observed. Conclusions: The GLT method provides a simple, objective, and effective tool for evaluating wound integrity in CCIs, ensuring optimal sealing and enhancing postoperative safety. Full article

The Basic Leucine Zipper (bZIP) transcription factors play a vital role in plant responses to abiotic stress. Despite being studied in various plant species, the function of the bZIP gene family in Soapberry (Sapindus mukorossi Gaertn.), a significant tree species for forestry biomass energy, remains unclear. In this study, we conducted a genome-wide analysis of the bZIP gene family in Soapberry, based on the observation that bZIP transcription factors were enriched in the transcriptome data of Soapberry-grafted stem segments, as revealed by both GO and KEGG analyses. For the first time, we identified 31 SmbZIPs and provided detailed information regarding their physicochemical characteristics, gene structures, protein motifs, phylogenetic relationships, cis-regulatory elements (CREs), and predicted transcriptional regulatory networks. According to our prediction of the SmbZIP-mediated regulatory network and CREs in the promoter region, SmbZIPs may be associated with plant growth and development as well as responses to mechanical wounding stress. By integrating RT-qPCR and RNA-seq analyses, we determined that the expression patterns of SmbZIPs were specific to the graft-healing stages and locations. In conclusion, our study elucidates the potential role of the bZIP gene family in responding to plant wounding stress and facilitating graft healing, thereby providing valuable insights for future functional genomics studies of Soapberry. Full article

Background: Chronic diabetic foot ulcers are a global health challenge, affecting approximately 18.6 million individuals each year. The timely and accurate prediction of wound healing paths is crucial for improving treatment outcomes and reducing complications. Methods: In this study, we apply predictive modeling to the case study of diabetic foot ulcers, analyzing and comparing multiple models based on Deep Neural Networks (DNNs) and Machine Learning (ML) algorithms to enhance wound prognosis and clinical decision making. Our approach leverages a dataset of 1766 diabetic foot wounds, each monitored for at least three visits, incorporating key clinical wound features such as WBP scores, wound area, depth, and tissue status. Results: Among the 12 models evaluated, the highest accuracy (80%) was achieved using a three-layer LSTM recurrent DNN trained on wound instances with four visits. The model performance was assessed through AUC (0.85), recall (0.80), precision (0.79), and F1-score (0.80). Our findings indicate that the wound depth and area at the first visit followed by the wound area and granulated tissue percentage at the second visit are the most influential factors in predicting the wound status. Conclusions: As future developments, we started building a weakly supervised semantic segmentation model that classifies wound tissues into necrosis, slough, and granulation, using tissue color proportions to further improve model performance. This research underscores the potential of predictive modeling in chronic wound management, specifically in the case of diabetic foot ulcers, offering a tool that can be seamlessly integrated into routine clinical practice. Full article

Background: Obinutuzumab is a glycoengineered type II anti-CD-20 monoclonal antibody, which can be applied as immunotherapy in patients with follicular lymphoma. To our knowledge, this is the first reported case in the literature describing osteonecrosis of the jaw associated with CD20 monoclonal antibody therapy. Methods: The following case report describes a 39-year-old female patient under maintaining therapy with Obinutuzumab developing osteonecrosis of the jaw after tooth extraction. The necrotic area was located in the right mandible and was rated as a stage II osteonecrosis. Results: This case report should draw attention to the importance of dental follow-ups during aftercare of patients with Non-Hodgkin’s Lymphoma as well as to the relevant precautions for performing tooth extractions in such patients. Conclusions: As Obinutuzumab seems to be a contributing factor in the development of MRONJ, special attention has to be drawn to tooth extractions in such patients, which should only be performed with perioperative antibiosis, the least amount of trauma possible, always including the smoothening of sharp residual bone segments and a saliva-proof wound closure, as well as constant dental follow-ups. Full article

The tensile armor layer plays a crucial role in offshore flexible pipelines, primarily bearing axial tensile loads. However, during installation and operation, it may experience compressive forces, leading to a risk of lateral buckling, which is further intensified by manufacturing deviations in the steel strips. This study introduces a method to quantify these deviations based on the circumferential length change in defect segments in helically wound steel strips. A deviation model is established and analyzed using Abaqus finite element simulations to evaluate the impact of helical angles and deviation severity on the critical lateral buckling load. The results reveal that as the deviation severity increases, the critical buckling load significantly decreases, with reductions of up to 65% for small helical angles. Additionally, the rapid rise in bending moment at the defect location is identified as the primary cause of lateral buckling initiation. Full article

Background: Diabetic foot ulcers (DFUs) represent a significant challenge in managing diabetes, leading to higher patient complications and increased healthcare costs. Traditional approaches, such as manual wound assessment and diagnostic tool usage, often require significant resources, including skilled clinicians, specialized equipment, and extensive time. Artificial intelligence (AI) and generative AI offer promising solutions for improving DFU management. This study systematically reviews the role of AI in DFU classification, prediction, segmentation, and detection. Furthermore, it highlights the role of generative AI in overcoming data scarcity and potential of AI-based smartphone applications for remote monitoring and diagnosis. Methods: A systematic literature review was conducted following the PRISMA guidelines. Relevant studies published between 2020 and 2025 were identified from databases including PubMed, IEEE Xplore, Scopus, and Web of Science. The review focused on AI and generative AI applications in DFU and excluded non-DFU-related medical imaging articles. Results: This study indicates that AI-powered models have significantly improved DFU classification accuracy, early detection, and predictive modeling. Generative AI techniques, such as GANs and diffusion models, have demonstrated potential in addressing dataset limitations by generating synthetic DFU images. Additionally, AI-powered smartphone applications provide cost-effective solutions for DFU monitoring, potentially improving diagnosis. Conclusions: AI and generative AI are transforming DFU management by enhancing diagnostic accuracy and predictive capabilities. Future research should prioritize explainable AI frameworks and diverse datasets for AI-driven healthcare solutions to facilitate broader clinical adoption. Full article

Introduction: Tooth autotransplantation is a well-established dental surgical procedure. However, third molar autotransplantation to bony free flaps is rarely performed. We present a case of two impacted wisdom teeth that were transplanted to a DCIA free flap using 3D printing technologies. Case report: A 10-year-old girl was diagnosed with ossifying fibroma. She underwent a segmental mandibular resection with nerve preservation and reconstruction using a DCIA free flap. Six years later, due to edentulism, wisdom tooth autotransplantation was performed with digital planning, thermoplastic vacuum-formed guides, and 3D-printed replicas. Postoperatively, splint fixation was required for 12 weeks due to mobility, and a minor wound complication resolved spontaneously. At the one-year follow-up, the transplanted teeth integrated successfully without resorption or ankylosis. Orthodontic treatment was initiated to optimize alignment. Conclusions: This case of an impacted third molar autotransplantation to a DCIA free flap in an adolescent patient after a non-malignant mandibular tumor resection and reconstruction demonstrates promising results. The application of 3D printing technology significantly enhances the feasibility of dental transplantation in challenging cases, particularly for suboptimal donor teeth such as impacted wisdom teeth, by enabling precise surgical planning and optimized recipient site preparation while also reducing damage to the grafted teeth during transplantation. Further research is needed to assess the role of tooth autotransplantation in bony free flaps. Full article

Chronic wounds require accurate and objective assessment to monitor healing progress and optimize treatment. Traditional contact-based methods for wound measurement are often uncomfortable for patients, impractical for clinicians, and prone to inaccuracies due to the complex shapes of wounds. Advances in computational power and data analysis have enabled non-contact techniques, particularly digital imaging, to play a greater role in wound assessment. However, challenges persist, as chronic wounds can vary greatly in size, shape, and surface geometry, making accurate 3D modeling difficult. Dynamic changes in wound dimensions during treatment and the potential for occluded areas further complicate assessment. Handheld 3D cameras and sensors, while promising, are limited by user experience and the potential for incomplete reconstructions. To address these challenges, this paper introduces a fully automated system for analyzing chronic wounds. The system consists of a robotic arm, an industrial-grade 3D scanner, and advanced algorithms for extracting and analyzing wound features. This complete pipeline improves the robustness and functionality of the system and enables precise 3D wound modeling and comprehensive data extraction. This paper discusses the operational system, highlights its advancements, and evaluates its potential for enhancing wound monitoring and healing outcomes. Full article

Introduction: Hyperspectral Imaging (HSI) combined with an augmented model-based data processing enables the measurement of the depth-resolved perfusion of burn wounds. With these methods, the fundamental problem of the wound dynamics (wound conversion or progression) in the first 4 days should be parametrically analyzed and evaluated. Material and Methods: From a cohort of 59 patients with burn injuries requiring medical intervention, 281 homogenous wound segments were selected and subjected to clinical classification based on the duration of healing. The classification was retrospectively assigned to each segment during the period from day 0 to day 2 post-burn. The perfusion parameters were presented in two parameter spaces describing the upper and deeper perfusion. Results: The investigation of value distributions within the parameter spaces pertaining to four distinct categories of damage from superficial dermal to full-thickness burns during the initial four days reveals the inherent variability and distinct patterns associated with wound progression, depending on the severity of damage. The analysis highlights the challenges associated with estimating the burn degrees during this early stage and elucidates the significance of deeper tissue perfusion in the classification process, which cannot be discerned through visual inspections. Conclusions: The feasibility of early classification on day 0 or 1 was assessed, and the findings indicate a restricted level of reliability, particularly on day 0, primarily due to the substantial variability observed in wound characteristics and inherent dynamics. Full article

Background/Objectives: The increasing complexity of spinal oncology procedures, particularly in en-bloc tumor resections, creates challenges in tissue perfusion assessment due to extended operative times and extensive surgical dissection. Real-time visualization of tissue perfusion can be achieved with ICG using commercially available handheld imaging systems, offering potential advantages in spinal oncology cases. This study assessed the utility of ICG in analyzing soft-tissue viability during complex spine procedures extending beyond 7.5 h, with a particular focus on oncologic resections. Methods: Three cases that required over 7.5 h of operative time were chosen for ICG utilization. These cases included an en-bloc malignant peripheral nerve sheath tumor resection, an en-bloc resection of a malignant epithelioid neoplasm, and a long-segment fusion revision for pseudoarthrosis. At the conclusion of the critical portion of the procedure, a handheld intraoperative fluorescence camera was utilized to visualize the tissue penetration of intravenous ICG. Results: Prior to injecting ICG, devascularized tissue was not clearly visible. Injecting ICG allowed clear separation of vascularized (fluorescing) and devascularized (non-fluorescing) tissues. One region of non-florescent tissue was later confirmed to be devascularized with MRI and experienced postoperative infection. Conclusions: As the complexity of spinal oncology procedures increases, ICG fluorescence imaging offers a novel method for real-time assessment of tissue perfusion. This technique may be particularly valuable in extensive tumor resections, post-radiation cases, and revision surgeries where tissue viability is at risk. Further investigation in the spinal oncology population could help establish whether early identification of poorly perfused tissues impacts wound healing outcomes. Full article

Skin ulcers are open wounds on the skin characterized by the loss of epidermal tissue. Skin ulcers can be acute or chronic, with chronic ulcers persisting for over six weeks and often being difficult to heal. Treating chronic wounds involves periodic visual inspections to control infection and maintain moisture balance, with edge and size analysis used to track wound evolution. This condition mostly affects individuals over 65 years old and is often associated with chronic conditions such as diabetes, vascular issues, heart diseases, and obesity. Early detection, assessment, and treatment are crucial for recovery. This study introduces a method for automatically detecting and segmenting skin ulcers using a Convolutional Neural Network and two-dimensional images. Additionally, a three-dimensional image analysis is employed to extract key clinical parameters for patient assessment. The developed system aims to equip specialists and healthcare providers with an objective tool for assessing and monitoring skin ulcers. An interactive graphical interface, implemented in Unity3D, allows healthcare operators to interact with the system and visualize the extracted parameters of the ulcer. This approach seeks to address the need for precise and efficient monitoring tools in managing chronic wounds, providing a significant advancement in the field by automating and improving the accuracy of ulcer assessment. Full article

Background/Objectives: Traditional autologous bone grafts as a treatment for bone defects have drawbacks like donor-site morbidity and limited supply. PerOssal^®, a ceramic bone substitute, may overcome those drawbacks and could offer additional benefits like prolonged, local antibiotic release. This study investigates the clinical and radiological outcomes, including patient-reported outcomes, of using PerOssal^® in nonunions (NU) and high-grade chronic osteomyelitis (COM). Methods: A single-center, retrospective study, investigating patients treated with PerOssal^® between January 2020 and December 2023. Collected data include patient characteristics as well as various surgical and outcome parameters including the Lower Extremity Functional Scale (LEFS). Results: A total of 82 patients were analyzed. Reinfection occurred in 19.5% of cases. Osseous integration of PerOssal^® was achieved in 89% of cases, higher in cavitary defects (91.5%) than segmental defects (72.7%). The revision rate was 32.9%, mainly due to wound healing disorders and reinfections. Mean LEFS score was 53.4 which was heavily influenced by sex (male: 50.7 vs. female: 63.4), revision surgery (no: 55.7 vs. yes: 49.1), reinfection (no: 56.6 vs. yes: 39.4), and osseous integration of PerOssal^® (yes: 55.8 vs. no: 38.4). Conclusions: PerOssal^® demonstrates promising outcomes in treating NUs and high-grade COM, especially in cavitary defects, with high osseous integration rates and acceptable functional results. However, reinfection remains a concern, particularly with difficult-to-treat pathogens and extensive surgical histories. Early, comprehensive surgical intervention and tailored antibiotic strategies are essential. Patient selection, defect characteristics, and comorbidities significantly influence success. Further research is needed to optimize treatment protocols. Full article

Introduction: Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. Results: Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. Discussion: AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusions: AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems. Full article