Pictorial Images Research Articles

Current Practices and Considerations in Lung Biopsy for Suspected Granulomatous-Lymphocytic Interstitial Lung Disease: A Clinician Survey

Introduction: This study explores clinicians’ diagnostic practices and perceptions in the context of granulomatous-lymphocytic interstitial lung disease (GLILD), a pulmonary manifestation of common variable immunodeficiency disorder. The aim was to gain valuable insights into key aspects, such as the utilization of radiological features for diagnostic purposes, indications for lung biopsy, preferred biopsy techniques, and the relative importance of different histopathological findings in confirming GLILD. Method: A survey targeting expert clinicians was conducted, focusing on their experiences, practices, and attitudes towards lung biopsy in suspected GLILD cases. Results: The survey revealed that the majority of respondents accepted high-resolution computed tomography as a sufficient alternative to biopsy for making a probable GLILD diagnosis in most patients. There was a consensus among most respondents that the presence of extrapulmonary granulomatous disease is adequate for making a diagnosis of GLILD where the chest imaging and clinical picture are consistent. When a biopsy was recommended, there was notable variation in the preferred initial biopsy technique, with 35% favouring transbronchial biopsy. Conclusion: Our findings underscore the complexity of diagnosing GLILD, indicating varied clinician opinions on the necessity and efficacy of lung biopsies. They highlight the need for further research and the development of consistent diagnostic criteria and management protocols, ultimately aiming to enhance the accuracy and safety of GLILD diagnosis and treatment strategies.

House price prediction

In recent years, more and more facts have proven that neural network models have good performance in machine learning, and researchers always use RNN and CNN models to solve complex problems, like how RNN can utilize historical information to predict further weather, identify human notes, and analyze text. The CNN model is always used to identify signatures, process picture images, and classify items. How I find that in the linear prediction field, people always use linear regression to predict results. For example, in house price prediction, people will use linear regression or the random forest model to predict house price. However, the author think neural network model can also have good performance in house prices. So, this paper is about building a better neural network model to predict house prices. This paper describes how to process data before training two models and compare their prediction accuracy. After that, this paper describes new neural network and train this model, which, after training, will output its accuracy. Luckily, after a long-time training, the author gets a neural network model to predict house price, it has a higher accuracy in predicting house price than two tradition models that we used.

Stereotypes-images and symbols-signs of a bee in the projection of the conceptual plane of linguistic consciousness

This article has several purposes: 1. analyse and identify stereotypical images of the entomological concept of bee in the Russian national linguistic consciousness; 2. reveal the symbolic content of linguistic consciousness by extracting semantic and symbolic signs attached to the stereotypical images of the entomological concept of bee; 3. establish the dynamic invariance (stability) and temporal relevance of the identified stereotypical images in the Russian linguistic picture of the world; 4. confirm the archetypal nature of bee images as universal “products” of the collective unconsciousness and indicators of being structure. According to the study results, stereotypical images of the concept of bee and its symbolic signs were revealed: the stereotype image «hard worker»: ‘hard work’, ‘painstaking’, ‘indefatigability’, ‘distinctness’, ‘purposefulness’, ‘endurance’, ‘perseverance’, ‘fruitfulness’, ‘diligence’, ‘organization’, ‘persistence’, ‘perseverance’, ‘concentration’; The stereotype image «social being»: ‘communism / collectivism’, ‘socialism’, ‘justice’, ‘equality’, ‘teamwork’ / ‘camaraderie’, ‘unity’, ‘autocracy / power’, ‘hierarchy’, ‘self-sacrifice’, ‘fearlessness / bravery’; the stereotype image «deity / divine creation»: ‘pleasing in goodness’, ‘divine love’, ‘divine message’, ‘divine gift’, ‘Last Judgment’, ‘suffering of Christ’, ‘purity (of the soul)’, ‘chastity’, ‘faithfulness’, ‘righteousness’, ‘punishment (of sinners)’, ‘God’, ‘immortality’, ‘power’, ‘divine blessing’, ‘abundance’, ‘prosperity’, ‘intuition’; stereotype image «female person / goddess»: ‘mother’, ‘care (warmth)’, ‘woman’, ‘meekness’, ‘virtue’, ‘diligence’, ‘chastity’, ‘motherhood’, ‘matriarchy’, ‘primacy’, ‘offspring / fertility’; the stereotype image «wise creature»: ‘eloquence’, ‘prudence’, ‘wisdom’, ‘morality’, ‘mathematical thinking / mind’, ‘expediency’, ‘(life) experience / knowledge’, ‘temptation’ / ‘self-control’. Symbolic signs reflect the idea of understanding God as the supreme being and mainly appeal to the anthropomorphic moral sphere. The time span of the illustrative material (from Ancient Russia to Modern times) testifies to the archetypality, temporal relevance and dynamic invariance of the highlighted stereotypical images of concept of bee in the linguistic consciousness of native Russian speakers and in the Russian language picture of the world. This demonstrates both ethnic and socio-cultural affiliation and kinship in the innate and instinctive perception of the being structure between human race representatives and the animal world.

A Region-Monitoring-Type Slitless Imaging Spectrometer.

In modern scientific practice, it is necessary to consistently observe predetermined zones, with the expectation of detecting and identifying emerging targets or events inside such areas. This research presents an innovative imaging spectrometer system for the continuous monitoring of specific areas. This study begins by providing detailed information on the features and optical structure of the constructed instrument. This is then followed by simulations using optical design tools. The device has an F-number of 5, a focal length of 100 mm, a field of view of 3 × 7, and a wavelength range spanning from 400 nm to 600 nm. The optical path diagram demonstrates that the system's dispersion and imaging pictures can be distinguished, hence fulfilling the system's specifications. Furthermore, the utilization of a Modulation Transfer Function (MTF) graph has substantiated that the image quality indeed satisfies the specified criteria. To evaluate the instrument's performance in the spectrum observation of fixed regions, a region-monitoring-type slitless imaging spectrometer was built. The equipment has the capability to identify a specific region and rapidly capture the spectra of objects or events that are present inside that region. The spectral data were collected effectively by the implementation of image processing techniques on the captured photos. The correlation coefficient between these data and the reference data was 0.9226, showing that the device successfully measured the target's spectrum. Therefore, the instrument that was created successfully demonstrated its ability to capture images of the observed areas and collect spectral data from the targets located within those regions.

Practice-Based Lessons in Photographic Education: An Alternative to Photo Image Development

The utilisation of contemporary photography technology facilitates rapid knowledge acquisition and enhancement of several aspects. Nevertheless, the fundamental process of picture-making needs to be adequately emphasised, resulting in a negligent approach and a lack of comprehension among students regarding the significance of generating high-quality images. This practice-based lesson aims to elucidate to students the fundamental relevance of picking picture image designs as a precursor to producing impactful photography. The chosen topic for creating artistic works is around nature and is centred on the Malacca campus. The PCT256 - Contemporary Photography Practice course selection at Universiti Teknologi MARA was based on its alignment with the initial approach, which involves creating artwork through various photographic processes. The subsequent topic, assessment analysis, will use the scoring rubric to assess the student's comprehension of the material. The fabrication of 58 pieces involved a total of 29 students. This essay will address the production process's various difficulties, problems, and challenges. The research findings indicate that students have a more significant potential for comprehending the process when they engage in iterative attempts to produce optimal outcomes.

Multi-media Image and Video Overlay Text Extraction Based on Bayesian Classification Algorithm

With the continuous development of Internet technology, using multimedia for virtual application has become a new way. In this paper, by introducing the bayesian classification algorithm, multimedia graphics and video for carding, testing and implementation of edge image of fine processing, matching the corresponding text for quick positioning, at the same time use the filter for image and video background picture, further classification, distinguish between background and text, the extraction of superposition character. Simulation results show that the bayesian classification algorithm is effective and improves the efficiency and accuracy of image and video processing.

Feasibility of 3 Tesla MRI for the assessment of mid-palatal suture maturation: a retrospective pilot study.

The maxilla occupies a key position in dentofacial orthopaedics, since its transversal development can be directly influenced by orthodontic therapy. The maturation stages of the mid-palatal suture, which are obtained from cone-beam computed tomography images (CBCT), present an addition to clinical decision-making in transversal discrepancies of the upper jaw. In an endeavour to reduce ionizing radiation in adolescents and young adults, who are particularly susceptible to long term stochastic irradiation effects, we investigated the feasibility of 3 Tesla (3T) MRI in detecting the maturation stages of the mid-palatal suture. A collective of 30 patients aged 24-93years with routine neck MRI at 3T, underwent an additional three-dimensional isotropic T1 weighted study sequence of the midface. Image evaluation was performed on axial, multi-planar formatted reconstructions of the dataset aligned to the midline axis of the palate, and curved reconstructions aligned to the concavity of the palate. Inverted images helped to achieve an image impression similar to the well-known CBCT appearance. All datasets were reviewed by three readers and mid-palatal maturation was scored twice according to Angelieri et al. Intra- and inter-rater agreement were evaluated to measure the robustness of the images for clinical evaluation. 3T MRI deemed reliable for the assessment of mid-palatal suture maturation and hence for the appraisal of the hard palate and its adjacent sutures. The data of this pilot study display the feasibility of non-ionizing cross-sectional MRI for the determination of sutural maturation stages. These findings underline the potential of MRI for orthodontic treatment planning, further contributing to the avoidance of unnecessary radiation doses.

Application of remote sensing techniques for estimation and mapping of iron oxides and iron-containing minerals in open pits of the Kryvyi Rih iron ore basin

The purpose of this study is to use remote sensing data to map the deposits of the Kryvyi Rih iron ore basin. Several methods for estimating iron oxide content have been applied and tested. Using image processing techniques, color composites, band ratios and supervised classification, a map of rocks with different iron oxide contents in the Inhulets and Skeliuvats-Magnetite deposits was create. The proposed method uses six Landsat 9 bands to map iron-bearing minerals and rocks (spectra 1, 3, 4 and 5) and hydroxyl (spectra 1, 5 and 7), which are easily visible in the pictures of the operation land images. The final identification of iron deposition was based on the difference in reflectivity of iron oxide content. The Kryvyi Rih iron ore basin is an excellent environment for remote sensing experiments for iron ore identification. Quite large-scale outcrops of ore bodies with pronounced reflection spectra are observe at the deposits. The large extent of development of ferruginous horizons makes them easy to diagnose using Landsat series satellites.

Automated Identification of Retinal Disorders by Deep Learning with Optical Coherence Tomography Pictures

A crucial problem in computer-aided diagnosis (CAD) for medical applications is the classification of retinal disorders. This paper addresses the 4-class classification problem in optical coherence tomography image analysis (OCT) to automatically identify choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. Retinal OCT pictures were identified by the proposed classification approach using a set of four classification model instances, each based on an upgraded residual neural network (ResNet50).At the patient level, the experiment employs a 10-fold cross-validation process that is founded on the growth of the OCT retinal picture collection. According to the study's findings, multiple ResNet50 concatenation is a helpful method when the availability of pictures used in medicine is scarce. In order to better understand the decision-making process, we also ran a bottleneck test and a qualitative assessment of the model predictions.

A Deep Learning-based System for Automated Plant Disease Severity Detection and Fertilizer Optimization

Abstract: Because food is a basic requirement for all living beings on Earth, agriculture is especially important in our daily life. Agriculture is our main source of food. In addition to plant diseases that impede the growth and quality of food crops, agriculture works to generate food to feed the growing population. We'll look at five plants: bitter gourd, mango tree, spinach, tomato, and hibiscus. This study proposes a CNN-based technique for early plant disease diagnosis. The approach consisted of three steps: image segmentation, feature extraction, and picture pre-processing. The results of these three processes are combined to form a Convolutional Neural Network (CNN) classifier. To research and analyse a plant, an input image of the damaged portions is obtained and compared to the desired dataset. The disease is then anticipated, along with therapeutic treatments. Once the disease has been recognized, the quantity of pesticides, recommended application place, and chemicals themselves will be displayed. It will also identify the nearest location where pesticides are available.

The Detection and Classification from the Multimodal Images using Artificial Intelligence for Lung Diseases

Lung cancer is a disease in which healthy cells in the body gradually convert into tumour cells, resulting in a variety of medical issues. A standard dataset exists for lung cancer. With the rising incidence of lung cancer and the exponential growth of CT pictures, having a quick and effective way to evaluate CT scans can help physicians or surgeons develop an early treatment plan. In this research, two approaches are investigated for lung cancer prediction. One approach is based on training machine learning model on the features extracted from image processing techniques. And the other approach involves Artificial Intelligence models for lung cancer detection. Computed Tomography (CT) pictures are useful for determining the stage of lung cancer in a patient. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. For medical photos, texture analysis is critical. As a result, algorithms such as the K-Means clustering method and morphological operations such as erosion, dilation, and so on are used

Experimental studies of a remote device for biometric identification of the pre-mastitis state of the udder of dairy cows

Analysis of development of dairy cattle breeding in the Republic of Belarus is presented. The problems of reducing milk productivity and the period of economic use of dairy cows as a result of mastitis disease are noted. The importance of timely detection of sick animals, as well as treatment of the inflammatory process of the udder in the early stages is noted. The results of theoretical and experimental studies of a mock-up sample of a biometric identification device for the pre-mastitis state of udder of dairy cows are presented. In the course of experimental studies, a significant influence of ambient temperature on the thermal imaging picture was revealed, based on which the values of the radiation coefficient for cows were determined. The final adjustment of the previously established temperature ranges was made when determining one or another form of mastitis, namely, within 32.0−36.3 ℃ – the range of normal temperatures, 36.4−37.7 ℃ – “Subclinical mastitis”, 37.8−39.0 ℃ – “Clinical mastitis”. The most significant factors and optimization parameter have been determined. The percentage of the useful area of the studied object is selected as the optimization parameter. Based on the processing of experimental data, a mathematical model is obtained, described by a regression equation in the form of a polynomial of the second degree. The rational parameters of the device for biometric identification of the pre-mastitis state of the udder of dairy cows were obtained, namely: the angle of inclination of the thermal imaging module αо – 2,7o, the focal length to the object l – 794 mm, installation height of the thermal imaging module h – 495 mm. Also, based on the conducted research, priority areas and opportunities for development of the domestic agro-industrial complex through creation of integrated systems for monitoring the physiological state of animals within the framework of development of digital automated technologies of a “smart” farm have been identified.

A comparative Analysis of Image Enhancement Using Autoencoders and Generative Adversarial Networks

The purpose of this study, which focuses on image enhancement methods, is to investigate how various methods contribute to higher-quality photos in particular applications. Tracing the history of image improvement, encompassing the technological evolution of early photography from the 19th century to the present. The foundation of conventional image enhancement technology, which directly affects image elements and transformation coefficients, respectively, emphasizes frequency domain and spatial domain processing. It is noted that the application of autoencoders and generative adversarial networks to image enhancement as a deep learning training strategy has shown effectiveness in picture classification, target recognition, and image segmentation. This article primarily examines and contrasts autoencoders and generative adversarial networks, summarizing their applicability, constraints, benefits, and drawbacks and talking about the patterns in their upcoming development. The study has guiding value for advancing the development of picture technology and offers significant theoretical and empirical support for the field of image enhancement.

Spontaneous coronary artery dissection in a young woman with signs of connective tissue dysplasia and hereditary thrombophilia: clinical case

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is a disease that develops unrelated to intracoronary intervention, atherosclerosis, aortic dissection, or mechanical trauma and causes a false lumen (intramural hematoma) in the wall of the coronary artery (CA) with impaired blood flow in it and myocardial ischemia in the affected region of the CA. SCAD most often develops in young and middle-aged adults (aged ≤50 years); among women, it becomes the culprit in 24%–35% of cases of acute myocardial infarction (MI). SCAD is a risk factor for MI, and incorrect interpretation of the angiographic picture and intravascular imaging methods can lead to incorrect tactics of patient behavior.
 CLINICAL CASE DESCRIPTION: This article presents a clinical case of SCAD leading to the development of MI in a young woman with concomitant connective tissue dysplasia and hereditary thrombophilia. The angiographic disease course resembled focal atherosclerosis, and in the course of invasive management, complications had arisen, confirming the probable genesis of coronary artery obstruction.
 CONCLUSION: SCAD is a complex disease, with a sudden onset and an ambiguous prognosis. In most cases, SCAD develops in young women in the absence of cardiovascular factors. It is difficult to diagnose because its signs and symptoms are similar to more common diseases, mainly MI. SCAD can masquerade as focal stenosis on an angiogram, mimicking an atherosclerotic plaque. The «gold standard» for diagnosing SCAD is optical coherence tomography (OCT). OCT enables the visualization of the state of all coronary artery walls and elucidates the pathogenetic mechanisms of MI. If performing OCT is impossible after diagnostic coronary angiography in young patients in suspected cases, the likelihood of DST and SCAD risk must be assessed to avoid errors in choosing treatments. The technical accessibility of intracoronary imaging methods reduces the frequency of diagnostic and, consequently, treatment errors.

A novel practical method to predict anterior cruciate ligament hamstring graft size using preoperative MRI

BackgroundPredicting hamstring graft size preoperatively for anterior cruciate ligament (ACL) reconstruction is important for preempting an insufficient diameter in graft size intraoperatively, possibly leading to graft failure. While there are multiple published methods using magnetic resonance imaging (MRI) picture archiving and communication systems (PACS), most are not feasible and practical. Our study aims to (1) practically predict the ACL hamstring graft size in a numerically continuous manner using the preoperative MRI from any native MRI PACS system, (2) determine the degree of correlation between the predicted and actual graft size, and (3) determine the performance of our prediction method if we define an adequate actual graft size as ≥ 8 mm.MethodsA retrospective review of 112 patients who underwent primary ACL reconstruction with quadrupled hamstring semitendinosus-gracilis grafts at a tertiary institution was conducted between January 2018 and December 2018. Graft diameter can be predicted in a numerically continuous manner as √[2*(AB + CD)], where A and B are the semitendinosus cross-sectional length and breath, respectively, and C and D are the gracilis cross-sectional length and breath, respectively.ResultsA moderately positive correlation exists between the predicted and actual graft diameter (r = 0.661 and p < .001). Our method yields a high specificity of 92.6% and a moderate sensitivity of 67.2% if we define an adequate actual graft size as ≥ 8 mm. An area under receiver-operating characteristic curve shows good discrimination (AUC = 0.856).ConclusionsWe present a practical method to predict the ACL hamstring graft size with high specificity using preoperative MRI measurements.

An Effective analysis of brain tumor detection using deep learning

INTRODUCTION: Cancer remains a significant health concern, with early detection crucial for effective treatment. Brain tumors, in particular, require prompt diagnosis to improve patient outcomes. Computational models, specifically deep learning (DL), have emerged as powerful tools in medical image analysis, including the detection and classification of brain tumors. DL leverages multiple processing layers to represent data, enabling enhanced performance in various healthcare applications.&#x0D; OBJECTIVES: This paper aims to discuss key topics in DL relevant to the analysis of brain tumors, including segmentation, prediction, classification, and assessment. The primary objective is to employ magnetic resonance imaging (MRI) pictures for the identification and categorization of brain malignancies. By reviewing prior research and findings comprehensively, this study provides valuable insights for academics and professionals in deep learning seeking to contribute to brain tumor identification and classification.&#x0D; METHODS: The methodology involves a systematic review of existing literature on DL applications in brain tumor analysis, focusing on MRI imaging. Various DL techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, are explored for their efficacy in tasks such as tumor segmentation, prediction of tumor characteristics, classification of tumor types, and assessment of treatment response.&#x0D; RESULTS: The review reveals significant advancements in DL-based approaches for brain tumor analysis, with promising results in segmentation accuracy, tumor subtype classification, and prediction of patient outcomes. Researchers have developed sophisticated DL architectures tailored to address the complexities of brain tumor imaging data, leading to improved diagnostic capabilities and treatment planning.&#x0D; CONCLUSION: Deep learning holds immense potential for revolutionizing the diagnosis and management of brain tumors through MRI-based analysis. This study underscores the importance of leveraging DL techniques for accurate and efficient brain tumor identification and classification. By synthesizing prior research and highlighting key findings, this paper provides valuable guidance for researchers and practitioners aiming to contribute to the field of medical image analysis and improve outcomes for patients with brain malignancies.

Is It Air or Fat? Chyluria as the Lesser Known Cause of Dark Fluid Levels within the Urinary System

Chyluria is caused by the development of the reno-lymphatic fistula leading to reflux of chyle in urine. The most common cause of chyluria is a parasitic infection, especially lymphatic filariasis; however, rarely chronic renal infections like tuberculosis can also cause chyluria. The presence of fat fluid level in the pelvicalyceal system, perinephric, retroperitoneal collections and in the urinary bladder on CT scan in a case of chronic pyelonephritis is an important indicator of the presence of the reno-lymphatic fistula. We describe clinical picture and CT images of a patient with chyluria showing fat fluid in abdominal collections.

Mohammad Ebrahim Jafari: Aesthetics of Painting in Poetry

The article is devoted to the creative heritage of the outstanding figure of Iranian culture — poet and artist Mohammad Ebrahim Jafari (1940–2018). The novelty of the presented research is determined by the almost complete absence of scientific works devoted to this issue: basic biographical facts, analysis of artistic language and the specific imagery of M. E. Jafari are poorly represented even in the Persian language. The purpose of the work is to identify the features of the national-cultural horizon of the synthetic genre combining painting and poetry developed by Jafari. To achieve this goal, it was necessary, firstly, to trace the poet’s biography; secondly, to clarify the nature of the artistic images created by him at different stages of his life and to analyze the original method of the author’s initiative as a way of artistic enrichment of the new synthetic genre. The multifaceted nature of the research subject determined the need for an interdisciplinary approach and the use of a complex of methods such as biographical, hermeneutic, and comparative typological. The author of the article has studied all the video materials and interviews of the poet published to date, his paintings, as well as a collection of selected poems. The author of the article also translated, for the first time, from Persian all the poetic material presented in the text. The study showed that M. E. Jafari, who does not differentiate the forms of creativity, easily transfers the stylistic techniques of his paintings to poems, which fills his lyrics with different shades of colors and smells. Poetic rhythm can be seen in his paintings, at the same time, abstract pictorial images embody the most significance for the poet in terms of acuity of perception, creating images of space and time as a unique experience, extremely concrete in its strength and depth. The masterly combination of abstract expressiveness and specificity of the sensual image makes it possible to attribute the deeply personal artistic creativity of M. E. Jafari to the cultural wave of new poetry, which meets the basic principles of the philosophy of Iranian modernism.

Brain Tumor Detection Using Neural Classification in Machine Learning

Brain cancer classification is a difficult task due to the variety and complexity of tumors shown in magnetic resonance imaging (MRI) pictures. This research presents two neural network approaches for categorizing MRI brain images. The proposed neural network method consists of three steps: feature extraction, dimensionality reduction, and classification. First, we extracted features from MRI images using discrete wavelet transformation (DWT). In this second stage, we reduce the salient features of MRIs using principal component analysis (PCA). For the classification step, two supervised machine learning classifiers have been developed. Artificial neural networks are used by both classifiers; however, the second one employs back propagation (BPN) while the first one uses feed-forward (FF-ANN). Using the classifiers, MRI brain images of the subjects were classified as normal or abnormal. Artificial neural networks have numerous applications, including function approximation, feature extraction, optimization, and classification (ANNs). They are specifically intended to enhance photos, distinguish and categorize items, separate and register objects, and extract features. Among these, object and picture recognition is the most important for complex processing tasks such as classifying brain tumors. Radial basis function (RBF), cellular, multi-layer perceptron (MLP), hop field, and pulse-coupled neural networks have all been used in image segmentation. These networks can be categorized as feed-forward (associative) or feedback (auto-associative).

Mechanical Purification of Lipofilling: The Relationship Between Cell Yield, Cell Growth, and Fat Volume Maintenance

BackgroundThe mechanical manipulations of fat tissue represented from centrifugation, filtration, washing, and fragmentation were considered the most effective strategies aiming to obtain purified lipofilling with different impacts both in terms of adipose-derived stem cells amount contained in stromal vascular fraction, and fat volume maintenance.ObjectivesThe present work aimed to report results in fat volume maintenance obtained by lipofilling purification based on the combined use of washing and filtration, in a clinical study, and to deeply investigate the adipose-derived stem cells yield and growth capacity of the different stromal vascular fraction extraction techniques with an in vitro approach.MethodsA preliminary prospective, case-control study was conducted. 20 patients affected by face and breast soft tissue defects were treated with lipofilling and divided into two groups: n = 10 patients (study group) were treated with lipofilling obtained by washing and filtration procedures, while n = 10 (control group) were treated with lipofilling obtained by centrifugation according to the Coleman technique. 6 months after the lipofilling, the volume maintenance percentage was analyzed by clinical picture and magnetic resonance imaging comparisons. Additionally, extracted stromal vascular fraction cells were also in vitro analyzed in terms of adipose-derived stem cell yield and growth capacity.ResultsA 69% ± 5.0% maintenance of fat volume after 6 months was observed in the study group, compared with 44% ± 5.5% in the control group. Moreover, the cellular yield of the control group resulted in 267,000 ± 94,107 adipose-derived stem cells/mL, while the study group resulted in 528,895 ± 115,853 adipose-derived stem cells /mL, with a p-value = 0.1805. Interestingly, the study group showed a fold increase in cell growth of 6758 ± 0.7122, while the control group resulted in 3888 ± 0.3078, with a p < 0.05 (p = 0.0122).ConclusionsThe comparison of both groups indicated that washing and filtration were a better efficient system in lipofilling preparation, compared to centrifugation, both in terms of volume maintenance and adipose-derived stem cell growth ability.Level of Evidence IIIThis journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266.