Development of craniofacial superimposition: a review / Desarrollo de la superposición craneofacial: una revisión

SUMMARY: Craniofacial superimposition is a method for identifying individuals by using secondary data in order to identify a target group of persons before a DNA process can be used, or to identify an individual instead of using primary data in cases where DNA, fingerprint or dental records are not found. Craniofacial superimposition has continued to evolve, with various techniques, including computer-assisted and photography techniques, to help the operation be more convenient, faster and reliable. The knowledge of forensic anthropology is applied, with a comparison between anatomical landmarks. The study of developments in craniofacial superimposition using computer-assistance has yielded satisfactory results.

La superposición craneofacial es un método para identificar individuos mediante el uso de datos secundarios, se utiliza para identificar un grupo objetivo de personas, antes de que se pueda utilizar un proceso de ADN, o para identificar a un individuo en lugar de utilizar datos primarios en los casos en que no se cuenta con registros de ADN, huellas dactilares o dentales. La superposición craneofacial ha seguido evolucionando, con diversas técnicas, incluidas las técnicas fotográficas y asistidas por computador, para ayudar a que la operación sea más conveniente, rápida y confiable. Se aplica el conocimiento de la antropología forense, con una comparación entre hitos anatómicos. El estudio de la evolución de la superposición craneofacial con asistencia informática ha arrojado resultados satisfactorios.

Tomografía computarizada con reconstrucción 3D para el diagnóstico de lesiones traumáticas de uretra / Three-dimensional computed tomography reconstruction for the diagnosis of traumatic urethral injuries

RESUMEN La uretrografía retrógrada es la técnica de referencia (gold standard) utilizada clásicamente para hacer diagnóstico de lesiones de uretra. En este contexto se presenta un caso en el que se realizó tomografía computarizada con reconstrucción 3D con contraste intravenoso y endouretral, pudiendo reconstruir la uretra en toda su extensión en forma tridimensional. De esta manera se arribó al diagnóstico de certeza de la lesión de uretra. Como ventaja del método se menciona la posibilidad de diagnosticar ‒ con un solo estudio por imágenes‒ lesiones de todo el tracto urinario, órganos sólidos, huecos y lesión del anillo pélvico asociados al traumatismo, con una alta sensibilidad y especificidad sin necesidad de requerir otros estudios complementarios.

ABSTRACT Retrograde urethrography is the gold standard method for the diagnosis of urethral injuries. In this setting, we report the use of computed tomography with intravenous injection and urethral administration of contrast medium and 3D reconstruction of the entire urethra. The definitive diagnosis of urethral injury was made. The advantage of this method is the possibility of making the diagnosis of traumatic injuries of the entire urinary tract, solid organs, hollow viscera and of the pelvic ring within a single imaging test, with high sensitivity and specificity, with no need to perform other complementary tests.

Aplicación de modelos 3D en cirugía hepática / Implementation of three-dimensional printed models in hepatic surgery

RESUMEN La impresión de modelos tridimensionales (M3D) implica obtener una estructura sólida y formada a partir de un modelo digital. Para la reconstrucción 3D se utilizó tomografía computarizada contrastada, realizándose impresión de modelos sobre la base de las principales estructuras anatómicas hepáticas. Se utilizaron M3D en dos pacientes con indicación quirúrgica, una mujer con trombocitopenia familiar y metástasis hepática de adenocarcinoma rectal, sin respuesta a quimioterapia, y un hombre con hepatopatía infecciosa crónica y diagnóstico de carcinoma hepatocelular. La aplicación de M3D resultó de gran utilidad, pues permitió un mejor entendimiento de la relación espacial de las estructuras anatómicas en ambos casos. En nuestra experiencia, la aplicación de M3D fue muy útil para planificar la cirugía y dar una aproximación más certera de los reparos anatómicos. El modelo se obtuvo en 7 días y costó 380 dólares, un valor elevado para nuestro medio.

ABSTRACT Three-dimensional (3D) printing is the construction of a solid structure from a digital model. 3D reconstruction was performed using contrast-enhanced computed tomography scan, and 3D-printed models were built based on the main anatomic structures of the liver. 3D-printed models were used in two patients with indication of surgery; one woman with inherited thrombocytopenia and liver metastases from colorectal adenocarcinoma with no response to chemotherapy, and one man with chronic liver infection and hepatocellular carcinoma. The implementation of 3D printing technology was very useful, as it facilitated the understanding of the spatial relationships among the anatomical structures in both cases. In our experience, the use of 3D-printed models was very useful for preoperative planning and for understanding the anatomic landmarks. The model was built in 7 days, with a cost of 380 dollars which is elevated in our environment.

Impact of different evaluation methods for diagnostic tasks in panoramic radiography / Impacto de diferentes métodos de evaluación durante el diagnóstico en la radiografía panorámica

Abstract The aim of this study was to evaluate the observers diagnostic performance in panoramic radiography using monitor, tablet, X-ray image view box, and against window daylight as a visualization method in different diagnostic tasks. Thirty panoramic radiography were assessed by three calibrated observers for each visualization method, in standardized light conditions, concerning dental caries, widened periodontal ligament space, and periapical bone defects from the four first molars; mucosal thickening and retention cysts in maxillary sinus; and stylo-hyoid ligament calcification and atheroma. A five-point confidence scale was used. The standard-reference was performed by two experienced observers. Diagnostic values using window light were significantly lower for caries and periapical bone defect and retention cyst, stylo-hyoid ligament calcification detection (p<0.05). For atheroma detection, X-ray image view box, tablet, and widow light had lower accuracy than the evaluation on the monitor (p<0.05). Observers diagnostic performances are worsened using window light as an evaluation method for panoramic radiography for dental, sinus, and calcification disorders, while the monitor was the most reliable method.

Resumen El objetivo de este estudio fue evaluar el desempeño diagnóstico de los observadores en la radiografía panorámica utilizando monitor, tablet, caja de visualización de imágenes de rayos X y contra la luz del día de la ventana como método de visualización en diferentes tareas de diagnóstico. Treinta radiografías panorámicas fueron evaluadas por tres observadores calibrados para cada método de visualización, en condiciones de luz estandarizadas, con respecto a caries dental, espacio del ligamento periodontal ensanchado y defectos óseos periapicales de los cuatro primeros molares; engrosamiento de la mucosa y quistes de retención en el seno maxilar; y calcificación y ateroma del ligamento estilohioideo. Se utilizó una escala de confianza de cinco puntos. La referencia estándar fue realizada por dos observadores experimentados. Los valores diagnósticos con luz de ventana fueron significativamente menores para caries y defecto óseo periapical y quiste de retención, detección de calcificación del ligamento estilohioideo (p <0.05). Para la detección de ateroma, la caja de visualización de imágenes de rayos X, el tablet y la luz de viuda tuvieron una precisión menor que la evaluación en el monitor (p <0.05). El rendimiento diagnóstico del observador empeora al utilizar la luz de la ventana como método de evaluación de la radiografía panorámica para los trastornos dentales, de los senos nasales y de la calcificación, mientras que el monitor fue el método más fiable.

Fractal evaluation in retinographies of individuals with different grades of diabetic retinopathy / Avaliação fractal de retinografias de indivíduos com diferentes graus de retinopatia diabética

ABSTRACT Purpose To evaluate the retinal blood vascular network of the retinographies of patients with different grades of diabetic retinopathy. Methods Ninety Retinographies (MESSIDOR database) were used, with different grades of diabetic retinopathy divided into 4 groups: no retinopathy (n=23), grade one (n=20), grade two (n=20) and grade three (n=27) diabetic retinopathy. The grades of diabetic retinopathy were classified according to the number of microaneurysms, number of hemorrhages and the presence of neovascularization. The images were skeletonized and quantified by fractal methods: dimension of box-counting (Dbc) and information (Dinf). Results The means of Dbc values of groups were around 1.25, without statistically significant difference in the dimension values between groups for whole retina. There was also no statistical difference in Dinf values between groups, whose means ranged between 1.294 ± 0.013 (group of grade 1) and 1.3 ± 0.017 (group of grade 3). The retinographies were divided into regions of equal areas. The fractal values of some retinal regions showed statistical differences, but these differences were not enough to show the sensitivity of fractal methods in identifying diabetic retinopathy. Conclusion The fractal methods were not able to identify the different grades of diabetic retinopathy in retinographies.

RESUMO Objetivo Avaliar a rede vascular sanguínea da retina a partir de retinografias de pacientes com diferentes graus de retinopatia diabética. Métodos Foram utilizadas 90 retinografias (banco de dados MESSIDOR), com diferentes graus de retinopatia diabética divididas em quatro grupos: sem retinopatia (n=23), retinopatia diabética de grau um (n=20), grau dois (n=20) e grau três (n=27). Os graus de retinopatia foram classificados conforme o número de microaneurismas, número de hemorragias e presença de neovascularização. As imagens foram esqueletizadas e quantificadas pelos métodos fractais: dimensão da contagem de caixas e informação. Resultados As médias dos valores das dimensões de contagem de caixas para todos os grupos foram próximas a 1,25, sem diferença estatisticamente significativa nos valores das dimensões entre os grupos para retina inteira. Também não houve diferença estatística nos valores da dimensão de informação entre os grupos, cujas médias variaram entre 1,294 ± 0,013 (grupo do grau 1) e 1,3 ± 0,017 (grupo do grau 3). As imagens retinianas foram divididas em regiões de áreas iguais. Os valores fractais de algumas regiões retinais mostraram diferenças estatísticas, mas estas não foram suficientes para mostrar a sensibilidade dos métodos fractais na identificação da retinopatia diabética. Conclusão Os métodos fractais não foram capazes de identificar os diferentes graus de retinopatia diabética em retinografias.

Deep Learning aplicado en imágenes fotoacústicas para la Identificación del cáncer de seno / Deep Learning Applied in Photoacoustic Images for the Identification of Breast Cancer

La Imagen Fotoacústica (PAI por sus siglas en inglés), es una modalidad de imagen híbrida que fusiona la iluminación óptica y la detección por ultrasonido. Debido a que los métodos de imágenes ópticas puras no pueden mantener una alta resolución, la capacidad de lograr imágenes de contraste óptico de alta resolución en tejidos biológicos hace que la fotoacústica (PA por sus siglas en inglés) sea una técnica prometedora para varias aplicaciones de imágenes clínicas. En la actualidad el Aprendizaje Profundo (Deep Learning) tiene el enfoque más reciente en métodos basados en la PAI, donde existe una gran cantidad de aplicaciones en análisis de imágenes, en especial en el área del campo biomédico, como lo es la adquisición, segmentación y reconstrucciones de imágenes de tomografía computarizada. Esta revisión describe las últimas investigaciones en PAI y un análisis sobre las técnicas y métodos basados en Deep Learning, aplicado en diferentes modalidades para el diagnóstico de cáncer de seno(AU)

Photoacoustic Imaging (PAI) is a hybrid imaging modality that combines optical illumination and ultrasound detection. Because pure optical imaging methods cannot maintain high resolution, the ability to achieve high resolution optical contrast images in biological tissues makes Photoacoustic (PA) a promising technique for various clinical imaging applications. At present, Deep Learning has the most recent approach of methods based on PAI where there are a large number of applications in image analysis especially in the area of ​​the biomedical field, such as acquisition, segmentation and reconstructions of computed tomography imaging. This review describes the latest research in PAI and an analysis of the techniques and methods based on Deep Learning applied in different modalities for the diagnosis of breast cancer(AU)

Infrared Imaging Meibomian Gland Segmentation System Based on Deep Learning / 中国医疗器械杂志

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.

The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images / 中国医疗器械杂志

Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V（ ASiR-V） and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning. Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset. Then structures including three soft tissue organs (bladder, bowelbag, small intestine) and five bone organs (left and right femoral head, left and right femur, pelvic) were segmented automatically by deep learning neural network. Performance was evaluated by dice similarity coefficient（ DSC） and Hausdorff distance, using filter back projection(FBP) as the reference. Results Auto-segmentation of deep learning is greatly affected by ASIR-V, but less affected by convolution kernel, especially in soft tissues. Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm. In practical application, it is necessary to find a balance between image quality and segmentation quality, or improve segmentation network to enhance the stability of auto-segmentation.

Automatic Post-operative Cervical Cancer Target Area and Organ at Risk Outlining Based on Fusion Convolutional Neural Network / 中国医疗器械杂志

CT image based organ segmentation is essential for radiotherapy treatment planning, and it is laborious and time consuming to outline the endangered organs and target areas before making radiation treatment plans. This study proposes a fully automated segmentation method based on fusion convolutional neural network to improve the efficiency of physicians in outlining the endangered organs and target areas. The CT images of 170 postoperative cervical cancer stage IB and IIA patients were selected for network training and automatic outlining of bladder, rectum, femoral head and CTV, and the neural network was used to localize easily distinguishable vessels around the target area to achieve more accurate outlining of CTV.

Sparse-view Cone-beam Breast CT Reconstruction via cGAN Constrained by Image Edges / 中国医疗器械杂志

Clinical applications of cone-beam breast CT(CBBCT) are hindered by relatively higher radiation dose and longer scan time. This study proposes sparse-view CBBCT, i.e. with a small number of projections, to overcome the above bottlenecks. A deep learning method - conditional generative adversarial network constrained by image edges (ECGAN) - is proposed to suppress artifacts on sparse-view CBBCT images reconstructed by filtered backprojection (FBP). The discriminator of the ECGAN is the combination of patchGAN and LSGAN for preserving high frequency information, with a modified U-net as the generator. To further preserve subtle structures and micro calcifications which are particularly important for breast cancer screening and diagnosis, edge images of CBBCT are added to both the generator and the discriminator to guide the learning. The proposed algorithm has been evaluated on 20 clinical raw datasets of CBBCT. ECGAN substantially improves the image qualities of sparse-view CBBCT, with a performance superior to those of total variation (TV) based iterative reconstruction and FBPConvNet based post-processing. On one CBBCT case with the projection number reduced from 300 to 100, ECGAN enhances peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) on FBP reconstruction from 24.26 and 0.812 to 37.78 and 0.963, respectively. These results indicate that ECGAN successfully reduces radiation dose and scan time of CBBCT by 1/3 with only small image degradations.

Research Progress and Application of Digital Pathology Technology in Bone Marrow Morphology / 中国医疗器械杂志

Advances in digital pathology technology have enabled pathologists and laboratory physicians to perform quick, easy, accurate and reproducible analysis of digital images of tissues and cells with the aid of electronic screens and software tools, rather than relying solely on traditional optical microscopy observations. The conventional clinical cytology testing practice is to be replaced by a digital workflow, which includes both digital imaging and image analysis. This article provides an overview of the basic principles of digital pathology techniques, the advances of development of device in cytology digital pathology, and their clinical applications in bone marrow morphology, and existing problems and prospects of digital pathology application in hematology.

Research Progress in 3D-reconstruction Based Imaging Analysis in Partial Solid Pulmonary Nodule / 中国肺癌杂志

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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A computed tomography image segmentation algorithm for improving the diagnostic accuracy of rectal cancer based on U-net and residual block / 生物医学工程学杂志

As an important basis for lesion determination and diagnosis, medical image segmentation has become one of the most important and hot research fields in the biomedical field, among which medical image segmentation algorithms based on full convolutional neural network and U-Net neural network have attracted more and more attention by researchers. At present, there are few reports on the application of medical image segmentation algorithms in the diagnosis of rectal cancer, and the accuracy of the segmentation results of rectal cancer is not high. In this paper, a convolutional network model of encoding and decoding combined with image clipping and pre-processing is proposed. On the basis of U-Net, this model replaced the traditional convolution block with the residual block, which effectively avoided the problem of gradient disappearance. In addition, the image enlargement method is also used to improve the generalization ability of the model. The test results on the data set provided by the "Teddy Cup" Data Mining Challenge showed that the residual block-based improved U-Net model proposed in this paper, combined with image clipping and preprocessing, could greatly improve the segmentation accuracy of rectal cancer, and the Dice coefficient obtained reached 0.97 on the verification set.

A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images / 南方医科大学学报

OBJECTIVE@#To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images.@*METHODS@#We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method.@*RESULTS@#The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001).@*CONCLUSION@#The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.

Performance of low-dose CT image reconstruction for detecting intracerebral hemorrhage: selection of dose, algorithms and their combinations / 南方医科大学学报

OBJECTIVE@#To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage.@*METHODS@#Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images.@*RESULTS@#At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively.@*CONCLUSION@#The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.

Multimodality-based super-resolution reconstruction for routine brain magnetic resonance images / 南方医科大学学报

OBJECTIVE@#To propose a multi-modality-based super-resolution synthesis model for reconstruction of routine brain magnetic resonance images (MRI) with a low resolution and a high thickness into high-resolution images.@*METHODS@#Based on real paired low-high resolution MRI data (2D T1, 2D T2 FLAIR and 3D T1), a structure-constrained image mapping network was used to extract important features from the images with different modalities including the whole T1 and subcortical regions of T2 FLAIR to reconstruct T1 images with higher resolutions. The gray scale intensity and structural similarities between the super-resolution images and high-resolution images were used to enhance the reconstruction performance. We used the anatomical information acquired from segment maps of the super-resolution T1 image and the ground truth by a segmentation tool as a significant constraint for adaptive learning of the intrinsic tissue structure characteristics of the brain to improve the reconstruction performance of the model.@*RESULTS@#Our method showed the performance on the testing dataset than other methods with an average PSNR of 33.11 and SSIM of 0.996. The anatomical structure of the brain including the sulcus, gyrus, and subcortex were all reconstructed clearly using the proposed method, which also greatly enhanced the precision of MSCSR for brain volume measurement.@*CONCLUSION@#The proposed MSCSR model shows excellent performance for reconstructing super-resolution brain MR images based on the information of brain tissue structure and multimodality MR images.

An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction / 南方医科大学学报

OBJECTIVE@#To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction.@*METHODS@#A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods.@*RESULTS@#For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001).@*CONCLUSION@#The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.

Research on knee sports injury classification based on medical images and kwon3d software / Pesquisa sobre classificação de lesões esportivas no joelho com base em imagens médicas e software kwon3d / Investigación sobre clasificación de lesiones deportivas de rodilla basada en imágenes médicas y software kwon3d

ABSTRACT Introduction: The integrity of articular cartilage determines the functional state of the joint. In recent years, the development of MRI sequences of various articular cartilage has become the focus of many research topics. Objective: The accuracy of diagnosis of knee cartilage injury caused by motion injury was studied retrospectively by meta-three-dimensional software. Methods: Forty-six knee joints of 45 patients with sports injuries, multi-sequence MRI was performed before surgery, including conventional knee MRI (SET1WI, FSEPD/T2WI), 3D SPGR, and 3D FIESTA sequences. Results: According to the operation results, the sensitivity, specificity, positive predictive value, and negative predictive value of 3D SPGR combined with conventional MRI sequence evaluation of cartilage damage are the highest, 73%, 98%, 95%, and 90%. Conclusions: 3D SPGR combined with conventional MRI sequences can improve accurate evaluation and diagnosis of cartilage disease over a reasonable scan time. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Introdução: A integridade da cartilagem articular determina o estado funcional da articulação. Nos últimos anos, o desenvolvimento de sequências de ressonância magnética de várias cartilagens articulares se tornou o foco de muitos tópicos de pesquisa. Objetivo: A precisão do diagnóstico de lesão da cartilagem do joelho causada por lesão de movimento foi estudada retrospectivamente por software meta-tridimensional. Métodos: Quarenta e seis articulações de joelho de 45 pacientes com lesões esportivas, várias sequências de ressonância magnética foram realizadas antes da cirurgia, incluindo ressonância magnética de joelho convencional (SET1WI, FSEPD / T2WI), 3D SPGR e sequências 3D FIESTA. Resultados: De acordo com os resultados da operação, a sensibilidade, especificidade, valor preditivo positivo e valor preditivo negativo de 3D SPGR combinado com avaliação de sequência de ressonância magnética convencional de danos na cartilagem são os mais altos, 73%, 98%, 95% e 90%. Conclusões: 3D SPGR combinado com sequências convencionais de ressonância magnética pode melhorar a avaliação precisa e diagnóstico de doença da cartilagem em um tempo de varredura razoável. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento.

RESUMEN Introducción: La integridad del cartílago articular determina el estado funcional de la articulación. En los últimos años, el desarrollo de secuencias de resonancia magnética de varios cartílagos articulares se ha convertido en el foco de muchos temas de investigación. Objetivo: La precisión del diagnóstico de la lesión del cartílago de la rodilla causada por una lesión por movimiento se estudió retrospectivamente mediante un software meta-tridimensional. Métodos: Cuarenta y seis articulaciones de rodilla de 45 pacientes con lesiones deportivas, se realizó una resonancia magnética de secuencia múltiple antes de la cirugía, incluida la resonancia magnética de rodilla convencional (SET1WI, FSEPD/T2WI), secuencias 3D SPGR y 3D FIESTA. Resultados: De acuerdo con los resultados de la operación, la sensibilidad, la especificidad, el valor predictivo positivo y el valor predictivo negativo de 3D SPGR combinados con la evaluación de la secuencia de resonancia magnética convencional del daño del cartílago son los más altos, 73%, 98%, 95% y 90%. Conclusiones: 3D SPGR combinado con secuencias de resonancia magnética convencionales puede mejorar la evaluación y el diagnóstico precisos de la enfermedad del cartílago en un tiempo de exploración razonable. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.

Analysis of biolo artificial neural network in prediction of aerobic exercise index based on algorithm / Análise de rede neural artificial na predição do índice de exercício aeróbio baseada em algorítimo / Análisis de red neuronal artificial en la predicción del índice de ejercicio aeróbico

ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects' body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Objetivo: Com o objetivo de estudar a relação entre atividade aeróbia e atividade nervosa autonômica cardíaca por algoritmo de rede neural artificial e fusão biológica de imagens, tendo em vista os problemas existentes no modelo de rede neural artificial (RNA), é introduzida a tecnologia de processamento biológico de imagens com base em ANN. Métodos: um algoritmo de inteligência biológica de imagem Ann é proposto, um classificador adequado para triagem eletrocardiográfica (ECG) é projetado e um sistema de triagem de sinal de ECG é estabelecido com sucesso. Além disso, o conjunto de dados de sinais de ECG normais recuperados dos sujeitos durante o período experimental é construído e um classificador é usado para extrair os dados característicos de um sinal de ECG normal durante o período experimental. Resultados: As alterações na frequência cardíaca em repouso e outros indicadores de saúde física são analisadas pela combinação de indicadores fisiológicos de repouso, a saber, frequência cardíaca, peso corporal, índice de massa corporal e índice de gordura corporal. Os resultados mostram que o classificador autodesenhado pode processar com eficiência as imagens de ECG, e as atividades regulares de longo prazo podem melhorar as condições físicas da maioria das pessoas. O peso corporal e a taxa de gordura corporal da maioria dos indivíduos diminuem com a extensão do tempo do experimento, e a freqüência cardíaca em repouso diminui relativamente. Conclusões: Certos indicadores podem ser usados para prever a saúde física dinâmica de uma pessoa, o que indica que a pesquisa experimental de predição de índice nesta pesquisa tem um bom efeito, que não apenas estende a aplicação da rede neural artificial, mas também estabelece uma base para a pesquisa e implementação de dispositivos vestíveis de teste inteligente de ECG. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento.

RESUMEN Objetivo: Para estudiar la relación entre la actividad aeróbica y la actividad del nervio autónomo cardíaco mediante el algoritmo de red neuronal artificial y la fusión de imágenes biológicas, ante los problemas existentes en el modelo de red neuronal artificial (ANN), se introduce la tecnología de procesamiento de imágenes biológicas basada en ANA. Métodos: Se propone un algoritmo de inteligencia de imagen biológica de Ann, se diseña un clasificador adecuado para el cribado electrocardiógrafo (ECG) y se establece con éxito un sistema de cribado de señales de ECG. Además, se construye el conjunto de datos de las señales de ECG recuperadas normales de los sujetos durante el período experimental, y se utiliza un clasificador para extraer los datos característicos de una señal de ECG normal durante el período experimental. Resultados: Los cambios en la frecuencia cardíaca en reposo y otros indicadores de salud física se analizan combinando indicadores fisiológicos en reposo, a saber, frecuencia cardíaca, peso corporal, índice de masa corporal y tasa de grasa corporal. Los resultados muestran que el clasificador de diseño propio puede procesar de manera eficiente las imágenes de ECG, y las actividades regulares a largo plazo pueden mejorar las condiciones físicas de la mayoría de las personas. El peso corporal y la tasa de grasa corporal de la mayoría de los sujetos disminuyen con la extensión del tiempo del experimento, y la frecuencia cardíaca en reposo disminuye relativamente. Conclusiones: Ciertos indicadores pueden usarse para predecir la salud física dinámica de una persona, lo que indica que la investigación experimental de predicción de índices en esta investigación tiene un buen efecto, lo que no solo extiende la aplicación de la red neuronal artificial sino que también sienta las bases para la investigación. e implementación de dispositivos portátiles de prueba inteligente de ECG. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.

Reconstrucción estético - funcional de labios menores post amputación Iatrogénica amplia con colgajo de avance V - Y / Aesthetic-functional reconstruction of labia minora after extensive iatrogenic amputation with V-Y advancement flap

La hipertrofia de labios menores es la prolongación de estos más allá de los límites anatómicos de los labios mayores. La creencia de la simplicidad en la reducción de los labios menores y la falla en observar importantes aspectos de la técnica quirúrgica que llevan a la resección total del labio. En tales casos, la cirugía reconstructiva es la única forma posible de rectificar la situación. Presentamos el caso de una mujer de 36 años que acude por amputación de los labios menores secundario a cirugía de labioplastia por hipertrofia realizada por médico esteticista. Se realiza reconstrucción de labios menores en dos tiempos quirúrgicos. Los colgajos de avance en V-Y del capuchón del clítoris, con remanentes de tejido de la horquilla posterior, pueden lograr resultados satisfactorios y permitir la adaptación a la anatomía genital y los deseos estéticos únicos de cada mujer.

Labia minora hypertrophy is the prolongation of these beyond the anatomical limits of the labia majora. The belief in simplicity in the reduction of the labia minora and the failure to observe important aspects of the surgical technique that lead to total lip resection. In such cases, reconstructive surgery is the only possible way to rectify the situation. A 36-year-old woman with medical history of labia minora amputation secondary to labiaplasty surgery for hypertrophy of the labia minora performed by a beautician. The labia minora reconstruction is performed in two surgical stages. The V-Y advancement flaps of the clitoral hood with remnants of tissue from the posterior fork can be achieved with satisfactory results and allow adaptation to the genital anatomy and unique aesthetic wishes of each woman.