Pregnancy state before the onset of labor: a holistic mechanical perspective.

Successful pregnancy highly depends on the complex interaction between the uterine body, cervix, and fetal membrane. This interaction is synchronized, usually following a specific sequence in normal vaginal deliveries: (1) cervical ripening, (2) uterine contractions, and (3) rupture of fetal membrane. The complex interaction between the cervix, fetal membrane, and uterine contractions before the onset of labor is investigated using a complete third-trimester gravid model of the uterus, cervix, fetal membrane, and abdomen. Through a series of numerical simulations, we investigate the mechanical impact of (i) initial cervical shape, (ii) cervical stiffness, (iii) cervical contractions, and (iv) intrauterine pressure. The findings of this work reveal several key observations: (i) maximum principal stress values in the cervix decrease in more dilated, shorter, and softer cervices; (ii) reduced cervical stiffness produces increased cervical dilation, larger cervical opening, and decreased cervical length; (iii) the initial cervical shape impacts final cervical dimensions; (iv) cervical contractions increase the maximum principal stress values and change the stress distributions; (v) cervical contractions potentiate cervical shortening and dilation; (vi) larger intrauterine pressure (IUP) causes considerably larger stress values and cervical opening, larger dilation, and smaller cervical length; and (vii) the biaxial strength of the fetal membrane is only surpassed in the cases of the (1) shortest and most dilated initial cervical geometry and (2) larger IUP.

Dual-Task vs. Single-Task Gait Training to Improve Spatiotemporal Gait Parameters in People with Parkinson's Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: People with Parkinson's disease (pwPD) present alterations of spatiotemporal gait parameters that impact walking ability. While preliminary studies suggested that dual-task gait training improves spatiotemporal gait parameters, it remains unclear whether dual-task gait training specifically improves dual-task gait performance compared to single-task gait training. The aim of this review is to assess the effect of dual-task training relative to single-task gait training on specific gait parameters during dual-task tests in pwPD. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs), searching three electronic databases. Two reviewers independently selected RCTs, extracted data, and applied the Cochrane risk-of-bias tool for randomized trials (Version 2) and the GRADE framework for assessing the certainty of evidence. The primary outcomes were dual-task gait speed, stride length, and cadence. Secondary outcomes included dual-task costs on gait speed, balance confidence, and quality of life. RESULTS: We included 14 RCTs (548 patients). Meta-analyses showed effects favoring dual-task training over single-task training in improving dual-task gait speed (standardized mean difference [SMD] = 0.48, 95% confidence interval [CI] = 0.20-0.77; 11 studies; low certainty evidence), stride length (mean difference [MD] = 0.09 m, 95% CI = 0.04-0.14; 4 studies; very low certainty evidence), and cadence (MD = 5.45 steps/min, 95% CI = 3.59-7.31; 5 studies; very low certainty evidence). We also found a significant effect of dual-task training over single-task training on dual-task cost and quality of life, but not on balance confidence. CONCLUSIONS: Our findings support the use of dual-task training relative to single-task training to improve dual-task spatiotemporal gait parameters in pwPD. Further studies are encouraged to better define the features of dual-task training and the clinical characteristics of pwPD to identify better responders.

An estimation of the biomechanical properties of the continent and incontinent woman bladder via inverse finite element analysis.

Stress urinary incontinence often results from pelvic support structures' weakening or damage. This dysfunction is related to direct injury of the pelvic organ's muscular, ligamentous or connective tissue structures due to aging, vaginal delivery or increase of the intra-abdominal pressure, for example, defecation or due to obesity. Mechanical changes alter the soft tissues' microstructural composition and therefore may affect their biomechanical properties. This study focuses on adapting an inverse finite element analysis to estimate the in vivo bladder's biomechanical properties of two groups of women (continent group (G1) and incontinent group (G2)). These properties were estimated based on MRI, by comparing measurement of the bladder neck's displacements during dynamic MRI acquired in Valsalva maneuver with the results from inverse analysis. For G2, the intra-abdominal pressure was adjusted after applying a 95% impairment to the supporting structures. The material parameters were estimated for the two groups using the Ogden hyperelastic constitutive model. Finite element analysis results showed that the bladder tissue of women with stress urinary incontinence have the highest stiffness (α1 = 0.202 MPa and µ1 = 7.720 MPa) approximately 47% higher when compared to continent women. According to the bladder neck's supero-inferior displacement measured in the MRI, the intra-abdominal pressure values were adjusted for the G2, presenting a difference of 20% (4.0 kPa for G1 and 5.0 kPa for G2). The knowledge of the pelvic structures' biomechanical properties, through this non-invasive methodology, can be crucial in the choice of the synthetic mesh to treat dysfunction when considering personalized options.

Development of a multilayer fetal membrane material model calibrated using bulge inflation mechanical tests.

The fetal membranes are an essential mechanical structure for pregnancy, protecting the developing fetus in an amniotic fluid environment and rupturing before birth. In cooperation with the cervix and the uterus, the fetal membranes support the mechanical loads of pregnancy. Structurally, the fetal membranes comprise two main layers: the amnion and the chorion. The mechanical characterization of each layer is crucial to understanding how each layer contributes to the structural performance of the whole membrane. The in-vivo mechanical loading of the fetal membranes and the amount of tissue stress generated in each layer throughout gestation remains poorly understood, as it is difficult to perform direct measurements on pregnant patients. Finite element analysis of pregnancy offers a computational method to explore how anatomical and tissue remodeling factors influence the load-sharing of the uterus, cervix, and fetal membranes. To aid in the formulation of such computational models of pregnancy, this work develops a fiber-based multilayer fetal membrane model that captures its response to previously published bulge inflation loading data. First, material models for the amnion, chorion, and maternal decidua are formulated, informed, and validated by published data. Then, the behavior of the fetal membrane as a layered structure was analyzed, focusing on the respective stress distribution and thickness variation in each layer. The layered computational model captures the overall behavior of the fetal membranes, with the amnion being the mechanically dominant layer. The inclusion of fibers in the amnion material model is an important factor in obtaining reliable fetal membrane behavior according to the experimental dataset. These results highlight the potential of this layered model to be integrated into larger biomechanical models of the gravid uterus and cervix to study the mechanical mechanisms of preterm birth.

Direct Energy Deposition Parametric Simulation Investigation in Gear Repair Applications.

Additive manufacturing technologies have numerous advantages over conventional technologies; nevertheless, their production process can lead to high residual stresses and distortions in the produced parts. The use of numerical simulation models is presented as a solution to predict the deformations and residual stresses resulting from the printing process. This study aimed to predict the tensions and distortions imposed in the gear repair process by directed energy deposition (DED). First, the case study proposed by National Institute of Standards and Technology (NIST) was analyzed to validate the model and the numerically obtained results. Subsequently, a parametric study of the influence of some of the parameters of DED technology was carried out. The results obtained for the validation of the NIST benchmark bridge model were in agreement with the results obtained experimentally. In turn, the results obtained from the parametric study were almost always in line with what is theoretically expected; however, some results were not very clear and consistent. The results obtained help to clarify the influence of certain printing parameters. The proposed model allowed accounting for the effect of residual stresses in calculating the stresses resulting from gear loading, which are essential data for fatigue analysis. Modeling and simulating a deposition process can be challenging due to several factors, including calibrating the model, managing the computational cost, accounting for boundary conditions, and accurately representing material properties. This paper aimed to carefully address these parameters in two case studies, towards reliable simulations.

Improvement and validation of a female finite element model of the cervical spine.

Although the cervical spine supports and controls the kinematics of the head, it is vulnerable to injuries during mechanical loading. Severe injuries often result in damage to the spinal cord, leading to significant ramifications. The role of gender in determining the outcome of such injuries has been established as significant. In order to better understand the essential mechanics and develop treatments or preventative measures, various forms of research have been conducted. Computational modelling is one of the most useful and extensively utilised methods, as it provides information that would otherwise be difficult to obtain. As such, the primary goal of this research is to create a new finite element of the female cervical spine that will more accurately represent the group most affected by such injuries. This work is a continuation of a previous study where a model was created from the computer tomography scans of a 46-year-old female. A functioning spinal unit consisting of the C6-C7 segment was simulated as a validation procedure. The experimental data obtained from cadaveric specimens, that assessed the range of motion of different cervical segments in flexion-extension, axial rotation, and lateral bending, was used to validate the reduced model.

Numerical Design of a Thread-Optimized Gripping System for Lap Joint Testing in a Split Hopkinson Apparatus.

Currently, few experimental methods exist that enable the mechanical characterization of adhesives under high strain rates. One such method is the Split Hopkinson Bar (SHB) test. The mechanical characterization of adhesives is performed using different specimen configurations, such as Single Lap Joint (SLJ) specimens. A gripping system, attached to the bars through threading, was conceived to enable the testing of SLJs. An optimization study for selecting the best thread was performed, analyzing the thread type, the nominal diameter, and the thread pitch. Afterwards, the gripping system geometry was numerically evaluated. The optimal threaded connection for the specimen consists of a trapezoidal thread with a 14 mm diameter and a 2 mm thread pitch. To validate the gripping system, the load-displacement (P-δ) curve of an SLJ, which was simulated as if it were tested on the SHB apparatus, was compared with an analogous curve from a validated drop-weight test numerical model.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.

BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions. METHODS: a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values. RESULTS: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second. CONCLUSIONS: we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network

Background and aims: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions. Methods: a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values. Results: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second. Conclusions: we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE (AU)

Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network.

Introduction: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods: A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results: Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion: We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

Introdução: A endoscopia por cápsula revolucionou a abordagem a doentes com hemorragia digestiva obscura. No entanto, a leitura de imagens de endoscopia por cápsula é morosa, havendo suscetibilidade para a perda de lesões significativas, limitando desta forma a sua eficácia diagnóstica. Este estudo visou a criação de um algoritmo de deep learning para deteção automática de sangue e resíduos hemáticos no lúmen entérico usando imagens de endoscopia por cápsula. Métodos: Foi desenvolvida uma rede neural convolucional com base num conjunto de 22,095 imagens de endoscopia de cápsula (13,510 imagens contendo sangue e 8,585 mucosa normal ou outros achados). Foi construído um grupo de imagens para treino, compreendendo 80% do total de imagens. O desempenho da rede foi comparado com a classificação consenso de dois especialistas em endoscopia por cápsula. Posteriormente, o desempenho da rede foi avaliado usando os restantes 20% de imagens. Foi calculada a sua sensibilidade, especificidade, exatidão e precisão. Resultados: O algoritmo detetou sangue e resíduos hemáticos no lúmen do intestino delgado com uma exatidão e precisão de 98.5% e 98.7%, respetivamente. A sensibilidade e especificidade foram 98.6% e 98.9%, respetivamente. A análise do conjunto de usado para teste da rede foi concluída em 24 segundos (aproximadamente 184 frames/s). Discussão/Conclusão: Foi desenvolvida uma ferramenta de inteligência artificial capaz de detetar efetivamente o sangue luminal. O desenvolvimento dessas ferramentas pode aumentar a precisão do diagnóstico da endoscopia por cápsula ao avaliar pacientes que apresentam sangramento obscuro do intestino delgado.

A biomechanical study of the birth position: a natural struggle between mother and fetus.

Birth trauma affects millions of women and infants worldwide. Levator ani muscle avulsions can be responsible for long-term morbidity, associated with 13-36% of women who deliver vaginally. Pelvic floor injuries are enhanced by fetal malposition, namely persistent occipito-posterior (OP) position, estimated to affect 1.8-12.9% of pregnancies. Neonates delivered in persistent OP position are associated with an increased risk for adverse outcomes. The main goal of this work was to evaluate the impact of distinct fetal positions on both mother and fetus. Therefore, a finite element model of the fetal head and maternal structures was used to perform childbirth simulations with the fetus in the occipito-anterior (OA) and OP position of the vertex presentation, considering a flexible-sacrum maternal position. Results demonstrated that the pelvic floor muscles' stretch was similar in both cases. The maximum principal stresses were higher for the OP position, and the coccyx rotation reached maximums of 2.17[Formula: see text] and 0.98[Formula: see text] for the OP and OA positions, respectively. Concerning the fetal head, results showed noteworthy differences in the variation of diameters between the two positions. The molding index is higher for the OA position, with a maximum of 1.87. The main conclusions indicate that an OP position can be more harmful to the pelvic floor and pelvic bones from a biomechanical point of view. On the other side, an OP position can be favorable to the fetus since fewer deformations were verified. This study demonstrates the importance of biomechanical analyses to further understand the mechanics of labor.

Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy.

Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

Biomechanical characterization of the small intestine to simulate gastrointestinal tract chyme propulsion.

Regular intestinal motility is essential to guarantee complete digestive function. The coordinative action and integrity of the smooth muscle layers in the small intestine's wall are critical for mixing and propelling the luminal content. However, some patients present gastrointestinal limitations which may negatively impact the normal motility of the intestine. These patients have altered mechanical and muscle properties that likely impact chyme propulsion and may pose a daily scenario for long-term complications. To better understand how mechanics affect chyme propulsion, the propulsive capability of the small intestine was examined during a peristaltic wave along the distal direction of the tract. It was assumed that such a wave works as an activation signal, inducing peristaltic contractions in a transversely isotropic hyperelastic model. In this work, the effect on the propulsion mechanics, from an impairment on the muscle contractile ability, typical from patients with systemic sclerosis, and the presence of sores resultant from ulcers was evaluated. The passive properties of the constitutive model were obtained from uniaxial tensile tests from a porcine small intestine, along with both longitudinal and circumferential directions. Our experiments show decreased stiffness in the circumferential direction. Our simulations show decreased propulsion forces in patients in systemic sclerosis and ulcer patients. As these patients may likely need medical intervention, establishing action concerning the impaired propulsion can help to ease the evaluation and treatment of future complications.

On the management of maternal pushing during the second stage of labor: a biomechanical study considering passive tissue fatigue damage accumulation.

BACKGROUND: During the second stage of labor, the maternal pelvic floor muscles undergo repetitive stretch loading as uterine contractions and strenuous maternal pushes combined to expel the fetus, and it is not uncommon that these muscles sustain a partial or complete rupture. It has recently been demonstrated that soft tissues, including the anterior cruciate ligament and connective tissue in sheep pelvic floor muscle, can accumulate damage under repetitive physiological (submaximal) loads. It is well known to material scientists that this damage accumulation can not only decrease tissue resistance to stretch but also result in a partial or complete structural failure. Thus, we wondered whether certain maternal pushing patterns (in terms of frequency and duration of each push) could increase the risk of excessive damage accumulation in the pelvic floor tissue, thereby inadvertently contributing to the development of pelvic floor muscle injury. OBJECTIVE: This study aimed to determine which labor management practices (spontaneous vs directed pushing) are less prone to accumulate damage in the pelvic floor muscles during the second stage of labor and find the optimum approach in terms of minimizing the risk of pelvic floor muscle injury. STUDY DESIGN: We developed a biomechanical model for the expulsive phase of the second stage of labor that includes the ability to measure the damage accumulation because of repetitive physiological submaximal loads. We performed 4 simulations of the second stage of labor, reflecting a directed pushing technique and 3 alternatives for spontaneous pushing. RESULTS: The finite element model predicted that the origin of the pubovisceral muscle accumulates the most damage and so it is the most likely place for a tear to develop. This result was independent of the pushing pattern. Performing 3 maternal pushes per contraction, with each push lasting 5 seconds, caused less damage and seemed the best approach. The directed pushing technique (3 pushes per contraction, with each push lasting 10 seconds) did not reduce the duration of the second stage of labor and caused higher damage accumulation. CONCLUSION: The frequency and duration of the maternal pushes influenced the damage accumulation in the passive tissues of the pelvic floor muscles, indicating that it can influence the prevalence of pelvic floor muscle injuries. Our results suggested that the maternal pushes should not last longer than 5 seconds and that the duration of active pushing is a better measurement than the total duration of the second stage of labor. Hopefully, this research will help to shed new light on the best practices needed to improve the experience of labor for women.

Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network.

Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.

Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network.

Capsule endoscopy (CE) is an important tool in the management of patients with known or suspected inflammatory bowel disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant.Over the last decade, artificial intelligence (AI) has emerged as a means for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in medical image analysis, particularly capsule endoscopy. Therefore, we aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. A total of 1483 CE exams (PillCam SB3®) performed at a single center between 2015 and 2020 were analysed. From these exams, a total of 6130 frames of the enteric mucosa were obtained, 4233 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to Saurin's classification for bleeding potential: P1E-erosions with intermediate bleeding risk; P1U-ulcers with intermediate bleeding risk; P2U-ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into a CNN model with transfer learning. The pool of images was divided for constitution of training and validation datasets, comprising 80% and 20% of the total number of images, respectively. The output provided by the CNN was compared to the classification provided by a consensus of specialists. After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 95.6%, sensitivity of 90.8%, and a specificity of 97.1%. We believe that our study lays the foundation for the development and application of effective AI tools to CE. These techniques should improve diagnostic accuracy and reading efficiency. Schematic representation of the workflow and summary of the results.

Simulation of vaginal uterosacral ligament suspension damage, mimicking a mesh-augmented apical prolapse repair.

Synthetic implants were used for repair of anterior compartment prolapses, which can be caused by direct trauma resulting in damaged pelvic structures. The mechanical properties of these implants may cause complications, namely erosion of the mesh through the vagina. In this study, we evaluated, by modeling, the behavior of implants, during Valsalva maneuver, used to replace damaged uterosacral ligaments (USLs), mimicking a sacrocolpopexy repair. For this purpose, two synthetic implants (A®, for prolapse repair and B®, for Hernia repair) were uniaxially tested, and the mechanical properties obtained were incorporated in the computational models of the implants. The computational model for the implant was incorporated into the model of the female pelvic cavity, in order to mimic the USLs after its total rupture and with 90% and 50% impairment. The total rupture and impairments of the USLs, caused a variation of the supero-inferior displacement and displacement magnitude of the vagina, with higher values for the total rupture. With total rupture of the USLs, when compared to healthy USLs, supero-inferior displacement and displacement magnitude of the vagina increased by 4.98 mm (7.69 mm vs 12.67 mm) and 6.62 mm (9.38 mm vs 16.00 mm), respectively. After implantation (A® and B®) a reduction of the supero-inferior displacements of the anterior vaginal wall occurred, to values found in the case of the model without any impairment or rupture of the ligaments. The simulation was able to mimic the biomechanical response of the USLs, in response to different implants stiffnesses, which can be used in the development of novel meshes.

A finite element model to predict the consequences of endolymphatic hydrops in the basilar membrane.

Ménière's disease is an inner ear disorder, associated with episodes of vertigo, fluctuant hearing loss, tinnitus, and aural fullness. Ménière's disease is associated with endolymphatic hydrops. Clinical evidences show that this disease is often incapacitating, negatively affecting the patients' everyday life. The pathogenesis of Ménière's disease is still not fully understood and remains unclear. Previous numerical studies available in the literature related with endolymphatic hydrops, are very scarce. The present work applies the finite element method to investigate the consequences of endolymphatic hydrops in the normal hearing, associated with the Ménière's disease. The obtained results for the steady state dynamics analysis are in accordance with clinical evidences. The results show that the basilar membrane is not affected in the same intensity along its length and that the lower frequencies are more affected by the endolymphatic hydrops. From a clinical point of view, this work shows the relationship between the increasing of the endolymphatic pressure and the development of hearing loss.

On the hearing effects of a cholesteatoma growing: A biomechanical study.

Chronic otitis media enables the appearance of a benign middle ear tumor, known as a cholesteatoma, that may compromise hearing. To evaluate the influence of a cholesteatoma growth on the hearing function, a computational middle ear model based on the finite element method was used and three different size of cholesteatoma were modeled. The cholesteatoma solidification and the consequent degradation of the ossicles were also simulated as two condition that commonly occurs during cholesteatoma evolution. A sound pressure level of 80 dB SPL was applied in the tympanic membrane and a steady state analysis was performed for frequencies from 100 Hz to 10 kHz. The displacements of both the tympanic membrane and the stapes footplate were measured. The results were compared with a healthy case and it was shown that the cholesteatoma development leads to a decrease in the umbo and stapes displacements. The ossicles degradation simulation showed the higher difference comparing with the cholesteatoma in an initial stage, with lower displacements in the stapes footplate mainly for high frequencies. The observed displacement differences are directly connected to hearing loss, being possible to conclude that cholesteatoma evolution in the middle ear will lead to hearing problems, mainly in an advanced stage.

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study.

BACKGROUND AND AIMS: The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images. METHODS: We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame. CONCLUSIONS: The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.