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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.

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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.

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BACKGROUND AND AIMS: Capsule endoscopy is a central element in the management of patients with suspected or known Crohn's disease. In 2017, PillCam™ Crohn's Capsule was introduced and demonstrated to have greater accuracy in the evaluation of extension of disease in these patients. Artificial intelligence [AI] is expected to enhance the diagnostic accuracy of capsule endoscopy. This study aimed to develop an AI algorithm for the automatic detection of ulcers and erosions of the small intestine and colon in PillCam™ Crohn's Capsule images. METHODS: A total of 8085 PillCam™ Crohn's Capsule images were extracted between 2017 and 2020, comprising 2855 images of ulcers and 1975 erosions; the remaining images showed normal enteric and colonic mucosa. This pool of images was subsequently split into training and validation datasets. The performance of the network was subsequently assessed in an independent test set. RESULTS: The model had an overall sensitivity and specificity of 90.0% and 96.0%, respectively. The precision and accuracy of this model were 97.1% and 92.4%, respectively. In particular, the algorithm detected ulcers with a sensitivity of 83% and specificity of 98%, and erosions with sensitivity and specificity of 91% and 93%, respectively. CONCLUSION: A deep learning model capable of automatically detecting ulcers and erosions in PillCam™ Crohn's Capsule images was developed for the first time. These findings pave the way for the development of automatic systems for detection of clinically significant lesions, optimizing the diagnostic performance and efficiency of monitoring Crohn's disease activity.

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We performed numerical simulation of voluntary contraction of the pelvic floor muscles to evaluate the resulting displacements of the organs and muscles. Structures were segmented in Magnetic Resonance (MR) images. Different material properties and constitutive models were attributed. The Finite Element Method was applied, and displacements were compared with dynamic MRI findings. Numerical simulation showed muscle magnitude displacement ranging from 0 to 7.9 mm, more evident in the posterior area. Accordingly, the anorectum moved more than the uterus and bladder. Dynamic MRI showed less 0.2 mm and 4.1 mm muscle dislocation in the anterior and cranial directions, respectively. Applications of this model include evaluating muscle impairment, subject-specific mesh implant planning, or effectiveness of rehabilitation.

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