Abstract

Abstract
Background: There is an urgent need to develop better materials to provide anatomical support to the pelvic floor without compromising its function. Objective: Our aim was to assess outcomes after simulated vaginal prolapse repair in a sheep model using three different materials: (1) ultra-lightweight polypropylene (PP) non-degradable textile (Restorelle) mesh, (2) electrospun biodegradable ureidopyrimidinone-polycarbonate (UPy-PC), and (3) electrospun non-degradable polyurethane (PU) mesh in comparison with simulated native tissue repair (NTR). These implants may reduce implant-related complications and avoid vaginal function loss. Design, setting, and participants: A controlled trial was performed involving 48 ewes that underwent NTR or mesh repair with PP, UPy-PC, or PU meshes (n = 12/group). Explants were examined 60 and 180 d (six per group) post-implantation. Intervention: Posterior rectovaginal dissection, NTR, or mesh repair. Outcome measurements and statistical analysis: Implant-related complications, vaginal contractility, compliance, and host response were assessed. Power calculation and analysis of variance testing were used to enable comparison between the four groups. Results: There were no visible implant-related complications. None of the implants compromised vaginal wall contractility, and passive biomechanical properties were similar to those after NTR. Shrinkage over the surgery area was around 35% for NTR and all mesh-augmented repairs. All materials were integrated well with similar connective tissue composition, vascularization, and innervation. The inflammatory response was mild with electrospun implants, inducing both more macrophages yet with relatively more type 2 macrophages present at an early stage than the PP mesh. Conclusions: Three very different materials were all well tolerated in the sheep vagina. Biomechanical findings were similar for all mesh-augmented repair and NTR. Constructs induced slightly different mid-term inflammatory profiles. Patient summary: Product innovation is needed to reduce implant-related complications. We tested two novel implants, electrospun and an ultra-lightweight polypropylene textile mesh, in a physiologically relevant model for vaginal surgery. All gave encouraging outcomes. (C) 2018 European Association of Urology. Published by Elsevier B.V.

Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
Background and Objective: Glaucoma, an eye condition that leads to permanent blindness, is typically asymptomatic and therefore difficult to be diagnosed in time. However, if diagnosed in time, Glaucoma can effectively be slowed down by using adequate treatment; hence, an early diagnosis is of utmost importance. Nonetheless, the conventional approaches to diagnose Glaucoma adopt expensive and bulky equipment that requires qualified experts, making it difficult, costly and time-consuming to diagnose large amounts of people. Consequently, new alternatives to diagnose Glaucoma that suppress these issues should be explored. Methods: This work proposes an interpretable computer-aided diagnosis (CAD) pipeline that is capable of diagnosing Glaucoma using fundus images and run offline in mobile devices. Several public datasets of fundus images were merged and used to build Convolutional Neural Networks (CNNs) that perform segmentation and classification tasks. These networks are then used to build a pipeline for Glaucoma assessment that outputs a Glaucoma confidence level and also provides several morphological features and segmentations of relevant structures, resulting in an interpretable Glaucoma diagnosis. To assess the performance of this method in a restricted environment, this pipeline was integrated into a mobile application and time and space complexities were assessed. Results: Considering the test set, the developed pipeline achieved 0.91 and 0.75 of Intersection over Union (IoU) in the optic disc and optic cup segmentation, respectively. With regards to the classification, an accuracy of 0.87 with a sensitivity of 0.85 and an AUC of 0.93 were attained. Moreover, this pipeline runs on an average Android smartphone in under two seconds. Conclusions: The results demonstrate the potential that this method can have in the contribution to an early Glaucoma diagnosis. The proposed approach achieved similar or slightly better metrics than the current CAD systems for Glaucoma assessment while running on more restricted devices. This pipeline can, therefore, be used to construct accurate and affordable CAD systems that could enable large Glaucoma screenings, contributing to an earlier diagnose of this condition. © 2020

Abstract