High Pressure Balloon Dilatation of Primary Obstructive Megaureter in Children: A Multicenter Study

Aim of the Study: We described the initial experience of four referral centers in the treatment of primary obstructive megaureter (POM) in children, by high-pressure balloon dilatation (HPBD) of the ureterovesical junction with double JJ stenting. We managed a retrospective multicenter study to assess its effectiveness in long-term.Methods: We reviewed the medical records of all children who underwent HPBD for POM that require surgical treatment from May 2012 to December 2017 in four different institutions. The primary outcome measured was ureterohydronephrosis (UHN) and its degree of improvement after the procedure. Secondary outcomes were postoperative complications and resolution of preoperative symptomatology.Main Results: Forty-two ureters underwent HPBD for POM in 33 children, with a median age of 14.7 months – (range: 3 months −15 years). Ureterohydronephrosis improves in 86% of ureters after one endoscopic treatment. Three cases required a second HPBD. Four patients required surgical treatment for worsening of UHN after endoscopic treatment. The post-operative complication rate was 50% (21 ureters). In 13 cases (61%), they were related to double J stent. The median follow-up was 24 months (2 months −5 years) and all patients were symptom-free.Conclusion: We reported the first multicenter study and the largest series of children treated with HPBD, with an overall success rate of 92%. Endoscopic treatment can be a definitive treatment of POM since it avoided reimplantation in 90% of cases. Complications are mainly due to double J stent

Financial and relational impact of having a boy with posterior urethral valves

IntroductionChildhood chronic diseases affect family functioning and well-being. The aim of this study was to measure the impact of caring for a child with PUV, and the factors that most impact the burden of care.Patients and methodWe gave a questionnaire on the familial impact of having a child with posterior urethral valves to all parents of a child included in the CIRCUP trial from 2015 onwards. The questionnaire included questions about the parents' demographics, health, professional, financial and marital status and how these evolved since the child's birth as well as the “impact on family scale” (IOFS), which gives a total score ranging from 15 (no impact) to 60 (maximum impact). We then analyzed both the results of the specific demographic questions as well as the factors which influenced the IOFS score.ResultsWe retrieved answers for 38/51 families (74.5% response rate). The average IOFS score was 23.7 (15–51). We observed that the child's creatinine level had an effect on the IOFS score (p = 0.02), as did the parent's gender (p = 0.008), health status (p = 0.015), being limited in activity since the birth of the child (p = 0.020), being penalized in one's job (p = 0.009), being supported in one's job (p = 0.002), and decreased income (p = 0.004). Out of 38 mother/father binomials, 8/33 (24.2%) declared that they were no longer in the same relationship afterwards.ConclusionIn conclusion, having a boy with PUV significantly impacts families. The risk of parental separation and decrease in revenue is significant. Strategies aiming to decrease these factors should be put in place as soon as possible

Utilisation d'outils d'intelligence artificielle et d'ontologies pour la segmentation automatique d'images médicales : application au traitement du néphroblastome chez l'enfant

Wilms’ Tumor (or nephroblastoma) is the most common malignant tumor of kidney in children. With diagnostic and therapeutic advances in the last decades, the prognosis of this tumor has dramatically improved, reaching an overall survival rate of 90% at 5 years. Nevertheless, the therapeutic morbidity (related to surgery, chemotherapy and radiotherapy) remains high since 25% of the patients will develop sequelae during their lifetime. Surgery is a fundamental step of the therapeutic pathway of these patients. The three-dimensional reconstruction of tumoral kidney and his surrounding area, performed preoperatively on CT scans of patients, offers several advantages such as risk anticipation during surgical planning, selection of patients who can benefit from nephron-sparing surgery, accurate measurement of renal and tumoral volumes or better family counselling. However, 3D reconstructions consistently require a preliminary step of image segmentation (i.e delineation of various anatomical structures on images) which is tedious, very time-consuming and source of mistakes when manually performed. We present the use of artificial intelligence tools (particularly convolutional neural networks and case-based reasoning) to automate the segmentation of kidney and Wilms’ tumor in children. Compared to manual segmentations performed by experts, the results obtained with these artificial intelligence tools are promising, but requiring optimization and validation on a largest set of data. For the optimization of these results, we have developed an application ontology called WilmsOntol. This ontology provides hierarchized anatomical knowledge to intelligent tools in order to improve their segmentation performance.Le néphroblastome (ou tumeur de Wilms) est la tumeur rénale maligne la plus fréquemment rencontrée chez l’enfant. Les progrès réalisés dans les dernières décennies concernant sa prise en charge diagnostique et thérapeutique ont permis d’améliorer son pronostic avec à l’heure actuelle une survie à 5 ans dépassant 90% tous stades confondus. Cependant, la morbidité liée aux traitements (chirurgie, chimiothérapie et radiothérapie) reste élevée puisque 25% des patients présenteront des séquelles au cours de leur vie. La chirurgie garde une place centrale dans la stratégie thérapeutique de cette tumeur. La reconstruction en 3 dimensions du rein tumoral et de son environnement, réalisée en pré-opératoire à partir des images scanner du patient, présente des avantages notamment pour la planification opératoire, la sélection des patients pouvant bénéficier d’une chirurgie conservatrice, la précision du calcul des volumes ou l’information des familles. Cependant, ces reconstructions 3D nécessitent une phase préalable de segmentation qui est chronophage, fastidieuse et source d’erreurs lorsqu’elle est réalisée manuellement. Nous présentons dans ce travail l’utilisation d’outils d’intelligence artificielle (réseaux de neurones et raisonnement à partir de cas) pour automatiser le processus de segmentation du rein tumoral. Les résultats obtenus avec les outils développés sont encourageants, nécessitant cependant d’être optimisés et validés sur un plus grand nombre de cas. C’est dans cette optique d’optimisation des résultats que nous avons créé une ontologie d’application, nommée WilmsOntol, dont l’objectif principal est d’apporter des connaissances anatomiques aux outils d’intelligence artificielle afin d’améliorer leurs performances de segmentation

Utilisation d'outils d'intelligence artificielle et d'ontologies pour la segmentation automatique d'images médicales : application au traitement du néphroblastome chez l'enfant

Wilms’ Tumor (or nephroblastoma) is the most common malignant tumor of kidney in children. With diagnostic and therapeutic advances in the last decades, the prognosis of this tumor has dramatically improved, reaching an overall survival rate of 90% at 5 years. Nevertheless, the therapeutic morbidity (related to surgery, chemotherapy and radiotherapy) remains high since 25% of the patients will develop sequelae during their lifetime. Surgery is a fundamental step of the therapeutic pathway of these patients. The three-dimensional reconstruction of tumoral kidney and his surrounding area, performed preoperatively on CT scans of patients, offers several advantages such as risk anticipation during surgical planning, selection of patients who can benefit from nephron-sparing surgery, accurate measurement of renal and tumoral volumes or better family counselling. However, 3D reconstructions consistently require a preliminary step of image segmentation (i.e delineation of various anatomical structures on images) which is tedious, very time-consuming and source of mistakes when manually performed. We present the use of artificial intelligence tools (particularly convolutional neural networks and case-based reasoning) to automate the segmentation of kidney and Wilms’ tumor in children. Compared to manual segmentations performed by experts, the results obtained with these artificial intelligence tools are promising, but requiring optimization and validation on a largest set of data. For the optimization of these results, we have developed an application ontology called WilmsOntol. This ontology provides hierarchized anatomical knowledge to intelligent tools in order to improve their segmentation performance.Le néphroblastome (ou tumeur de Wilms) est la tumeur rénale maligne la plus fréquemment rencontrée chez l’enfant. Les progrès réalisés dans les dernières décennies concernant sa prise en charge diagnostique et thérapeutique ont permis d’améliorer son pronostic avec à l’heure actuelle une survie à 5 ans dépassant 90% tous stades confondus. Cependant, la morbidité liée aux traitements (chirurgie, chimiothérapie et radiothérapie) reste élevée puisque 25% des patients présenteront des séquelles au cours de leur vie. La chirurgie garde une place centrale dans la stratégie thérapeutique de cette tumeur. La reconstruction en 3 dimensions du rein tumoral et de son environnement, réalisée en pré-opératoire à partir des images scanner du patient, présente des avantages notamment pour la planification opératoire, la sélection des patients pouvant bénéficier d’une chirurgie conservatrice, la précision du calcul des volumes ou l’information des familles. Cependant, ces reconstructions 3D nécessitent une phase préalable de segmentation qui est chronophage, fastidieuse et source d’erreurs lorsqu’elle est réalisée manuellement. Nous présentons dans ce travail l’utilisation d’outils d’intelligence artificielle (réseaux de neurones et raisonnement à partir de cas) pour automatiser le processus de segmentation du rein tumoral. Les résultats obtenus avec les outils développés sont encourageants, nécessitant cependant d’être optimisés et validés sur un plus grand nombre de cas. C’est dans cette optique d’optimisation des résultats que nous avons créé une ontologie d’application, nommée WilmsOntol, dont l’objectif principal est d’apporter des connaissances anatomiques aux outils d’intelligence artificielle afin d’améliorer leurs performances de segmentation

Use of artificial intelligence tools and ontologies to automatize the segmentation of medical images in children with nephroblastoma

Le néphroblastome (ou tumeur de Wilms) est la tumeur rénale maligne la plus fréquemment rencontrée chez l’enfant. Les progrès réalisés dans les dernières décennies concernant sa prise en charge diagnostique et thérapeutique ont permis d’améliorer son pronostic avec à l’heure actuelle une survie à 5 ans dépassant 90% tous stades confondus. Cependant, la morbidité liée aux traitements (chirurgie, chimiothérapie et radiothérapie) reste élevée puisque 25% des patients présenteront des séquelles au cours de leur vie. La chirurgie garde une place centrale dans la stratégie thérapeutique de cette tumeur. La reconstruction en 3 dimensions du rein tumoral et de son environnement, réalisée en pré-opératoire à partir des images scanner du patient, présente des avantages notamment pour la planification opératoire, la sélection des patients pouvant bénéficier d’une chirurgie conservatrice, la précision du calcul des volumes ou l’information des familles. Cependant, ces reconstructions 3D nécessitent une phase préalable de segmentation qui est chronophage, fastidieuse et source d’erreurs lorsqu’elle est réalisée manuellement. Nous présentons dans ce travail l’utilisation d’outils d’intelligence artificielle (réseaux de neurones et raisonnement à partir de cas) pour automatiser le processus de segmentation du rein tumoral. Les résultats obtenus avec les outils développés sont encourageants, nécessitant cependant d’être optimisés et validés sur un plus grand nombre de cas. C’est dans cette optique d’optimisation des résultats que nous avons créé une ontologie d’application, nommée WilmsOntol, dont l’objectif principal est d’apporter des connaissances anatomiques aux outils d’intelligence artificielle afin d’améliorer leurs performances de segmentation.Wilms’ Tumor (or nephroblastoma) is the most common malignant tumor of kidney in children. With diagnostic and therapeutic advances in the last decades, the prognosis of this tumor has dramatically improved, reaching an overall survival rate of 90% at 5 years. Nevertheless, the therapeutic morbidity (related to surgery, chemotherapy and radiotherapy) remains high since 25% of the patients will develop sequelae during their lifetime. Surgery is a fundamental step of the therapeutic pathway of these patients. The three-dimensional reconstruction of tumoral kidney and his surrounding area, performed preoperatively on CT scans of patients, offers several advantages such as risk anticipation during surgical planning, selection of patients who can benefit from nephron-sparing surgery, accurate measurement of renal and tumoral volumes or better family counselling. However, 3D reconstructions consistently require a preliminary step of image segmentation (i.e delineation of various anatomical structures on images) which is tedious, very time-consuming and source of mistakes when manually performed. We present the use of artificial intelligence tools (particularly convolutional neural networks and case-based reasoning) to automate the segmentation of kidney and Wilms’ tumor in children. Compared to manual segmentations performed by experts, the results obtained with these artificial intelligence tools are promising, but requiring optimization and validation on a largest set of data. For the optimization of these results, we have developed an application ontology called WilmsOntol. This ontology provides hierarchized anatomical knowledge to intelligent tools in order to improve their segmentation performance

Le robot chirurgical dans la chirurgie de l'enfant (applications, contraintes et bénéfices.)

BESANCON-BU Médecine pharmacie (250562102) / SudocSudocFranceF

Congenital Urethral Fistula: A Case Report and Literature Review

Male congenital urethral fistula is an extremely rare condition. It is characterized by an abnormal opening of the ventral aspect of the penis. We report the case of a 1-month-old boy with congenital urethral fistula. We will describe the surgical technique, postoperative results, and literature review

Fusion of multiple segmentations of medical images using OV2ASSION and Deep Learning methods: Application to CT-Scans for tumoral kidney

International audienceNephroblastoma is the most common kidney tumour in children. Its diagnosis is based on imagery. In the SAIAD project, we have designed a platform for optimizing the segmentation of deformed kidney and tumour with a small dataset, using Artificial Intelligence methods. These patient's structures segmented by separate tools and processes must then be fused to obtain a unique numerical 3D representation. However, when aggregating these structures into a final segmentation, conflicting pixels may appear. These conflicts can be solved by IA techniques. This paper presents a synthesis of our segmentation contribution in the SAIAD project and a new fusion method. The segmentation method uses the FCN-8s network with the OV2ASSION training method, which allows segmentation by patient and overcomes the limited dataset. This new fusion method combines the segmentations of the previously performed structures, using a simple and efficient network combined with the OV2ASSION training method as well, in order to manage eventual conflicting pixels. These segmentation and fusion methods were evaluated on pathological kidney and tumour structures of 14 patients affected by nephroblastoma, included in the final dataset of the SAIAD project. They are compared with other methods adapted from the literature. The results demonstrate the effectiveness of our training method coupled with the FCN-8s network in the segmentation process with more patients, and in the case of the fusion process, its effectiveness coupled with a common network, in resolving the conflicting pixels and its ability to improve the resulting segmentations

Use of Cellulose Nanofibers as an Electrode Binder for Lithium Ion Battery Screen Printing on a Paper Separator

Water-based inks were formulated using cellulose nanofibers as a binder in order to directly front/reverse print lithium ion cells on a paper separator. Moreover, the high cohesion of electrodes as provided by cellulose nanofibers allowed for the embedding metallic current collectors in the electrodes during the printing stage, in order to develop a one-step printing and assembling process. Positive and negative inks based on LiFePO4, or graphite, respectively, and cellulose nanofibers, displayed rheological properties complying with a variety of printing processes, as well as with screen printing. Printed cells exhibited high electrical conductivity and adhesion between current collectors and inks, i.e., up to 64 ± 1 J/m2. Electrochemical cycling tests at C/10 showed a reversible capacity during the first cycle of about 80 mAh/g, which slightly decayed upon cycling. Preliminary results and assembling strategies can be considered as promising, and they represent a quick solution for the manufacturing of lithium ion batteries. Work is in progress to improve these processing issues and the cycling performances of Li-ion cells

Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network

International audienceMost often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897