Background:Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children.Materials and Methods:Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables.Results:A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis.Discussion:This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool.Conclusion:ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

The moisture content of the human skin, but also the loss of water through the skin, the transepidermal water loss (TEWL), plays a significant role in the skin’s health. Various medical indications require the use of a wound dressing. However, how the skin environment changes under a wound dressing has not yet been sufficiently investigated. Skin moisture and TEWL values were measured in 20 healthy volunteers before and after the application of a total of 23 different wound dressings distributed over the back. Significant changes in the parameters from day 1 to day 2 were tested. Wound dressings change the underlying skin environment. Occlusive dressings significantly increase skin hydration and TEWL. The findings could contribute to quantitative analysis and monitoring of topical-wound therapy in the future.

Background: Simple appendicitis may be self-limiting or require antibiotic treatment or appendectomy. The aim of this study was to assess the feasibility and safety of a nonoperative, antibiotic-free approach for suspected simple appendicitis in children. Methods: This single-center, retrospective study included patients (0–17 years old) who were hospitalized at the pediatric surgery department due to suspected appendicitis between 2011 and 2012. Data from patients who primarily underwent appendectomy were used as controls. The follow-up of nonoperatively managed patients was conducted in 2014. The main outcome of interest was appendicitis recurrence. Results: A total of 365 patients were included: 226 were treated conservatively and 139 underwent appendectomy. Fourteen (6.2% of 226) of the primarily nonoperatively treated patients required secondary appendectomy during follow-up, and histology confirmed simple, uncomplicated appendicitis in 10 (4.4% of 226) patients. Among a subset of 53 patients managed nonoperatively with available Alvarado and/or Pediatric Appendicitis Scores and sonographic appendix diameters in clinical reports, 29 met the criteria for a high probability of appendicitis. Three of these patients (10.3% of 29) underwent secondary appendectomy. No complications were reported during follow-up. Conclusions: A conservative, antibiotic-free approach may be considered for pediatric patients with suspected uncomplicated appendicitis in a hospital setting. Only between 6 and 10% of these patients required secondary appendectomy. Nevertheless, the cohort of patients treated nonoperatively was likely to have also included individuals with further abdominal conditions other than appendicitis. Active observation and clinical support during the disease course may help patients avoid unnecessary procedures and contribute to spontaneous resolution of appendicitis or other pediatric conditions as the cause of abdominal pain. However, further studies are needed to define validated diagnostic and management criteria.

Several surgical techniques are available for an adult patient collective with diastasis recti. Only few research papers addresses the treatment options of diastasis of the rectus abdominis in children. In this case series, we present a new technique of epifascial repair as a novel possibility in successfully repairing defects of the anterior abdominal wall using minimally invasive surgery. In this case series, we present an epifascial repair technique for patients with a diastasis recti with a dehiscence cranial of the umbilicus. Four pediatric patients with symptomatic rectus diastasis were treated with this new surgical technique. All procedures were conducted successfully, and no recurrence was observed in the follow up. All patients showed clinical regression of the rectus diastasis without any postoperative abdominal wall protrusion and good improvement of the symptoms. Intraoperative intraperitoneal air loss, postoperative scar keloid, thermal lesions due to Ultracision and one seroma/hematoma after the removal of the drain were minor complications observed during the follow-up.

Background: Appendicitis is a common condition among children and adolescents. Machine learning models can offer much-needed tools for improved diagnosis, severity assessment and management guidance for pediatric appendicitis. However, to be adopted in practice, such systems must be reliable, safe and robust across various medical contexts, e.g., hospitals with distinct clinical practices and patient populations. Methods: We performed external validation of models predicting the diagnosis, management and severity of pediatric appendicitis. Trained on a cohort of 430 patients admitted to the Children’s Hospital St. Hedwig (Regensburg, Germany), the models were validated on an independent cohort of 301 patients from the Florence-Nightingale-Hospital (Düsseldorf, Germany). The data included demographic, clinical, scoring, laboratory and ultrasound parameters. In addition, we explored the benefits of model retraining and inspected variable importance. Results: The distributions of most parameters differed between the datasets. Consequently, we saw a decrease in predictive performance for diagnosis, management and severity across most metrics. After retraining with a portion of external data, we observed gains in performance, which, nonetheless, remained lower than in the original study. Notably, the most important variables were consistent across the datasets. Conclusions: While the performance of transferred models was satisfactory, it remained lower than on the original data. This study demonstrates challenges in transferring models between hospitals, especially when clinical practice and demographics differ or in the presence of externalities such as pandemics. We also highlight the limitations of retraining as a potential remedy since it could not restore predictive performance to the initial level.