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Background We sought to use deep learning to extract anatomic features from postnatal kidney ultrasounds and evaluate their performance in predicting the risk and timing of chronic kidney disease (CKD) progression for boys with posterior urethral valves (PUV). We hypothesized that these features would predict CKD progression better than clinical characteristics such as nadir creatinine alone. Methods We performed a retrospective cohort study of boys with PUV treated at two pediatric health systems from 1990 to 2021. Features of kidneys were extracted from initial postnatal kidney ultrasound images using a deep learning model. Three time-to-event prediction models were built using random survival forests. The Imaging Model included deep learning imaging features, the Clinical Model included clinical data, and the Ensemble Model combined imaging features and clinical data. Separate models were built to include time-dependent clinical data that were available at 6 months, 1 year, 3 years, and 5 years. Results Two-hundred and twenty-five patients were included in the analysis. All models performed well with C-indices of 0.7 or greater. The Clinical Model outperformed the Imaging Model at all time points with nadir creatinine driving the performance of the Clinical Model. Combining the 6-month Imaging Model (C-index 0.7; 95% confidence interval [CI] 0.6, 0.79) with the 6-month Clinical Model (C-index 0.79; 95% CI 0.71, 0.86) resulted in a 6-month Ensemble Model that performed better (C-index 0.82; 95% CI 0.77, 0.88) than either model alone. Conclusions Deep learning imaging features extracted from initial postnatal kidney ultrasounds may improve early prediction of CKD progression among children with PUV. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information, Author(s): John K. Weaver [sup.1] [sup.2] , Karen Milford [sup.3] , Mandy Rickard [sup.3] , Joey Logan [sup.1] [sup.4] [sup.5] , Lauren Erdman [sup.6] [sup.7] , Bernarda Viteri [sup.8] , [...]

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area. [ABSTRACT FROM AUTHOR]

Objective: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmentation algorithm to detect one of the most common fetal anomalies: a thickened nuchal translucency (NT), which is a marker for genetic and structural anomalies. Methods: Standardized mid‐sagittal ultrasound images of the fetal head and chest were collected for 560 fetuses between 11 and 13 weeks and 6 days of gestation, 88 (15.7%) of whom had an NT thicker than 3.5 mm. Image quality was graded as high or low by two fetal medicine experts. Images were divided into a training‐set (n = 451, 55 thick NT) and a test‐set (n = 109, 33 thick NT). We then trained a U‐Net convolutional neural network to segment the fetus and the NT region and computed the NT:fetus ratio of these regions. The ability of this ratio to separate thick (anomalous) NT regions from healthy, typical NT regions was first evaluated in ground‐truth segmentation to validate the metric and then with predicted segmentation to validate our algorithm, both using the area under the receiver operator curve (AUROC). Results: The ground‐truth NT:fetus ratio detected thick NTs with 0.97 AUROC in both the training and test sets. The fetus and NT regions were detected with a Dice score of 0.94 in the test set. The NT:fetus ratio based on model segmentation detected thick NTs with an AUROC of 0.96 relative to clinician labels. At a 91% specificity, 94% of thick NT cases were detected (sensitivity) in the test set. The detection rate was statistically higher (p = 0.003) in high versus low‐quality images (AUROC 0.98 vs. 0.90, respectively). Conclusion: Our model provides an explainable deep‐learning method for detecting increased NT. This technique can be used to screen for other fetal anomalies in the first trimester of pregnancy. Key points: What's already known about this topic? Increased nuchal translucency (NT) is a first trimester marker for genetic and structural anomalies.Currently, identification of an increased NT relies on a sonographer or radiologist measuring the NT on a standardized midsagittal ultrasound picture of the fetal head and neck. What does this study add? The ratio of the NT area to the fetal body area on a standardized first trimester ultrasound image is a highly correlated marker of a thick NT, enabling detection of a thick NT in ultrasound images without dicom meta data indicating image scale.The NT:fetus ratio can be assessed using artificial intelligence by segmenting the NT and fetus regions and computing the ratio of the two regions. This opens the door to AI‐assisted identification of fetal anomalies. [ABSTRACT FROM AUTHOR]

Introduction Neonates with lower urinary tract obstruction (LUTO) experience high morbidity and mortality associated with the development of chronic kidney disease. The prenatal detection rate for LUTO is less than 50%, with late or missed diagnosis leading to delayed management and long-term sequelae in the remainder. We aimed to explore the trends in prenatal detection and management at a high-risk fetal center and determine if similar trends of postnatal presentations were noted for the same period. Methods Prenatal and postnatal LUTO databases from a tertiary fetal center and its associated pediatric center between 2009 and 2021 were reviewed, capturing maternal age, gestational age (GA) at diagnosis, and rates of termination of pregnancy (TOP). Time series analysis using autocorrelation was performed to investigate time trend changes for prenatally suspected and postnatally confirmed LUTO cases. Results A total of 161 fetuses with prenatally suspected LUTO were identified, including 78 terminations. No significant time trend was found when evaluating the correlation between time periods, prenatal suspicion, and postnatal confirmation of LUTO cases (Durbin–Watson [DW] = 1.99, p = 0.3641 and DW = 2.86, p = 0.9113, respectively). GA at referral was 20.0 weeks (interquartile range [IQR] 12, 35) and 22.0 weeks (IQR 13, 37) for TOP and continued pregnancies (p < 0.0001). GA at initial ultrasound was earlier in terminated fetuses compared to continued (20.0 [IQR 12, 35] weeks vs. 22.5 [IQR 13, 39] weeks, p < 0.0001). While prenatal LUTO suspicion remained consistently higher than postnatal presentations, the rates of postnatal presentations and terminations remained stable during the study years (p = 0.7913 and 0.2338), as were GA at TOP and maternal age at diagnosis (p = 0.1710 and 0.1921). Conclusion This study demonstrated that more severe cases of LUTO are referred earlier and are more likely to undergo TOP. No significant trend was detected between time and prenatally suspected or postnatally confirmed LUTO, highlighting the need for further studies to better delineate factors that can increase prenatal detection. [ABSTRACT FROM AUTHOR]

Objective: To sensitively predict the risk of renal obstruction on diuretic renography using routine reported ultrasonography (US) findings, coupled with machine learning approaches, and determine safe criteria for deferral of diuretic renography. Patients and Methods: Patients from two institutions with isolated hydronephrosis who underwent a diuretic renogram within 3 months following renal US were included. Age, sex, and routinely reported US findings (laterality, kidney length, anteroposterior diameter, Society for Fetal Urology [SFU] grade) were abstracted. The drainage half‐times were collected from renography and stratified as low risk (<20 min, primary outcome), intermediate risk (20–60 min), and high risk of obstruction (>60 min). A random Forest model was trained to classify obstruction risk, here named the 'Artificial intelligence Evaluation of Renogram Obstruction' (AERO). Model performance was determined by measuring area under the receiver‐operating‐characteristic curve (AUROC) and decision curve analysis. Results: A total of 304 patients met the inclusion criteria, with a median (interquartile range) age of diuretic renogram at 4 (2–7) months. Of all patients, 48 (16%) were low risk, 102 (33%) were intermediate risk, 156 (51%) were high risk of obstruction based on diuretic renogram. The AERO achieved a binary AUROC of 0.84, multi‐class AUROC of 0.74 that was superior to the SFU grade, and external validation (n = 64) binary AUROC of 0.76. The most important features for prediction included age, anteroposterior diameter, and SFU grade. We deployed our application in an easy‐to‐use application (https://sickkidsurology.shinyapps.io/AERO/). At a threshold probability of 30%, the AERO would allow 66 more patients per 1000 to safely avoid a renogram without missing significant obstruction compared to a strategy in which a renogram is routinely performed for SFU Grade ≥3. Conclusions: Coupled with machine learning, routine US findings can improve the criteria to determine in which children with isolated hydronephrosis a diuretic renogram can be safely avoided. Further optimisation and validation are required prior to implementation into clinical practice. [ABSTRACT FROM AUTHOR]

Objective To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. Methods SLE patients ages 18–65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were collected on demographic and clinical variables, disease burden/activity, health-related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal–Wallis and χ2 tests. Results Of the 238 patients, 90% were female, with a mean age of 41 years (s. d. 12) and a disease duration of 14 years (s. d. 10) at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (P   <  0.03) compared with subtype B. Subtype A also had greater levels of subjective cognitive function (P  < 0.001), disease burden/damage (P  < 0.04), HRQoL (P  < 0.001) and psychiatric measures (P  < 0.001) compared with subtype B. Conclusion This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multifactorial phenotypes exist. Those with greater disease burden, from SLE-specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately this will aid our understanding of personalized CI trajectories and identification of appropriate treatments. [ABSTRACT FROM AUTHOR]

Objectives The Reach Out and Read program (ROaR) is associated with increased parent-child book reading and improved language development in children. Though children of adolescent parents may have an elevated risk of language delay, ROaR has never been specifically studied among adolescent-headed families. This pilot evaluated the feasibility and effectiveness of ROaR among adolescent mothers and their children. Methods This randomized controlled pilot followed thirty adolescent mothers with children aged 6-20 months in a teen-tot clinic in downtown Toronto. At each of three consecutive well child checkups, intervention families received a new children's book, reading-related anticipatory guidance customized to the mother's developmental stage, counselling from a librarian, and a public library card. Control families received routine care. At baseline and study completion, all mothers completed a survey on family reading patterns and the Beck Depression Inventory-Revised (BDI-IA). Results Though regression models were not statistically significant, bivariate analyses at study completion revealed that intervention mothers were significantly more likely than controls to report reading as one of the child's favorite activities (29 vs 0 %) and had significantly lower maternal depression scores (7.0 vs 12.5; [greater than or equal to]10 = clinically significant depression). Trends for all other variables, including time spent reading together and maternal enjoyment of reading, were also in the direction of benefit. This program was implemented at minimal cost and adopted permanently following study completion. Conclusions This feasible and developmentally appropriate intervention shows promise in promoting shared book reading and reducing maternal depression within adolescent-headed families, warranting investigation with larger trials., Author(s): Maya M. Kumar[sup.1] [sup.4] , Henry R. Cowan[sup.2] , Lauren Erdman[sup.3] , Miriam Kaufman[sup.1] , Katherine M. Hick[sup.1] Author Affiliations: (1) Division of Adolescent Medicine, Department of Paediatrics, The [...]

Background: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. Objective: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. Methods: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. Results: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. Conclusion: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children. [ABSTRACT FROM AUTHOR]

Purpose: Vesicoureteral reflux grading from voiding cystourethrograms is highly subjective with low reliability. We aimed to demonstrate improved reliability for vesicoureteral reflux grading with simple and machine learning approaches using ureteral tortuosity and dilatation on voiding cystourethrograms. Materials and Methods: Voiding cystourethrograms were collected from our institution for training and 5 external data sets for validation. Each voiding cystourethrogram was graded by 5-7 raters to determine a consensus vesicoureteral reflux grade label and inter- and intra-rater reliability was assessed. Each voiding cystourethrogram was assessed for 4 features: ureteral tortuosity, proximal, distal, and maximum ureteral dilatation. The labels were then assigned to the combination of the 4 features. A machine learning-based model, qVUR, was trained to predict vesicoureteral reflux grade from these features and model performance was assessed by AUROC (area under the receiver-operator-characteristic). Results: A total of 1,492 kidneys and ureters were collected from voiding cystourethrograms resulting in a total of 8,230 independent gradings. The internal inter-rater reliability for vesicoureteral reflux grading was 0.44 with a median percent agreement of 0.71 and low intra-rater reliability. Higher values for each feature were associated with higher vesicoureteral reflux grade. qVUR performed with an accuracy of 0.62 (AUROC[0.84) with stable performance across all external data sets. The model improved vesicoureteral reflux grade reliability by 3.6-fold compared to traditional grading (P < .001). Conclusions: In a large pediatric population from multiple institutions, we show that machine learning-based assessment for vesicoureteral reflux improves reliability compared to current grading methods. qVUR is generalizable and robust with similar accuracy to clinicians but the added prognostic value of quantitative measures warrants further study. [ABSTRACT FROM AUTHOR]

Objective: To compare the outcomes of pre‐ vs postnatally diagnosed posterior urethral valves (PUV) at two large paediatric centres in North America to ascertain if the prenatal diagnosis of PUV is associated with better outcomes. Patients and Methods: All boys with PUV were identified at two large paediatric institutions in North America between 2000 and 2020 (The Hospital for Sick Children [SickKids, SK] and Children's Hospital of Philadelphia [CHOP]). Baseline characteristics and outcome measures were compared between those diagnosed pre‐ vs postnatally. Main outcomes of interest included progression of chronic kidney disease (CKD), the need for renal replacement therapy (RRT), and bladder function compromise, as determined by need for clean intermittent catheterisation (CIC). Time‐to‐event analyses were completed when possible. Results: During the study period, 152 boys with PUV were treated at the SK (39% prenatal) and 216 were treated at the CHOP (71% prenatal). At the SK, there was no difference between the pre‐ and postnatal groups in the proportion of boys who required RRT, progressed to CKD Stage ≥3, or who were managed with CIC when comparing the timing of diagnosis. The time to event for RRT and CIC was significantly younger for prenatally detected PUV. At the CHOP, significantly more prenatal boys required RRT; however, there was no significant difference in the age this outcome was reached. The proportion of boys managed with CIC was not different but the time to event was significantly earlier in the prenatal group. Conclusion: This study represents the largest multi‐institutional series of boys with PUV and failed to identify any difference in the outcomes of pre‐ vs postnatal detection of PUV. A multidisciplinary approach with standardisation of the treatment pathways will help in understanding the true impact of prenatal/early detection on outcomes of PUV. [ABSTRACT FROM AUTHOR]

Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications. [ABSTRACT FROM AUTHOR]

Background: Lower urinary tract obstruction (LUTO) is a rare but critical fetal diagnosis. Different ultrasound markers have been reported with varying sensitivity and specificity.Aims: The objective of this systematic review and meta-analysis was to identify the diagnostic accuracy of ultrasound markers for LUTO.Materials and Methods: We performed a systematic literature review of studies reporting on fetuses with hydronephrosis or a prenatally suspected and/or postnatally confirmed diagnosis of LUTO. Bayesian bivariate random effects meta-analytic models were fitted, and we calculated posterior means and 95% credible intervals for the pooled diagnostic odds ratio (DOR).Results: A total of 36,189 studies were identified; 636 studies were available for full text review and a total of 42 studies were included in the Bayesian meta-analysis. Among the ultrasound signs assessed, megacystis (DOR 49.15, [15.28, 177.44]), bilateral hydroureteronephrosis (DOR 41.33, [13.36,164.83]), bladder thickening (DOR 13.73, [1.23, 115.20]), bilateral hydronephrosis (DOR 8.36 [3.17, 21.91]), male sex (DOR 8.08 [3.05, 22.82]), oligo- or anhydramnios (DOR 7.75 [4.23, 14.46]), and urinoma (DOR 7.47 [1.14, 33.18]) were found to be predictive of LUTO (Table 1). The predictive sensitivities and specificities however are low and wide study heterogeneity existed.Discussion: Classically, LUTO is suspected in the presence of prenatally detected megacystis with a dilated posterior urethra (i.e., the keyhole sign), and bilateral hydroureteronephrosis. However, keyhole sign has been found to have modest diagnostic performance in predicting the presence of LUTO in the literature which we confirmed in our analysis. The surprisingly low specificity may be influenced by several factors, including the degree of obstruction, and the diligence of the sonographer at searching for and documenting it during the scan. As a result, providers should consider this when establishing the differential for a fetus with hydronephrosis as the presence or absence of keyhole sign does not reliably rule in or rule out LUTO.Conclusions: Megacystis, bilateral hydroureteronephrosis and bladder wall thickening are the most accurate predictors of LUTO. Given the significant consequences of a missed LUTO diagnosis, clinicians providing counselling for prenatal hydronephrosis should maintain a low threshold for considering LUTO as part of the differential diagnosis. [ABSTRACT FROM AUTHOR]

Background: Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection.Methods: We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features.Results: Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%-89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures.Conclusion: Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns. [ABSTRACT FROM AUTHOR]