The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone.

Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical decision-making directly from renal ultrasound images. We applied a deep learning model to paediatric renal ultrasound images to predict the need for surgical intervention based on the HSI. The model was developed and studied at four large quaternary free-standing paediatric hospitals in North America. We evaluated the degree to which HSI corresponded with surgical intervention at each hospital using area under the receiver-operator curve, area under the precision-recall curve, sensitivity, and specificity. HSI predicted subsequent surgical intervention with > 90% AUROC, > 90% sensitivity, and > 70% specificity in a test set of 202 patients from the same institution. At three external institutions, HSI corresponded with AUROCs ≥ 90%, sensitivities ≥ 80%, and specificities > 50%. It is possible to automatically and reliably assess HN severity directly from a single ultrasound. The HSI stratifies low- and high-risk HN patients thus helping to triage low-risk patients while maintaining very high sensitivity to surgical cases. HN severity can be predicted from a single patient ultrasound using a novel image-based artificial intelligence system.

Unmasking Ureteral Polyps: Insights From a Case Series.

Fibroepithelial polyps in the urinary tract are a rare cause of obstructive uropathy with fewer than 130 cases reported in the literature. In our series, we describe polyps that were missed on preoperative imaging and later found in the operating room during pyeloplasty. It is critical for urologists to be aware of polyps as a potential source of obstruction as they can increase the complexity of a reconstruction and, if missed, may result in a failed repair and persistent obstruction. We hypothesize that performing a retrograde pyelogram prior to ureteric reconstruction will facilitate diagnosis prior to surgical repair.

Most of the photons that reionized the Universe came from dwarf galaxies.

The identification of sources driving cosmic reionization, a major phase transition from neutral hydrogen to ionized plasma around 600-800 Myr after the Big Bang1-3, has been a matter of debate4. Some models suggest that high ionizing emissivity and escape fractions (fesc) from quasars support their role in driving cosmic reionization5,6. Others propose that the high fesc values from bright galaxies generate sufficient ionizing radiation to drive this process7. Finally, a few studies suggest that the number density of faint galaxies, when combined with a stellar-mass-dependent model of ionizing efficiency and fesc, can effectively dominate cosmic reionization8,9. However, so far, comprehensive spectroscopic studies of low-mass galaxies have not been done because of their extreme faintness. Here we report an analysis of eight ultra-faint galaxies (in a very small field) during the epoch of reionization with absolute magnitudes between MUV ≈ -17 mag and -15 mag (down to 0.005L⋆ (refs. 10,11)). We find that faint galaxies during the first thousand million years of the Universe produce ionizing photons with log[ξion (Hz erg-1)] = 25.80 ± 0.14, a factor of 4 higher than commonly assumed values12. If this field is representative of the large-scale distribution of faint galaxies, the rate of ionizing photons exceeds that needed for reionization, even for escape fractions of the order of 5%.

Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis.

INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) in pediatric urology is gaining increased popularity and credibility. However, the literature lacks standardization in reporting and there are areas for methodological improvement, which incurs difficulty in comparison between studies and may ultimately hurt clinical implementation of these models. The "STandardized REporting of Applications of Machine learning in UROlogy" (STREAM-URO) framework provides methodological instructions to improve transparent reporting in urology and APPRAISE-AI in a critical appraisal tool which provides quantitative measures for the quality of AI studies. The adoption of these will allow urologists and developers to ensure consistency in reporting, improve comparison, develop better models, and hopefully inspire clinical translation. METHODS: In this article, we have applied STREAM-URO framework and APPRAISE-AI tool to the pediatric hydronephrosis literature. By doing this, we aim to describe best practices on ML reporting in urology with STREAM-URO and provide readers with a critical appraisal tool for ML quality with APPRAISE-AI. By applying these to the pediatric hydronephrosis literature, we provide some tutorial for other readers to employ these in developing and appraising ML models. We also present itemized recommendations for adequate reporting, and critically appraise the quality of ML in pediatric hydronephrosis insofar. We provide examples of strong reporting and highlight areas for improvement. RESULTS: There were 8 ML models applied to pediatric hydronephrosis. The 26-item STREAM-URO framework is provided in Appendix A and 24-item APPRAISE-AI tool is provided in Appendix B. Across the 8 studies, the median compliance with STREAM-URO was 67 % and overall study quality was moderate. The highest scoring APPRAISE-AI domains in pediatric hydronephrosis were clinical relevance and reporting quality, while the worst were methodological conduct, robustness of results, and reproducibility. CONCLUSIONS: If properly conducted and reported, ML has the potential to impact the care we provide to patients in pediatric urology. While AI is exciting, the paucity of strong evidence limits our ability to translate models to practice. The first step toward this goal is adequate reporting and ensuring high quality models, and STREAM-URO and APPRAISE-AI can facilitate better reporting and critical appraisal, respectively.

A high black-hole-to-host mass ratio in a lensed AGN in the early Universe.

Early JWST observations have uncovered a population of red sources that might represent a previously overlooked phase of supermassive black hole growth1-3. One of the most intriguing examples is an extremely red, point-like object that was found to be triply imaged by the strong lensing cluster Abell 2744 (ref. 4). Here we present deep JWST/NIRSpec observations of this object, Abell2744-QSO1. The spectroscopy confirms that the three images are of the same object, and that it is a highly reddened (AV ≃ 3) broad emission line active galactic nucleus at a redshift of zspec = 7.0451 ± 0.0005. From the width of Hß (full width at half-maximum = 2,800 ± 250 km s-1), we derive a black hole mass of M BH = 4 - 1 + 2 × 1 0 7 M ⊙ . We infer a very high ratio of black-hole-to-galaxy mass of at least 3%, an order of magnitude more than that seen in local galaxies5 and possibly as high as 100%. The lack of strong metal lines in the spectrum together with the high bolometric luminosity (Lbol = (1.1 ± 0.3) × 1045 erg s-1) indicate that we are seeing the black hole in a phase of rapid growth, accreting at 30% of the Eddington limit. The rapid growth and high black-hole-to-galaxy mass ratio of Abell2744-QSO1 suggest that it may represent the missing link between black hole seeds6 and one of the first luminous quasars7.

Predicting obstruction risk using common ultrasonography parameters in paediatric hydronephrosis with machine learning.

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.

Penile keloid formation post-circumcision: A case series and review of literature.

The formation of penile keloid after circumcision is an uncommon complication. Herein, we report two pediatric cases of large circumferential keloids that developed post-circumcision and were successfully treated by surgical excision and intralesional triamcinolone injections. In addition, we provide a comprehensive review of the reported cases of penile keloids that developed after circumcision in the literature to highlight the various presentations, treatment options, and outcomes for this condition.

Posterior urethral valves in patients with trisomy 21: Similar renal outcomes and rates of volitional voiding.

INTRODUCTION: Posterior urethral valves (PUV) occur in patients with Down Syndrome (DS) at a rate of 3-4%; far higher than the general population. Our understanding of the relationship between PUVs and DS is in its infancy, with the majority of the literature consisting of case reports. In this study, we present the largest known series of DS patients with PUVs. AIM: We hypothesized that patients with DS and PUVs would have worse functional bladder outcomes and renal outcomes when compared to PUV patients without DS. STUDY DESIGN: We queried our prospectively managed multi-institutional database of PUV patients from 1990 to 2021. We identified patients with a concomitant diagnosis of DS and PUV. In addition, we performed a systematic review of the literature describing the presentation of children with PUV and DS. Patient demographics, renal outcomes, voiding habits, surgical interventions, and radiologic images were aggregated and analyzed. RESULTS: Out of the 537 patients in our PUV database, we identified 18 patients with a concomitant diagnosis of PUV and DS, as well as 14 patients with a concomitant diagnosis of PUV and DS from the literature. DS and non-DS patients had a similar age at presentation, 31.5 days (2-731) and 17 (4-846), and length of follow up 6.32 years (2-11.2) and 6.98 (1-13). Both groups had similar nadir creatinines DS 0.43 (0.4-0.8), non-DS 0.31 (0.2-0.5) and similar rates of renal failure (DS 11.1% and non-DS 14.5%). With respect to bladder outcomes, a similar percentage of patients were volitionally voiding at last follow up (DS 72.2% and non-DS 72.3%). Our literature review corroborated these findings. CONCLUSIONS: Patients with DS and PUV have similar renal outcomes to other PUV patients in terms of renal function, progression to renal failure, and probability of volitional voiding with continence. Given the increased rate of PUVs in the DS population, physicians should have a high index of suspicion for PUV when patients with DS present with voiding dysfunction.

Immunogenicity of Oral Rabies Vaccine Strain SPBN GASGAS in Local Dogs in Bali, Indonesia.

Dog-mediated rabies is endemic in much of Indonesia, including Bali. Most dogs in Bali are free-roaming and often inaccessible for parenteral vaccination without special effort. Oral rabies vaccination (ORV) is considered a promising alternative to increase vaccination coverage in these dogs. This study assessed immunogenicity in local dogs in Bali after oral administration of the highly attenuated third-generation rabies virus vaccine strain SPBN GASGAS. Dogs received the oral rabies vaccine either directly or by being offered an egg-flavored bait that contained a vaccine-loaded sachet. The humoral immune response was then compared with two further groups of dogs: a group that received a parenteral inactivated rabies vaccine and an unvaccinated control group. The animals were bled prior to vaccination and between 27 and 32 days after vaccination. The blood samples were tested for the presence of virus-binding antibodies using ELISA. The seroconversion rate in the three groups of vaccinated dogs did not differ significantly: bait: 88.9%; direct-oral: 94.1%; parenteral: 90.9%; control: 0%. There was no significant quantitative difference in the level of antibodies between orally and parenterally vaccinated dogs. This study confirms that SPBN GASGAS is capable of inducing an adequate immune response comparable to a parenteral vaccine under field conditions in Indonesia.

Automated Society of Fetal Urology (SFU) grading of hydronephrosis on ultrasound imaging using a convolutional neural network.

INTRODUCTION: Grading of hydronephrosis severity on postnatal renal ultrasound guides management decisions in antenatal hydronephrosis (ANH). Multiple systems exist to help standardize hydronephrosis grading, yet poor inter-observer reliability persists. Machine learning methods may provide tools to improve the efficiency and accuracy of hydronephrosis grading. OBJECTIVE: To develop an automated convolutional neural network (CNN) model to classify hydronephrosis on renal ultrasound imaging according to the Society of Fetal Urology (SFU) system as potential clinical adjunct. STUDY DESIGN: A cross-sectional, single-institution cohort of postnatal renal ultrasounds with radiologist SFU grading from pediatric patients with and without hydronephrosis of stable severity was obtained. Imaging labels were used to automatedly select sagittal and transverse grey-scale renal images from all available studies from each patient. A VGG16 pre-trained ImageNet CNN model analyzed these preprocessed images. Three-fold stratified cross-validation was used to build and evaluate the model that was used to classify renal ultrasounds on a per patient basis into five classes based on the SFU system (normal, SFU I, SFU II, SFU III, or SFU IV). These predictions were compared to radiologist grading. Confusion matrices evaluated model performance. Gradient class activation mapping demonstrated imaging features driving model predictions. RESULTS: We identified 710 patients with 4659 postnatal renal ultrasound series. Per radiologist grading, 183 were normal, 157 were SFU I, 132 were SFU II, 100 were SFU III, and 138 were SFU IV. The machine learning model predicted hydronephrosis grade with 82.0% (95% CI: 75-83%) overall accuracy and classified 97.6% (95% CI: 95-98%) of the patients correctly or within one grade of the radiologist grade. The model classified 92.3% (95% CI: 86-95%) normal, 73.2% (95% CI: 69-76%) SFU I, 73.5% (95% CI: 67-75%) SFU II, 79.0% (95% CI: 73-82%) SFU III, and 88.4% (95% CI: 85-92%) SFU IV patients accurately. Gradient class activation mapping demonstrated that the ultrasound appearance of the renal collecting system drove the model's predictions. DISCUSSION: The CNN-based model classified hydronephrosis on renal ultrasounds automatically and accurately based on the expected imaging features in the SFU system. Compared to prior studies, the model functioned more automatically with greater accuracy. Limitations include the retrospective, relatively small cohort, and averaging across multiple imaging studies per patient. CONCLUSIONS: An automated CNN-based system classified hydronephrosis on renal ultrasounds according to the SFU system with promising accuracy based on appropriate imaging features. These findings suggest a possible adjunctive role for machine learning systems in the grading of ANH.