Minimizing and managing needle and instrument loss during pediatric robotic surgery.

INTRODUCTION: Lost objects and equipment malfunctions during robotic and laparoscopic cases can lead to an increase in operating time and potential risk to the patient. The literature on the management of foreign bodies during pediatric robotic-assisted surgery is limited. The purpose of the video is to review proper instrument handling to prevent loss of an object and to propose our technique for retrieving lost objects through two pediatric case examples. MATERIALS AND METHODS: The first case is a robotic-assisted laparoscopic left pyeloplasty in a 6-week-old male with congenital uteropelvic junction obstruction during which a needle was lost. In the video, we describe our techniques for safe needle passage, proper suturing technique, and recovering a lost needle. The second case is a robotic-assisted right upper pole heminephrectomy in a 14-month-old female with a duplicated renal collecting system and hydroureteronephrosis. We present the management of a rare case during which a harmonic scalpel jaw malfunctioned leading to a lost foreign body. We describe our technique for retrieving the lost fragment. RESULTS: All objects were safely removed, and patients were discharged post-op day one without complication. CONCLUSION: Our video presents two case examples of foreign bodies lost during pediatric robotic surgeries and approaches to manage each of these incidents.

Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound.

PURPOSE: Hydronephrosis is a common pediatric urological condition, characterized by dilation of the renal collecting system. Accurate identification of the severity of hydronephrosis is crucial in clinical management, as high-grade hydronephrosis can cause significant damage to the kidney. In this pilot study, we demonstrate the feasibility of machine learning in differentiating between high and low-grade hydronephrosis in pediatric patients. MATERIALS AND METHODS: We retrospectively reviewed 592 images from 90 unique patients ages 0-8 years diagnosed with hydronephrosis at the University of Chicago's Pediatric Urology Clinic. The study included 74 high-grade hydronephrosis (145 images) and 227 low-grade hydronephrosis (447 images). Patients were excluded if they had less than 2 studies prior to surgical intervention or had structural abnormalities. We developed a radiomic-based artificial intelligence algorithm incorporating computerized texture analysis and machine learning (support-vector machine) to yield a predictor of hydronephrosis grade. RESULTS: Receiver operating characteristic analysis of the classifier output yielded an area under the curve value of 0.86 (95% CI 0.81-0.92) in the task of distinguishing between low and high-grade hydronephrosis using a five-fold cross-validation by kidney. In addition, a Mann-Kendall trend test between computer output and clinical hydronephrosis grade yielded a statistically significant upward trend (p<0.001). CONCLUSIONS: Our findings demonstrate the potential of machine learning in the differentiation between low and high-grade hydronephrosis. Further studies are warranted to validate our findings and their generalizability for use in clinical practice as a means to predict clinical outcomes and the resolution of hydronephrosis.