Machine learning to predict pediatric choledocholithiasis: A Western Pediatric Surgery Research Consortium retrospective study

Pediatric cholelithiasis is becoming increasingly common due to the rising obesity rates among children and adolescents. Unobstructed common bile duct stone can be found in up to 30% of pediatric patients with cholelithiasis during surgery. Making a timely diagnosis of common bile duct stone can be critical for improving patient outcome. The article titled Machine Learning to Predict Pediatric Choleocholithiasis, a Western Pediatric Surgery Research Consortium. retrospective study was published in surgery in 2023. The authors evaluated if a machine learning model could accurately predict pediatric cleoliassis using clinical and laboratory data. This retrospective cohort study included 1,597 pediatric patients who underwent cholecystectomy across 10 institutions between 2016 and 2019. An extra 3's machine learning algorithm was used to evaluate nine clinical features including age, BMI, and specific lab values to predict the presence of cholelithiasis. The machine learning model demonstrated high accuracy with an area under the receiver operating characteristic curve out of 0.95 and a negative protective value of 98%, significantly outperforming previous prediction models. A limitation of the study is the reliance on the retrospective data, which could introduce bias and affect generalizability of the model. Also, machine learning is a novel technology for the medical field. Implementing this machine learning model in clinical practice could improve the accuracy of diagnosing cholelithiasis preoperatively in children and improving patient selection and resource organization and use. It could also reduce the need for invasive procedures and unnecessary imaging.