Harada GK, Siyaji ZK, Mallow GM, et al. - This study was intended to use machine-learning (ML) analytics to predict lumbar recurrent herniated nucleus pulposus (re-HNP), whereby a personalized risk prediction can be developed as a clinical tool. Researchers performed a retrospective, single center study including a total of 2,630 consecutive patients that had undergone lumbar microdiscectomy (mean follow-up: 22-months). They reported various preoperative patient pain/disability/functional profiles, imaging parameters, and anthropomorphic/demographic metrics. According to the findings, predictive modeling via an ML approach of the large-scale cohort is the first study that has distinguished significant risk factors for the development of re-HNP after initial lumbar decompression. The re-herniation was developed after decompression profile index that has been translated into an online screening tool to identify low–high risk patients for re-HNP. There is a need for additional validation for potential global implementation.