Evaluating the value of AI-generated questions for USMLE step 1 preparation: A study using ChatGPT-3.5.

Purpose: Students are increasingly relying on artificial intelligence (AI) for medical education and exam preparation. However, the factual accuracy and content distribution of AI-generated exam questions for self-assessment have not been systematically investigated.

Methods: Curated prompts were created to generate multiple-choice questions matching the USMLE Step 1 examination style.

Automated Intraoperative Visual Detection of Pediatric Epileptogenic Brain Lesions Using a Machine Learning Classifier.

450,000 children with epilepsy in the United States suffer lifelong disability and are at risk of sudden death. Surgical treatment of epilepsy is limited by the ability to visually discriminate between normal and abnormal brain tissue using visual light surgical microscopes: resection of excessive tissue can lead to neurologic injury, while insufficient resection often does not lead to durable cures. We propose a machine-learning-based segmentation model to identify epileptogenic, abnormal tissue thereby improving accuracy of surgical resection.

Optimal timing of endoscopic sagittal suturectomy.

Objective: Endoscopic sagittal suturectomy (ESS) is commonly offered for sagittal craniosynostosis in infants, but the optimal timing of surgery remains controversial, with many clinicians only offering ESS surgery before 3 months of age. This study investigated whether patient age predicts craniometric correction and, more specifically, whether patients > 3 months of age at surgery manifest less correction. The effects of age on blood transfusion were also investigated.

Controversies in Artificial Intelligence in Neurosurgery.

Artificial intelligence (AI) has evolved from science fiction to a technology infiltrating everyday life. In neurosurgery, clinicians and researchers are exploring ways to implement this powerful tool to improve the safety and efficiency of the perioperative process. Current applications include preoperative diagnosis, intraoperative detection and recommendations, and technical skills assessment and feedback.

MR-guided focused ultrasound in pediatric neurosurgery: current insights, technical challenges, and lessons learned from 45 treatments at Children's National Hospital.

Objective: MR-guided focused ultrasound (MRgFUS) is an evolving technology with numerous present and potential applications in pediatric neurosurgery. The aim of this study was to describe the use of MRgFUS, technical challenges, complications, and lessons learned at a single children's hospital.

Methods: A retrospective analysis was performed of a prospectively collected database of all pediatric patients undergoing investigational use of MRgFUS for treatment of various neurosurgical pathologies at Children's National Hospital.

A microdiscectomy surgical video annotation framework for supervised machine learning applications.

Purpose: Lumbar discectomy is among the most common spine procedures in the US, with 300,000 procedures performed each year. Like other surgical procedures, this procedure is not excluded from potential complications. This paper presents a video annotation methodology for microdiscectomy including the development of a surgical workflow.

The role of focused ultrasound for pediatric brain tumors: current insights and future implications on treatment strategies.

Introduction: Focused ultrasound (FUS) is an innovative and emerging technology for the treatment of adult and pediatric brain tumors and illustrates the intersection of various specialized fields, including neurosurgery, neuro-oncology, radiation oncology, and biomedical engineering.

Objective: The authors provide a comprehensive overview of the application and implications of FUS in treating pediatric brain tumors, with a special focus on pediatric low-grade gliomas (pLGGs) and the evolving landscape of this technology and its clinical utility.

Methods: The fundamental principles of FUS include its ability to induce thermal ablation or enhance drug delivery through transient blood-brain barrier (BBB) disruption, emphasizing the adaptability of high-intensity focused ultrasound (HIFU) and low-intensity focused ultrasound (LIFU) applications.

Anterior cervical discectomy and fusion for the treatment of pediatric Hirayama disease.

Purpose: Hirayama disease, a rare cervical myelopathy in children and young adults, leads to progressive upper limb weakness and muscle loss. Non-invasive external cervical orthosis has been shown to prevent further neurologic decline; however, this treatment modality has not been successful at restoring neurologic and motor function, especially in long standing cases with significant weakness. The pathophysiology remains not entirely understood, complicating standardized operative guidelines; however, some studies report favorable outcomes with internal fixation.

Simulated outcomes for durotomy repair in minimally invasive spine surgery.

Minimally invasive spine surgery (MISS) is increasingly performed using endoscopic and microscopic visualization, and the captured video can be used for surgical education and development of predictive artificial intelligence (AI) models. Video datasets depicting adverse event management are also valuable, as predictive models not exposed to adverse events may exhibit poor performance when these occur. Given that no dedicated spine surgery video datasets for AI model development are publicly available, we introduce Simulated Outcomes for Durotomy Repair in Minimally Invasive Spine Surgery (SOSpine).

Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.

Pediatric brain tumors are the second most common type of cancer, accounting for one in four childhood cancer types. Brain tumor resection surgery remains the most common treatment option for brain cancer. While assessing tumor margins intraoperatively, surgeons must send tissue samples for biopsy, which can be time-consuming and not always accurate or helpful.

Socioeconomic Disparities Affecting the Presentation and Outcomes in Pediatric Subdural Empyema Patients.

Background And Objectives: Pediatric subdural empyemas (SDE) carry significant morbidity and mortality, and prompt diagnosis and treatment are essential to ensure optimal outcomes. Nonclinical factors affect presentation, time to diagnosis, and outcomes in several neurosurgical conditions and are potential causes of delay in presentation and treatment for patients with SDE. To evaluate whether socioeconomic status, race, and insurance status affect presentation, time to diagnosis, and outcomes for children with subdural empyema.

Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.

Background And Objectives: Assessment and feedback are critical to surgical education, but direct observational feedback by experts is rarely provided because of time constraints and is typically only qualitative. Automated, video-based, quantitative feedback on surgical performance could address this gap, improving surgical training. The authors aim to demonstrate the ability of Shannon entropy (ShEn), an information theory metric that quantifies series diversity, to predict surgical performance using instrument detections generated through deep learning.

Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care.

The worlds of spinal surgery and computational science are intersecting at the nexus of the operating room and across the continuum of patient care. As medicine moves toward digitizing all aspects of a patient's care, immense amounts of patient data generated and aggregated across surgeons, procedures, and institutions will enable previously inaccessible computationally driven insights. These early insights from artificial intelligence (AI) and machine learning (ML)-enabled technologies are beginning to transform medicine and surgery.

A methodology for the annotation of surgical videos for supervised machine learning applications.

Purpose: Surgical data science is an emerging field focused on quantitative analysis of pre-, intra-, and postoperative patient data (Maier-Hein et al. in Med Image Anal 76: 102306, 2022). Data science approaches can decompose complex procedures, train surgical novices, assess outcomes of actions, and create predictive models of surgical outcomes (Marcus et al.

A vision transformer for decoding surgeon activity from surgical videos.

The intraoperative activity of a surgeon has substantial impact on postoperative outcomes. However, for most surgical procedures, the details of intraoperative surgical actions, which can vary widely, are not well understood. Here we report a machine learning system leveraging a vision transformer and supervised contrastive learning for the decoding of elements of intraoperative surgical activity from videos commonly collected during robotic surgeries.

A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons.

Background: Surgeons who receive reliable feedback on their performance quickly master the skills necessary for surgery. Such performance-based feedback can be provided by a recently-developed artificial intelligence (AI) system that assesses a surgeon's skills based on a surgical video while simultaneously highlighting aspects of the video most pertinent to the assessment. However, it remains an open question whether these highlights, or explanations, are equally reliable for all surgeons.

Vertebral Aneurysmal Bone Cyst Mimicking Osteosarcoma: Case Report and Review of the Literature.

Aneurysmal bone cysts are benign osseous lesions containing blood-filled cavities separated by walls of connective tissue. They can be difficult to identify clinically due to similarities in presentation, imaging, and histology with other pathologies. Specifically, it is important to distinguish these benign lesions from malignant processes, as both surgical and medical management differ.