Characterizing the motile properties of glioblastoma tumor cells could provide a useful way to predict the spread of tumors and to tailor the therapeutic approach. Radiomics has emerged as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential of radiomics to predict the motility of glioblastoma cells. Tissue specimens were obtained from 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos of specimen cells. Manual segmentation was used to define the border of the enhancing tumor T1-weighted MR images, and 107 radiomics features were extracted from the normalized image volumes. Model parameter coefficients were estimated using the adaptive lasso technique validated with leave-one-out cross validation (LOOCV) and permutation tests. The R-squared value for the predictive model was 0.60 with p-values for each individual parameter estimate less than 0.0001. Permutation test models trained with scrambled motility failed to produce a model that out-performed the model trained on the true data. The results of this work suggest that it is possible for a quantitative MRI feature-based regression model to non-invasively predict the cellular motility of glioblastomas.

Background: We aimed to develop automated data extraction pipelines with large language models (LLMs) to extract registry data from total hip arthroplasty (THA) operative notes and compare the performance with that of existing natural language processing (NLP) algorithms. Methods: We randomly sampled 1,000 primary THA cases from our institutional registry. Two human annotators manually reviewed each operative note for 3 data points: surgical approach, bearing surface, and fixation technique. All labeled THA notes were split into the development set (n = 239) and the testing set (n = 719). We developed a custom data extraction pipeline for each data point by combining an iteratively customized prompt with an LLM. The performance was compared with that of existing rules-based NLP algorithms. Results: The accuracy of LLMs was superior to that of NLP algorithms for all data points: surgical approach (96% compared with 94%), bearing surface (89% compared with 74%), and fixation technique (96% compared with 95%). Furthermore, the LLM accurately inferred the bearing surface for 80% of the notes that were ambiguous about the bearing surface. Conclusions: We developed LLM pipelines for extracting 3 registry-relevant data points from THA operative notes, demonstrating superior performance to existing NLP algorithms. Clinical Relevance: LLMs have the potential to impact clinical care, including the evaluation of electronic medical record free-text data. As registries serve as a cornerstone of orthopaedic evidence, this work demonstrates promise for LLMs to simplify, improve, and democratize the construction of registry databases from operative notes.

Artificial intelligence (AI) is increasingly shaping pediatric spine care, leveraging its rapid advancements in healthcare to improve efficiency, accuracy, and disease understanding. Moreover, machine learning and deep learning excel at detecting complex patterns. This holds promise in processing spinal deformity data, with the potential to surpass traditional statistical methods in predictive accuracy. Challenges persist, however, including unclear clinical implementation guidelines, limited model transparency, and ethical concerns surrounding data privacy and bias. Small sample sizes and the need for larger, diverse datasets further complicate integration. In order to realize AI’s transformative potential in pediatric spine care, these critical obstacles must be addressed for effective and ethical clinical adoption. This review examines the role of AI through applications such as image sorting, surgical outcome prediction, forecasting of spinal curve progression, and vertebral volumetric analysis using deep reasoning. It also explores possible intraoperative contributions from AI, including robotics and optimized screw trajectory planning, and the potential of large language models in clinical practice.

Background/Objectives: The burden of scoliosis care extends beyond treatment costs and includes missed school for patients and lost income for parents. Chronic absenteeism, defined as more than 18 days of missed school, can have a significant impact on a child’s educational progression, but missed school and work due to scoliosis treatment are not well quantified in the literature. This study investigates absenteeism among scoliosis patients and their caregivers. Methods: We conducted a retrospective comparative study of survey results based on surgery timing and surgery type. Patients and caregivers presenting for clinic visits for scoliosis treatment at a single large tertiary care center from 2014 to 2022 were queried. Results: We collected 2772 surveys from 1104 unique patients. Of these, 223 surveys from 132 patients were within one year of surgery: 140 post-fusion surveys, 71 post-nonfusion surveys, and 11 post-halo/multistage surgery surveys. A total of 2280 surveys were from 1022 nonoperatively treated patients. School absenteeism was significantly higher for surgeries during the school year compared to summer in both the fusion and nonfusion groups, though work absenteeism showed no significant differences. Halo/multistage surgery patients had the highest rates of absenteeism. Conclusions: This study highlights the impacts of scoliosis surgery timing and type on absenteeism among patients and their caregivers. Surgery during summer breaks reduces school absenteeism and academic disruption. Halo/multistage surgery patients face the greatest risk of chronic absenteeism from school, indicating a need for targeted interventions. Optimized surgical timing and planning can help families navigate the educational and financial challenges of scoliosis treatment.