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. RESULTS: Several ongoing clinical trials explore the potential of FUS in offering alternative therapeutic strategies for pathologies where conventional treatments fall short, specifically centrally-located benign CNS tumors and diffuse intrinsic pontine glioma (DIPG). A case illustration involving the use of HIFU for pilocytic astrocytoma is presented. CONCLUSION: Discussions regarding future applications of FUS for the treatment of gliomas include improved drug delivery, immunomodulation, radiosensitization, and other technological advancements.

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). A validated MISS cadaveric dural repair simulator was used to educate neurosurgery residents, and surgical microscope video recordings were paired with outcome data. Objects including durotomy, needle, grasper, needle driver, and nerve hook were then annotated. Altogether, SOSpine contains 15,698 frames with 53,238 annotations and associated durotomy repair outcomes. For validation, an AI model was fine-tuned on SOSpine video and detected surgical instruments with a mean average precision of 0.77. In summary, SOSpine depicts spine surgeons managing a common complication, providing opportunities to develop surgical AI models.

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. We report a successful surgically treated case of pediatric Hirayama disease, supplemented by a systematic review and collation of reported cases in the literature. METHODS: A review of the literature was performed by searching PubMed, Embase, and Web of Science. Full-length articles were included if they reported clinical data regarding the treatment of at least one patient with Hirayama disease and the neurologic outcome of that treatment. Articles were excluded if they did not provide information on treatment outcomes, were abstract-only publications, or were published in languages other than English. RESULTS: Of the fifteen articles reviewed, 63 patients were described, with 59 undergoing surgery. This encompassed both anterior and posterior spinal procedures and 1 hand tendon transfer. Fifty-five patients, including one from our institution, showed improvement post-treatment. Eleven of these patients were under 18 years old. CONCLUSION: Hirayama disease is an infrequent yet impactful cervical myelopathy with limited high-quality evidence available for optimal treatment. The current literature supports surgical decompression and stabilization as promising interventions. However, comprehensive research is crucial for evolving diagnosis and treatment paradigms.

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. METHODS: We conducted a retrospective cohort study with patients diagnosed with SDE between 2005 and 2020 at our institution. Information regarding demographics (age, sex, zip code, insurance status, race/ethnicity) and presentation (symptoms, number of prior visits, duration of symptoms) was collected. Outcome measures included mortality, postoperative complications, length of stay, and discharge disposition. RESULTS: 42 patients were diagnosed with SDE with a mean age of 9.5 years. Most (85.7%) (n = 36) were male ( P = .0004), and a majority, 28/42 (66.7%), were African American ( P < .0001). There was no significant difference in socioeconomic status based on zip codes, although a significantly higher number of patients were on public insurance ( P = .015). African American patients had a significantly longer duration of symptoms than their Caucasian counterparts (8.4 days vs 1.8 days P = .0316). In total, 41/42 underwent surgery for the SDE, most within 24 hours of initial neurosurgical evaluation. There were no significant differences in the average length of stay. The average length of antibiotic duration was 57.2 days and was similar for all patients. There were no significant differences in discharge disposition based on any of the factors identified with most of the patients (52.4%) being discharged to home. There was 1 mortality (2.4%). CONCLUSION: Although there were no differences in outcomes based on nonclinical factors, African American men on public insurance bear a disproportionately high burden of SDE. Further investigation into the causes of this is warranted.

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. Snapshot hyperspectral imaging (sHSI) cameras can capture scenes beyond the human visual spectrum and provide real-time guidance where we aim to segment healthy brain tissues from lesions on pediatric patients undergoing brain tumor resection. With the institutional research board approval, Pro00011028, 139 red-green-blue (RGB), 279 visible, and 85 infrared sHSI data were collected from four subjects with the system integrated into an operating microscope. A random forest classifier was used for data analysis. The RGB, infrared sHSI, and visible sHSI models achieved average intersection of unions (IoUs) of 0.76, 0.59, and 0.57, respectively, while the tumor segmentation achieved a specificity of 0.996, followed by the infrared HSI and visible HSI models at 0.93 and 0.91, respectively. Despite the small dataset considering pediatric cases, our research leveraged sHSI technology and successfully segmented healthy brain tissues from lesions with a high specificity during pediatric brain tumor resection procedures.

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. METHODS: Annotated images from a publicly available video data set of surgeons managing endoscopic endonasal carotid artery lacerations in a perfused cadaveric simulator were collected. A deep learning model was implemented to detect surgical instruments across video frames. ShEn score for the instrument sequence was calculated from each surgical trial. Logistic regression using ShEn was used to predict hemorrhage control success. RESULTS: ShEn scores and instrument usage patterns differed between successful and unsuccessful trials (ShEn: 0.452 vs 0.370, P < .001). Unsuccessful hemorrhage control trials displayed lower entropy and less varied instrument use patterns. By contrast, successful trials demonstrated higher entropy with more diverse instrument usage and consistent progression in instrument utilization. A logistic regression model using ShEn scores (78% accuracy and 97% average precision) was at least as accurate as surgeons' attending/resident status and years of experience for predicting trial success and had similar accuracy as expert human observers. CONCLUSION: ShEn score offers a summative signal about surgeon performance and predicted success at controlling carotid hemorrhage in a simulated cadaveric setting. Future efforts to generalize ShEn to additional surgical scenarios can further validate this metric.

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. The complex pathologies facing spine surgeons and their patients require integrative, multimodal, data-driven management strategies. As these data and the technological tools to computationally process them become increasingly available to spine surgeons, AI and ML methods will inform patient selection, preoperatively risk-stratify patients based on myriad factors, and inform interoperative surgical decisions. Once these tools enter early clinical practice, their use creates a virtual flywheel whereby the use of these tools generates additional data that further accelerate the evolution of computational "knowledge" systems. At this digital crossroads, interested and motivated surgeons have an opportunity to understand these technologies, guide their application toward optimal care, and advocate for opportunities where these powerful new tools can deliver step changes in efficiency, accuracy, and intelligence. In the present article, we review the nomenclature and basics of AI and ML and highlight the current and future applications of these technologies across the care continuum of spinal 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. in Pituitary 24: 839-853, 2021; Røadsch et al. in Nat Mach Intell, 2022). Surgical videos contain powerful signals of events that may impact patient outcomes. A necessary step before the deployment of supervised machine learning methods is the development of labels for objects and anatomy. We describe a complete method for annotating videos of transsphenoidal surgery. METHODS: Endoscopic video recordings of transsphenoidal pituitary tumor removal surgeries were collected from a multicenter research collaborative. These videos were anonymized and stored in a cloud-based platform. Videos were uploaded to an online annotation platform. Annotation framework was developed based on a literature review and surgical observations to ensure proper understanding of the tools, anatomy, and steps present. A user guide was developed to trained annotators to ensure standardization. RESULTS: A fully annotated video of a transsphenoidal pituitary tumor removal surgery was produced. This annotated video included over 129,826 frames. To prevent any missing annotations, all frames were later reviewed by highly experienced annotators and a surgeon reviewer. Iterations to annotated videos allowed for the creation of an annotated video complete with labeled surgical tools, anatomy, and phases. In addition, a user guide was developed for the training of novice annotators, which provides information about the annotation software to ensure the production of standardized annotations. CONCLUSIONS: A standardized and reproducible workflow for managing surgical video data is a necessary prerequisite to surgical data science applications. We developed a standard methodology for annotating surgical videos that may facilitate the quantitative analysis of videos using machine learning applications. Future work will demonstrate the clinical relevance and impact of this workflow by developing process modeling and outcome predictors.

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. We present the case of a 21-year-old patient who presented with impaired motor and sensory function in his lower extremities. Radiologic findings were concerning for an invasive neoplasm, and the intraoperative frozen section supported this conclusion. However, an additional histological investigation was confirmatory for a diagnosis of an aneurysmal bone cyst. The patient underwent corpectomy, laminectomy, and a posterior spinal fusion, and regained motor and sensory function shortly thereafter. This report details the importance of considering aneurysmal bone cysts in the differential of infiltrative bone lesions, despite their benign nature, as medical and surgical management can vary greatly.

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. METHODS: Here, we systematically quantify the reliability of AI-based explanations on surgical videos from three hospitals across two continents by comparing them to explanations generated by humans experts. To improve the reliability of AI-based explanations, we propose the strategy of training with explanations -TWIX -which uses human explanations as supervision to explicitly teach an AI system to highlight important video frames. RESULTS: We show that while AI-based explanations often align with human explanations, they are not equally reliable for different sub-cohorts of surgeons (e.g., novices vs. experts), a phenomenon we refer to as an explanation bias. We also show that TWIX enhances the reliability of AI-based explanations, mitigates the explanation bias, and improves the performance of AI systems across hospitals. These findings extend to a training environment where medical students can be provided with feedback today. CONCLUSIONS: Our study informs the impending implementation of AI-augmented surgical training and surgeon credentialing programs, and contributes to the safe and fair democratization of surgery.

Surgeons aim to master skills necessary for surgery. One such skill is suturing which involves connecting objects together through a series of stitches. Mastering these surgical skills can be improved by providing surgeons with feedback on the quality of their performance. However, such feedback is often absent from surgical practice. Although performance-based feedback can be provided, in theory, by recently-developed artificial intelligence (AI) systems that use a computational model to assess a surgeon's skill, the reliability of this feedback remains unknown. Here, we compare AI-based feedback to that provided by human experts and demonstrate that they often overlap with one another. We also show that explicitly teaching an AI system to align with human feedback further improves the reliability of AI-based feedback on new videos of surgery. Our findings outline the potential of AI systems to support the training of surgeons by providing feedback that is reliable and focused on a particular skill, and guide programs that give surgeons qualifications by complementing skill assessments with explanations that increase the trustworthiness of such assessments.

Human visual explanations mitigate bias in AI-based assessment of surgeon skills.

Artificial intelligence (AI) systems can now reliably assess surgeon skills through videos of intraoperative surgical activity. With such systems informing future high-stakes decisions such as whether to credential surgeons and grant them the privilege to operate on patients, it is critical that they treat all surgeons fairly. However, it remains an open question whether surgical AI systems exhibit bias against surgeon sub-cohorts, and, if so, whether such bias can be mitigated. Here, we examine and mitigate the bias exhibited by a family of surgical AI systems-SAIS-deployed on videos of robotic surgeries from three geographically-diverse hospitals (USA and EU). We show that SAIS exhibits an underskilling bias, erroneously downgrading surgical performance, and an overskilling bias, erroneously upgrading surgical performance, at different rates across surgeon sub-cohorts. To mitigate such bias, we leverage a strategy -TWIX-which teaches an AI system to provide a visual explanation for its skill assessment that otherwise would have been provided by human experts. We show that whereas baseline strategies inconsistently mitigate algorithmic bias, TWIX can effectively mitigate the underskilling and overskilling bias while simultaneously improving the performance of these AI systems across hospitals. We discovered that these findings carry over to the training environment where we assess medical students' skills today. Our study is a critical prerequisite to the eventual implementation of AI-augmented global surgeon credentialing programs, ensuring that all surgeons are treated fairly.

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. The system accurately identified surgical steps, actions performed by the surgeon, the quality of these actions and the relative contribution of individual video frames to the decoding of the actions. Through extensive testing on data from three different hospitals located in two different continents, we show that the system generalizes across videos, surgeons, hospitals and surgical procedures, and that it can provide information on surgical gestures and skills from unannotated videos. Decoding intraoperative activity via accurate machine learning systems could be used to provide surgeons with feedback on their operating skills, and may allow for the identification of optimal surgical behaviour and for the study of relationships between intraoperative factors and postoperative outcomes.

Validation of Machine Learning-Based Automated Surgical Instrument Annotation Using Publicly Available Intraoperative Video.

BACKGROUND: Intraoperative tool movement data have been demonstrated to be clinically useful in quantifying surgical performance. However, collecting this information from intraoperative video requires laborious hand annotation. The ability to automatically annotate tools in surgical video would advance surgical data science by eliminating a time-intensive step in research. OBJECTIVE: To identify whether machine learning (ML) can automatically identify surgical instruments contained within neurosurgical video. METHODS: A ML model which automatically identifies surgical instruments in frame was developed and trained on multiple publicly available surgical video data sets with instrument location annotations. A total of 39 693 frames from 4 data sets were used (endoscopic endonasal surgery [EEA] [30 015 frames], cataract surgery [4670], laparoscopic cholecystectomy [2532], and microscope-assisted brain/spine tumor removal [2476]). A second model trained only on EEA video was also developed. Intraoperative EEA videos from YouTube were used for test data (3 videos, 1239 frames). RESULTS: The YouTube data set contained 2169 total instruments. Mean average precision (mAP) for instrument detection on the YouTube data set was 0.74. The mAP for each individual video was 0.65, 0.74, and 0.89. The second model trained only on EEA video also had an overall mAP of 0.74 (0.62, 0.84, and 0.88 for individual videos). Development costs were $130 for manual video annotation and under $100 for computation. CONCLUSION: Surgical instruments contained within endoscopic endonasal intraoperative video can be detected using a fully automated ML model. The addition of disparate surgical data sets did not improve model performance, although these data sets may improve generalizability of the model in other use cases.