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.

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.

Assessment of a Perfusion and Ventilation Method for Detecting Lung and Liver Injury in a Cadaveric Model

Surgical simulation models have been developed using post-mortem human subjects (PMHS). These models involve the pressurization and ventilation of the PMHS to create a more realistic environment for training and the practice of surgical procedures. The overall objective of this study was to determine the feasibility of a previously developed surgical simulation model to detect soft tissue injuries during a ballistic impact to the torso. One of the main limitations of using PMHS for the assessment of soft tissue injuries in the field of injury biomechanics is the lack of physiological blood flow. To overcome this limitation, the assessment of the surgical simulation model for use in injury biomechanics applications was conducted based on data collected from behind armor blunt trauma (BABT) case studies. Documented injuries in real-world cases included anterior lung contusion, posterior lung contusion, and liver contusion. These real-world injuries were compared to those seen post-impact in the PMHS using pathological and histological techniques. Discussion of limitations and future work is presented.

Resident Training in Spine Surgery: A Systematic Review of Simulation-Based Educational Models

OBJECTIVE

With the increasing prevalence of spine surgery, ensuring effective resident training is becoming of increasing importance. Training safe, competent surgeons relies heavily on effective teaching of surgical indications and adequate practice to achieve a minimum level of technical proficiency before independent practice. American Council of Graduate Medical Education work-hour restrictions have complicated the latter, forcing programs to identify novel methods of surgical resident training. Simulation-based training is one such method that can be used to complement traditional training. The present review aims to evaluate the educational success of simulation-based models in the spine surgical training of residents.

METHODS

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the PubMed, Web of Science, and Google Scholar databases were systematically screened for English full-text studies examining simulation-based spine training curricula. Studies were categorized based on simulation model class, including animal-cadaveric, human-cadaveric, physical/3-dimensional, and computer-based/virtual reality. Outcomes studied included participant feedback regarding the simulator and competency metrics used to evaluate participant performance.

RESULTS

Seventy-two studies were identified. Simulators displayed high face validity and were useful for spine surgery training. Objective measures used to evaluate procedural performance included implant placement evaluation, procedural time, and technical skill assessment, with numerous simulators demonstrating a learning effect.

CONCLUSIONS

While simulation-based educational models are one potential means of training residents to perform spine surgery, traditional in-person operating room training remains pivotal. To establish the efficacy of simulators, future research should focus on improving study quality by leveraging longitudinal study designs and correlating simulation-based training with clinical outcome measures.

A novel ex vivo perfusion-based mandibular pig model for dental product testing and training

BACKGROUND

A translational ex vivo perfusion-based mandibular pig model was developed as an alternative to animal experiments, for initial assessment of biomaterials in dental and maxillofacial surgery and training. This study aimed to assess the face and content validity of the novel perfusion-based model.

METHODS

Cadaveric porcine heads were connected to an organ assist perfusion device for blood circulation and tissue oxygenation. Dental professionals and dental trainees performed a surgical procedure on the mandibula resembling a submandibular extraoral incision to create bone defects. The bone defects were filled and covered with a commercial barrier membrane. All participants completed a questionnaire using a 5-point Likert scale to assess the face and content validity of the model. Validation data between the two groups of participants were compared with Mann-Whitney U test.

RESULTS

Ten dental professionals and seven trainees evaluated the model for face and content validity. Participants reported model realism, with a mean face validity score of 3.9 ± 1.0 and a content validity of 4.1 ± 0.8. No significant differences were found for overall face and content validity between experts and trainees.

CONCLUSION

We established face and content validity in a novel perfusion-based mandibular surgery model. This model can be used as an alternative for animal studies evaluating new biomaterials and related dental and maxillofacial surgical procedural training.

The Future of Minimally Invasive Spinal Surgery

Strong forces are pushing minimally invasive spinal surgery (MISS) to the forefront of spine care. Less-invasive surgical techniques have been enabled by a variety of technical advances. Despite the promise of MISS, however, several factors, including few training opportunities, perception of a steep learning curve, and high upfront costs, have limited the adoption of these techniques. The "6 T's" is a framework highlighting key factors that must be accounted for to ensure safe and effective MISS as techniques continually evolve. Further, technological advancement in endoscopy, robotics, and augmented/virtual reality is enhancing minimally invasive surgeries to make them even less invasive and safer for patients. The evolution of these new techniques and technologies is driving the future of MISS.

Simulation Training in Spine Surgery

Simulated surgery is part of a growing paradigm shift in surgical education as a whole. Various modalities from cadaver models to virtual reality have been developed and studied within the context of surgical education. Simulation training in spine surgery has an immense potential to improve education and ultimately improve patient safety. This is due to the inherent risk of operating the spine and the technical difficulty of modern techniques. Common procedures in the modern orthopaedic armamentarium, such as pedicle screw placement, can be simulated, and proficiency is rapidly achieved before application in patients. Furthermore, complications such as dural tears can be simulated and effectively managed in a safe environment with simulation. New techniques with steeper learning curves, such as minimally invasive techniques, can now be safely simulated. Hence, augmenting surgical education through simulation has great potential to benefit trainees and practicing orthopaedic surgeons in modern spine surgery techniques. Additional work will aim to improve access to such technologies and integrate them into the current orthopaedic training curriculum.

Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance

At a time of significant global unrest and uncertainty surrounding how the delivery of clinical training will unfold over the coming years, we offer a systematic review, meta-analysis, and bibliometric analysis of global studies showing the crucial role simulation will play in training. Our aim was to determine the types of simulators in use, their effectiveness in improving clinical skills, and whether we have reached a point of global acceptance. A PRISMA-guided global systematic review of the neurosurgical simulators available, a meta-analysis of their effectiveness, and an extended analysis of their progressive scholarly acceptance on studies meeting our inclusion criteria of simulation in neurosurgical education were performed. Improvement in procedural knowledge and technical skills was evaluated. Of the identified 7405 studies, 56 studies met the inclusion criteria, collectively reporting 50 simulator types ranging from cadaveric, low-fidelity, and part-task to virtual reality (VR) simulators. In all, 32 studies were included in the meta-analysis, including 7 randomised controlled trials. A random effects, ratio of means effects measure quantified statistically significant improvement in procedural knowledge by 50.2% (ES 0.502; CI 0.355; 0.649, p < 0.001), technical skill including accuracy by 32.5% (ES 0.325; CI - 0.482; - 0.167, p < 0.001), and speed by 25% (ES - 0.25, CI - 0.399; - 0.107, p < 0.001). The initial number of VR studies (n = 91) was approximately double the number of refining studies (n = 45) indicating it is yet to reach progressive scholarly acceptance. There is strong evidence for a beneficial impact of adopting simulation in the improvement of procedural knowledge and technical skill. We show a growing trend towards the adoption of neurosurgical simulators, although we have not fully gained progressive scholarly acceptance for VR-based simulation technologies in neurosurgical education.

Tracking Scan to Incision Time in Patients with Emergent Operative Traumatic Brain Injuries as a Measure for Systems-Based Practice in Neurosurgical Trainees

BACKGROUND

Evaluation of trainee performance remains a challenge in resident education, particularly for systems-based practice (SysBP) metrics including care coordination and interdisciplinary teamwork. Time to intervention is an important modifiable outcome variable in severe traumatic brain injury (TBI) and may serve as a trackable metric for SysBP evaluation.

METHODS

We retrospectively studied time from computed tomography head scan to surgical incision (CTH-INC, minutes) among neurosurgical trainees treating patients with emergently operative TBI as a proxy SysBP measure. Our institutional operative database was utilized to identify all emergent TBI cases between July 2015 and June 2020. Patients evaluated by program year (PGY)-2 residents proceeding directly to the operating room from the emergency department were included. Statistical analysis was performed using linear regression.

RESULTS

One hundred sixty-six cases triaged by 14 PGY-2 neurosurgical trainees were analyzed. Median CTH-INC was 104 minutes (interquartile range, 82-136 minutes). CTH-INC improved 20.1% over the academic year (95% confidence interval, 4.3%-33.2%, P = 0.015). Between the first and second 6-month periods, the rate of CTH-INC within 90 minutes (29% vs. 46%, P = 0.04) improved. On a per-individual PGY-2 basis, median CTH-INC ranged from 83-127 minutes, 25th percentile CTH-INC ranged from 62-108 minutes, and fastest CTH-INC ranged from 45-92 minutes.

CONCLUSIONS

CTH-INC is an objective and trackable proxy measure for evaluating SysBP during neurosurgical training. Use of CTH-INC or other time metrics in resident evaluations should not supersede the safe and effective delivery of patient care.

The Use of a Novel Perfusion-Based Human Cadaveric Model for Simulation of Dural Venous Sinus Injury and Repair

BACKGROUND

Dural sinus injuries are potentially serious complications associated with acute blood loss. It is imperative that neurosurgery trainees are able to recognize and manage this challenging scenario.

OBJECTIVE

To assess the feasibility of a novel perfusion-based cadaveric simulation model to provide the fundamentals of dural sinus repair to neurosurgical trainees.

METHODS

A total of 10 perfusion-based human cadaveric models underwent superior sagittal sinus (SSS) laceration. Neurosurgery residents were instructed to achieve hemostasis by any method in the first trial and then repeated the trial after watching the instructional dural flap technique video. Trials were timed until hemostasis and control of the region of injury was achieved. Pre- and post-trial questionnaires were administered to assess trainee confidence levels.

RESULTS

The high-flow extravasation of the perfusion-based cadaveric model mimicked similar conditions and challenges encountered during acute SSS injury. Mean ± standard deviation time to hemostasis was 341.3 ± 65 s in the first trial and 196.9 ± 41.8 s in the second trial (P < .0001). Mean trainee improvement time was 144.4 s (42.3%). Of the least-experienced trainees with longest repair times in the initial trial, a mean improvement time of 188.3 s (44.8%) was recorded. All participants reported increased confidence on post-trial questionnaires following the simulation (median pretrial confidence of 2 vs post-trial confidence of 4, P = .002).

CONCLUSION

A perfusion-based human cadaveric model accurately simulates acute dural venous sinus injury, affording neurosurgical trainees the opportunity to hone management skills in a simulated and realistic environment.