Background: Advances in computer sciences, including novel 3-dimensional rendering techniques, have enabled the creation of cloud-based virtual reality (VR) interfaces, making real-time peer-to-peer interaction possible even from remote locations. This study addresses the potential use of this technology for microsurgery anatomy education. Methods: Digital specimens were created using multiple photogrammetry techniques and imported into a virtual simulated neuroanatomy dissection laboratory. A VR educational program using a multiuser virtual anatomy laboratory experience was developed. Internal validation was performed by five multinational neurosurgery visiting scholars testing and assessing the digital VR models. For external validation, 20 neurosurgery residents tested and assessed the same models and virtual space. Results: Each participant responded to 14 statements assessing the virtual models, categorized under realism (n = 3), usefulness (n = 2), practicality (n = 3), enjoyment (n = 3), and recommendation (n = 3). Most responses expressed agreement or strong agreement with the assessment statements (internal validation, 94% [66/70] total responses; external validation, 91.4% [256/280] total responses). Notably, most participants strongly agreed that this system should be part of neurosurgery residency training and that virtual cadaver courses through this platform could be effective for education. Conclusion: Cloud-based VR interfaces are a novel resource for neurosurgery education. Interactive and remote collaboration between instructors and trainees is possible in virtual environments using volumetric models created with photogrammetry. We believe that this technology could be part of a hybrid anatomy curriculum for neurosurgery education. More studies are needed to assess the educational value of this type of innovative educational resource.
INTRODUCTION:The new FDA-cleared fluorescein sodium (FNa)-based confocal laser endomicroscopy (CLE) imaging system allows for intraoperative on-the-fly cellular level imaging. Two feasibility studies have been completed with intraoperative use of this CLE system in ex vivo and in vivo modalities.METHODS:Images acquired from two prospective CLE clinical studies, one ex vivo and one in vivo, were analyzed quantitatively. Two image quality parameters – brightness and contrast – were measured using Fiji software and compared between ex vivo and in vivo images for imaging timing from FNa dose and in glioma, meningioma, and intracranial metastatic tumor cases. The diagnostic performance of the two studies was compared.RESULTS:Overall, the in vivo images have higher brightness and contrast than the ex vivo images (p < 0.001). A weak negative correlation exists between image quality and timing of imaging after FNa dose for the ex vivo images, but not the in vivo images. In vivo images have higher brightness and contrast than ex vivo images acquired after FNa redosing. In vivo images have higher image quality than ex vivo images (p < 0.001) in glioma, meningioma, and intracranial metastatic tumor cases. In vivo imaging yielded higher sensitivity (90% vs. 72%) and negative predictive value (81% vs 38%) than ex vivo imaging.CONCLUSIONS:In our setting, in vivo CLE optical biopsy outperforms ex vivo CLE by producing higher quality images and less image deterioration, leading to better diagnostic performance. These results support the in vivo modality as the modality of choice for intraoperative CLE imaging.
BACKGROUND Currarino syndrome is a rare disorder that classically presents with the triad of presacral mass, anorectal malformation, and spinal dysraphism. The presacral mass is typically benign, although malignant transformation is possible. Surgical treatment of the mass and exploration and repair of associated dysraphism are indicated for diagnosis and symptom relief. There are no previous reports of Currarino syndrome in an androgen-insensitive patient. OBSERVATIONS A 17-year-old female patient presented with lack of menarche. Physical examination and laboratory investigation identified complete androgen insensitivity. Imaging analysis revealed a presacral mass lesion, and the patient was taken to surgery for resection of the mass and spinal cord untethering. Intraoperative ultrasound revealed a fibrous stalk connecting the thecal sac to the presacral mass, which was disconnected without the need for intrathecal exploration. The presacral mass was then resected, and pathological analysis revealed a mature cystic teratoma. Postoperatively, the patient recovered without neurological or gastrointestinal sequelae. LESSONS Diagnosis of incomplete Currarino syndrome may be difficult but can be identified via work-up of other disorders, such as androgen insensitivity. Intraoperative ultrasound is useful for surgical decision making and may obviate the need for intrathecal exploration during repair of dysraphism in the setting of Currarino syndrome.
OBJECTIVE Microanastomosis is one of the most technically demanding and important microsurgical skills for a neurosurgeon. A hand motion detector based on machine learning tracking technology was developed and implemented for performance assessment during microvascular anastomosis simulation. METHODS A microanastomosis motion detector was developed using a machine learning model capable of tracking 21 hand landmarks without physical sensors attached to a surgeon’s hands. Anastomosis procedures were simulated using synthetic vessels, and hand motion was recorded with a microscope and external camera. Time series analysis was performed to quantify the economy, amplitude, and flow of motion using data science algorithms. Six operators with various levels of technical expertise (2 experts, 2 intermediates, and 2 novices) were compared. RESULTS The detector recorded a mean (SD) of 27.6 (1.8) measurements per landmark per second with a 10% mean loss of tracking for both hands. During 600 seconds of simulation, the 4 nonexperts performed 26 bites in total, with a combined excess of motion of 14.3 (15.5) seconds per bite, whereas the 2 experts performed 33 bites (18 and 15 bites) with a mean (SD) combined excess of motion of 2.8 (2.3) seconds per bite for the dominant hand. In 180 seconds, the experts performed 13 bites, with mean (SD) latencies of 22.2 (4.4) and 23.4 (10.1) seconds, whereas the 2 intermediate operators performed a total of 9 bites with mean (SD) latencies of 31.5 (7.1) and 34.4 (22.1) seconds per bite. CONCLUSIONS A hand motion detector based on machine learning technology allows the identification of gross and fine movements performed during microanastomosis. Economy, amplitude, and flow of motion were measured using time series data analysis. Technical expertise could be inferred from such quantitative performance analysis.
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