Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning's ability to provide diagnoses and treatment plans for example neuro-oncology cases.

OBJECTIVE: Assess the capabilities of ChatGPT-3.5 and 4 to provide accurate diagnoses, treatment options, and treatment plans for brain tumors in example neuro-oncology cases. METHODS: ChatGPT-3.5 and 4 were provided with twenty example neuro-oncology cases of brain tumors, all selected from medical textbooks. The artificial intelligence programs were asked to give a diagnosis, treatment option, and treatment plan for each of these twenty example cases. Team members first determined in which cases ChatGPT-3.5 and 4 provided the correct diagnosis or treatment plan. Twenty neurosurgeons from the researchers' institution then independently rated the diagnoses, treatment options, and treatment plans provided by both artificial intelligence programs for each of the twenty example cases, on a scale of one to ten, with ten being the highest score. To determine whether the difference between the scores of ChatGPT-3.5 and 4 was statistically significant, a paired t-test was conducted for the average scores given to the programs for each example case. RESULTS: In the initial analysis of correct responses, ChatGPT-4 had an accuracy of 85% for its diagnoses of example brain tumors and an accuracy of 75% for its provided treatment plans, while ChatGPT-3.5 only had an accuracy of 65% and 10%, respectively. The average scores given by the twenty independent neurosurgeons to ChatGPT-4 for its accuracy of diagnosis, provided treatment options, and provided treatment plan were 8.3, 8.4, and 8.5 out of 10, respectively, while ChatGPT-3.5's average scores for these categories of assessment were 5.9, 5.7, and 5.7. These differences in average score are statistically significant on a paired t-test, with a p-value of less than 0.001 for each difference. CONCLUSIONS: ChatGPT-4 demonstrates great promise as a diagnostic tool for brain tumors in neuro-oncology, as attested to by the program's performance in this study and its assessment by surveyed neurosurgeon reviewers.

Virtual neurosurgery anatomy laboratory: A collaborative and remote education experience in the metaverse.

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.

Traumatic subgaleal hematoma in patient with Ehlers-Danlos syndrome: A rare case report.

Background: A subgaleal hematoma (SGH) describes scalp bleeding in the potential space between the periosteum and the galea aponeurosis. This hematoma generally occurs after vacuum-assisted and forceps delivery, but may also be seen following head trauma. Despite its benign course, SGHs may complicate by life-threatening events. Case Description: We report a case of a 10-year-old male with Ehlers-Danlos syndrome presenting with scalp swelling following minor head trauma. On examination, a small swelling was observed in the occipital region. During the follow up, as the volume of subgaleal hematoma was increasing, we performed needle aspiration to achieve volume reduction, and dressed with a cap like bandage that wrapped and compressed scalp. The patient was hospitalized due to hemodynamic instability and a blood transfusion was performed. Due to extended usage of compressive bandage, a large area of scalp tissue became necrotic. The necrotic scalp tissue was debrided and reconstructed by plastic and reconstructive surgery. After surgery, another hematoma formed extending from the front of the ear to the ipsilateral neck caused facial paralysis, this hematoma was evacuated and a drain was placed. The patient was followed up for 1 year and no recurrent cephalhematoma was observed. Conclusion: Ehlers-Danlos is a rarely encountered connective tissue syndrome, this case underscores the importance for neurosurgery physicians to recognize the potential catastrophes, these patients may present with following even minor injury.

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities.

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes. Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall "hubness" measure combining all five were studied. Group-level ranking-based aggregation method ("measure-then-aggregate") was used to investigate network properties on population level. Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88-0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level. Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.