Tag: Neurosurgical training

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Artificial intelligence–based deep learning model for evaluating procedural consistency in microvascular anastomosis

J Neurosurg 144:1–10, 2026

This study presents an LSTM-based deep learning model that objectively evaluates microvascular anastomosis performance by predicting hand-motion trajectories from MediaPipe-derived hand landmarks. It quantifies consistency using Kullback-Leibler divergence and validates complementary metrics—economy and flow of motion—comparing two expert neurosurgeons (repeat sessions) and one trainee in simulated end-to-side anastomoses.

Results show low KL divergence for experts versus higher divergence for the trainee, reflecting greater consistency and efficiency. The authors discuss methodology, model architecture choices, limitations in generalizability, and potential integration into microsurgical training workflows for objective skill assessment.

Deep Learning Model: An LSTM-based neural network was developed to objectively assess consistency and precision in microvascular anastomosis by predicting and comparing suturing hand movements using video-based hand landmark tracking, eliminating the need for physical sensors.

Hand Tracking Technology: The model utilized MediaPipe Hand Landmarker, a CNN-based system that detects 21 hand landmarks from standard video, enabling detailed, sensor-free motion analysis during microsurgical simulation.

Performance Metrics: Three primary metrics were used: Kullback-Leibler (KL) divergence for consistency, economy of motion (mean Euclidean distance of hand movement), and flow of motion (median time per suture), providing quantitative, objective evaluation of surgical skill.

Experimental Setup: Two expert neurosurgeons performed microanastomosis simulations (interrupted and continuous suturing) in two sessions one year apart, and a trainee performed the same task for comparison; all sessions were recorded and analyzed using the AI pipeline.

Results and Interpretation: Experts showed low KL divergence (high consistency) and efficient, rhythmic motion, while the trainee had higher KL divergence, longer suture intervals, and more variable motion, reflecting less developed skill.

Model Application: The approach enables rapid, automated assessment of multiple trainees using standard video equipment, supporting objective tracking of skill progression and facilitating feedback in training environments.

Model Rationale: LSTM architecture was chosen for its ability to model long-term temporal dependencies in sequential hand movement data, making it suitable for predicting surgical motion patterns over extended timeframes.

Limitations and Future Directions: Current findings are based on a small sample of experts and one trainee in a simulated environment; broader validation, metric standardization (especially for KL divergence), and extension to real operative settings are needed for generalizability.

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Microsurgical training: vascular control and intraoperative vessel rupture in the human placenta infusion model

Acta Neurochirurgica (2021) 163:2525–2532

Microsurgery is a challenging discipline. Regular lab training under the operating microscope has been the environment where most surgeons have mastered the skills and techniques inherent to most microneurosurgical procedures. However, some critical scenarios remain difficult to master or simulate. We describe a step-by-step method for how to build a low-cost, feasible, and widely available model that allows residents to familiarize themselves with demanding critical situations such as intraoperative rupture of major vessels.

Methods After delivery, nine fresh human placentas were transferred to the lab. The umbilical vein was cannulated for normal saline infusion. Several hands-on procedures were performed under direct microscope vision. Operating microscope setup, allantoic membrane splitting, vascular dissection and vessel injury, and repair exercises were simulated and video recorded. Indocyanine green was administered to simulate intraoperative angiography.

Results The model can be setup in less than 15 min, with minimal cost and infrastructure requirements. All the exercises described above can be conducted with a single placenta. Umbilical vein cannulation adds realism and allows quantification of the volume of saline required to complete the exercise. The final check with indocyanine green simulates intraoperative angiography and allows the assessment of distal vessel patency.

Conclusions Minimal infrastructure requirements, simplicity, and easy setup models provide a suitable environment for regular training. The human placenta is inexpensive and widely available, making it a feasible model for residents training. Neurosurgery residents may benefit from this model to familiarize with microsurgery and critical scenarios in a risk-free environment without time or resource constraints.

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Middle Cerebral Artery Bifurcation Aneurysms: An Anatomic Classification Scheme for Planning Optimal Surgical Strategies

Middle Cerebral Artery Bifurcation Aneurysms- An Anatomic Classification Scheme for Planning Optimal Surgical Strategies

Operative Neurosurgery 10:145–155, 2014

Changing landscapes in neurosurgical training and increasing use of endovascular therapy have led to decreasing exposure in open cerebrovascular neurosurgery. To ensure the effective transition of medical students into competent practitioners, new training paradigms must be developed.

OBJECTIVE: Using principles of pattern recognition, we created a classification scheme for middle cerebral artery (MCA) bifurcation aneurysms that allows their categorization into a small number of shape pattern groups.

METHODS: Angiographic data from patients with MCA aneurysms between 1995 and 2012 were used to construct 3-dimensional models. Models were then analyzed and compared objectively by assessing the relationship between the aneurysm sac, parent vessel, and branch vessels. Aneurysms were then grouped on the basis of the similarity of their shape patterns in such a way that the in-class similarities were maximized while the total number of categories was minimized. For each category, a proposed clip strategy was developed.

RESULTS: From the analysis of 61 MCA bifurcation aneurysms, 4 shape pattern categories were created that allowed the classification of 56 aneurysms (91.8%). The number of aneurysms allotted to each shape cluster was 10 (16.4%) in category 1, 24 (39.3%) in category 2, 7 (11.5%) in category 3, and 15 (24.6%) in category 4.

CONCLUSION: This study demonstrates that through the use of anatomic visual cues, MCA bifurcation aneurysms can be grouped into a small number of shape patterns with an associated clip solution. Implementing these principles within current neurosurgery training paradigms can provide a tool that allows more efficient transition from novice to cerebrovascular expert.

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