Payman Andre A, El-Sayed Ivan, Rubio Roberto Rodriguez
Skull Base and Cerebrovascular Laboratory, University of California, San Francisco, California, USA; Department of Neurological Surgery, University of California, San Francisco, California, USA.
Skull Base and Cerebrovascular Laboratory, University of California, San Francisco, California, USA; Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, USA.
World Neurosurg. 2024 Nov;191:e403-e410. doi: 10.1016/j.wneu.2024.08.137. Epub 2024 Sep 2.
The skull base is a complex region in neurosurgery, featuring numerous foramina. Accurate identification of these foramina is imperative to avoid intraoperative complications and to facilitate educational progress in neurosurgical trainees. The intricate landscape of the skull base often challenges both clinicians and learners, necessitating innovative identification solutions. We aimed to develop a computer vision model that automates the identification and labeling of the skull base foramina from various image formats, enhancing surgical planning and educational outcomes.
We employed a deep learning methodology, specifically using a convolutional neural network architecture. Our model was trained on a dataset comprising of 3560 high-resolution, annotated images of the skull base, taken from various perspectives and lighting conditions to ensure model generalizability. Model performance was quantitatively assessed using precision and recall metrics.
The convolutional neural network model demonstrated strong performance, achieving an average precision of 0.77. At a confidence threshold of 0.28, the model reached an optimal precision of 90.4% and a recall of 89.6%. Validation on an independent test set of images corroborated the model's capability to consistently and accurately identify and label multiple skull base foramina across diverse imaging scenarios.
This study successfully introduces a highly accurate computer vision model tailored for the identification of skull base foramina, illustrating the model's potential as a transformative tool in anatomical education and intraoperative structure visualization. The findings suggest promising avenues for future research into automated anatomical recognition models, suggesting a trajectory toward increasingly sophisticated aids in neurosurgical operations and education.
颅底是神经外科中的一个复杂区域，有许多孔。准确识别这些孔对于避免术中并发症和促进神经外科实习生的教育进展至关重要。颅底错综复杂的结构常常给临床医生和学习者带来挑战，因此需要创新的识别解决方案。我们旨在开发一种计算机视觉模型，该模型能够自动识别和标记来自各种图像格式的颅底孔，从而改善手术规划和教育成果。
我们采用了深度学习方法，具体使用卷积神经网络架构。我们的模型在一个由3560张高分辨率、带注释的颅底图像组成的数据集上进行训练，这些图像从不同角度和光照条件下获取，以确保模型的通用性。使用精确率和召回率指标对模型性能进行定量评估。
卷积神经网络模型表现出色，平均精确率达到0.77。在0.28的置信阈值下，模型达到了90.4%的最佳精确率和89.6%的召回率。在独立测试图像集上的验证证实了该模型能够在各种成像场景中一致且准确地识别和标记多个颅底孔。
本研究成功引入了一种专为识别颅底孔而定制的高精度计算机视觉模型，说明了该模型作为解剖学教育和术中结构可视化变革性工具的潜力。研究结果为未来自动解剖识别模型的研究提供了有前景的途径，表明在神经外科手术和教育中朝着越来越复杂的辅助工具发展的轨迹。
