Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition. Participants were tasked with labelling the sella on six images, initially without assistance, then augmented by AI. Mean DICE scores and the proportion of annotations encompassing the centroid of the sella were calculated. Six medical students, six junior trainees, six intermediate trainees and six experts were recruited. There was an overall improvement in sella recognition from a DICE of score 70.7% without AI assistance to 77.5% with AI assistance (+6.7; p < 0.001). Medical students used and benefitted from AI assistance the most, improving from a DICE score of 66.2% to 78.9% (+12.8; p = 0.02). This technology has the potential to augment surgical education and eventually be used as an intra-operative decision support tool.

垂体瘤被关键的神经血管结构包围，术中识别这些结构可能具有挑战性。我们之前开发了一种能够进行鞍区解剖分割的 AI 模型。本研究旨在应用该模型，并探讨 AI 辅助对临床医生解剖学识别的影响。参与者的任务是在六张图像上标记蝶鞍，最初没有帮助，然后由 AI 增强。计算平均 DICE 分数和包含蝶鞍质心的注释比例。招募了 6 名医学生、6 名初级实习生、6 名中级实习生和 6 名专家。蝶鞍识别总体上有所提高，从没有 AI 协助的 DICE 评分 70.7% 到有 AI 协助的 77.5% （+6.7;p < 0.001）。医学生使用人工智能帮助并从中受益最多，从 66.2% 的 DICE 分数提高到 78.9% （+12.8;p = 0.02）。这项技术有可能增强外科教育，并最终用作术中决策支持工具。