Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey.
TUBITAK BILGEM, Kocaeli, Turkey.
Surg Laparosc Endosc Percutan Tech. 2023 Aug 1;33(4):327-331. doi: 10.1097/SLE.0000000000001185.
Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of adrenal veins are critical parts of adrenal surgery. The utilization of artificial intelligence and deep learning algorithms to identify anatomic structures during laparoscopic and robot-assisted surgery can be used to provide real-time guidance.
In this experimental feasibility study, intraoperative videos of patients who underwent minimally invasive transabdominal left adrenalectomy procedures between 2011 and 2022 in a tertiary endocrine referral center were retrospectively analyzed and used to develop an artificial intelligence model. Semantic segmentation of the left adrenal vein with deep learning was performed. To train a model, 50 random images per patient were captured during the identification and dissection of the left adrenal vein. A randomly selected 70% of data was used to train models while 15% for testing and 15% for validation with 3 efficient stage-wise feature pyramid networks (ESFPNet). Dice similarity coefficient (DSC) and intersection over union scores were used to evaluate segmentation accuracy.
A total of 40 videos were analyzed. Annotation of the left adrenal vein was performed in 2000 images. The segmentation network training on 1400 images was used to identify the left adrenal vein in 300 test images. The mean DSC and sensitivity for the highest scoring efficient stage-wise feature pyramid network B-2 network were 0.77 (±0.16 SD) and 0.82 (±0.15 SD), respectively, while the maximum DSC was 0.93, suggesting a successful prediction of anatomy.
Deep learning algorithms can predict the left adrenal vein anatomy with high performance and can potentially be utilized to identify critical anatomy during adrenal surgery and provide real-time guidance in the near future.
微创肾上腺切除术是切除肾上腺肿块的主要手术治疗选择。识别和结扎肾上腺静脉是肾上腺手术的关键部分。在腹腔镜和机器人辅助手术中利用人工智能和深度学习算法识别解剖结构，可以用于提供实时指导。
在这项实验可行性研究中，回顾性分析了 2011 年至 2022 年期间在一家三级内分泌转诊中心接受微创经腹腔左肾上腺切除术的患者的术中视频，并将其用于开发人工智能模型。使用深度学习对左肾上腺静脉进行语义分割。为了训练模型，每位患者在识别和解剖左肾上腺静脉时采集 50 张随机图像。70%的数据随机用于训练模型，15%用于测试，15%用于验证，使用 3 个高效阶段特征金字塔网络（ESFPNet）。使用 Dice 相似系数（DSC）和交并比分数来评估分割准确性。
共分析了 40 个视频。在 2000 张图像上进行了左肾上腺静脉的注释。在 1400 张图像上进行分割网络训练，用于识别 300 张测试图像中的左肾上腺静脉。评分最高的高效阶段特征金字塔网络 B-2 网络的平均 DSC 和灵敏度分别为 0.77（±0.16 SD）和 0.82（±0.15 SD），而最大 DSC 为 0.93，表明成功预测了解剖结构。
深度学习算法可以以较高的性能预测左肾上腺静脉的解剖结构，并有可能在不久的将来用于识别肾上腺手术中的关键解剖结构并提供实时指导。
