基于近红外自发荧光成像深度学习的甲状旁腺自动识别与分割模型。
An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.
机构信息
Department of Thyroid, Breast and Hernia Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
School of Computer Engineering and Science, Shanghai University, Shanghai, China.
出版信息
Cancer Med. 2024 Feb;13(4):e7065. doi: 10.1002/cam4.7065.
INTRODUCTION
Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs.
METHODS
Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model.
RESULTS
Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05).
CONCLUSIONS
This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.
简介
近红外自体荧光成像（NIFI）可用于术中识别甲状旁腺（PG）。本研究的目的是建立一种新的模型，帮助外科医生更好地识别和保护 PG。
方法
选择了 523 张 NIFI 图像。通过 NIFI 记录 PG 并由人工智能（AI）模型标记。计算 PG 的识别率。分析不同经验年限的外科医生与 AI 识别之间的差异，并评估 AI 模型的诊断和治疗效果。
结果
我们的模型在内部验证集中达到了 83.5%的精度和 57.8%的召回率。AI 模型在内部和外部数据集上的视觉识别率分别为 85.2%和 82.4%。AI 模型的 PG 识别率高于初级外科医生（p<0.05）。
结论
该 AI 模型将帮助外科医生识别 PG，并提高他们的学习能力和自信心。
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