An automatic parathyroid recognition and segmentation model based on deep learning of near‐infrared autofluorescence imaging

Author:

Yu Fan1ORCID,Sang Tian2,Kang Jie1,Deng Xianzhao1,Guo Bomin1,Yang Hangzhou1,Chen Xiaoyi3,Fan Youben1,Ding Xuehai2,Wu Bo1

Affiliation:

1. Department of Thyroid Breast and Hernia Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China

2. School of Computer Engineering and Science Shanghai University Shanghai China

3. Ningbo Institute of Life and Health Industry University of Chinese Academy of Sciences Ningbo China

Abstract

AbstractIntroductionNear‐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.MethodsFive 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.ResultsOur 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).ConclusionsThis AI model will help surgeons identify PGs, and develop their learning ability and self‐confidence.

Publisher

Wiley

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