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Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study 期刊论文

编号：

0F7026F39E5E2BFF74185A6510BA5309
作者：

Tie, ChengWei#^[1]Li, DeYang#^[2]Zhu, JiQing^[1];Wang, MeiLing^[3,4];Wang, JianHui^[5];Chen, BingHong^[3,4];Li, Ying^[3,4];Zhang, Sen^[6];Liu, Lin^[7];Guo, Li^[8];Yang, Long^[9];Yang, LiQun^[9];Wei, Jiao^[10];Jiang, Feng^[11];Zhao, ZhiQiang^[12];Wang, GuiQi(王贵齐)*^[1]Zhang, Wei*^[3,4]Zhang, QuanMao*^[5]Ni, XiaoGuang(倪晓光)*^[1]
语种：

英文
期刊：

LARYNGOSCOPE ISSN：0023-852X 2024 年 134 卷 10 期 (4321 - 4328) ; OCT
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关键词：

laryngoscopy multi-instance learning narrow-band imaging vocal fold leukoplakia white light imaging
摘要：

Objectives: Vocal fold leukoplakia (VFL) is a precancerous lesion of laryngeal cancer, and its endoscopic diagnosis poses challenges. We aim to develop an artificial intelligence (AI) model using white light imaging (WLI) and narrow-band imaging (NBI) to distinguish benign from malignant VFL. Methods: A total of 7057 images from 426 patients were used for model development and internal validation. Additionally, 1617 images from two other hospitals were used for model external validation. Modeling learning based on WLI and NBI modalities was conducted using deep learning combined with a multi-instance learning approach (MIL). Furthermore, 50 prospectively collected videos were used to evaluate real-time model performance. A human-machine comparison involving 100 patients and 12 laryngologists assessed the real-world effectiveness of the model. Results: The model achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.868 and 0.884 in the internal and external validation sets, respectively. AUC in the video validation set was 0.825 (95% CI: 0.704-0.946). In the human-machine comparison, AI significantly improved AUC and accuracy for all laryngologists (p < 0.05). With the assistance of AI, the diagnostic abilities and consistency of all laryngologists improved. Conclusions: Our multicenter study developed an effective AI model using MIL and fusion of WLI and NBI images for VFL diagnosis, particularly aiding junior laryngologists. However, further optimization and validation are necessary to fully assess its potential impact in clinical settings. Level of Evidence3 Laryngoscope, 2024
推荐引用方式
GB/T 7714：

Tie Cheng-Wei,Li De-Yang,Zhu Ji-Qing, et al. Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study [J].LARYNGOSCOPE,2024,134(10):4321-4328.
APA：

Tie Cheng-Wei,Li De-Yang,Zhu Ji-Qing,Wang Mei-Ling,&Ni Xiao-Guang.(2024).Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study .LARYNGOSCOPE,134(10):4321-4328.
MLA：

Tie Cheng-Wei, et al. "Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study" .LARYNGOSCOPE 134,10(2024):4321-4328.
入库时间：

2024/6/8 20:55:53
更新时间：

2024/6/8 20:55:53
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