AI Article Synopsis
- - The study aimed to create and validate an AI system using a deep convolutional neural network (DCNN) to identify nasopharyngeal carcinoma (NPC) from historical nasopharyngoscopic images, utilizing a large dataset of over 14,000 images.
- - The DCNN model, built on the YOLOv5 architecture, achieved impressive results with precision, recall, and accuracy rates significantly higher than junior otolaryngologists when tested on a separate validation dataset of 3,501 images.
- - The results suggest that this AI model consistently outperforms junior doctors, indicating its potential to enhance diagnostic accuracy and reduce missed cases of NPC in clinical settings.
Article Abstract
Objective: To construct and validate a deep convolutional neural network (DCNN)-based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images.
Methods: We retrospectively collected 14107 nasopharyngoscopic images (7108 NPCs and 6999 noncancers) to construct a DCNN model and prepared a validation dataset containing 3501 images (1744 NPCs and 1757 noncancers) from a single center between January 2009 and December 2020. The DCNN model was established using the You Only Look Once (YOLOv5) architecture. Four otolaryngologists were asked to review the images of the validation set to benchmark the DCNN model performance.
Results: The DCNN model analyzed the 3501 images in 69.35 s. For the validation dataset, the precision, recall, accuracy, and F1 score of the DCNN model in the detection of NPCs on white light imaging (WLI) and narrow band imaging (NBI) were 0.845 ± 0.038, 0.942 ± 0.021, 0.920 ± 0.024, and 0.890 ± 0.045, and 0.895 ± 0.045, 0.941 ± 0.018, and 0.975 ± 0.013, 0.918 ± 0.036, respectively. The diagnostic outcome of the DCNN model on WLI and NBI images was significantly higher than that of two junior otolaryngologists (p < 0.05).
Conclusion: The DCNN model showed better diagnostic outcomes for NPCs than those of junior otolaryngologists. Therefore, it could assist them in improving their diagnostic level and reducing missed diagnoses.
Level Of Evidence: 3 Laryngoscope, 134:127-135, 2024.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/lary.30781 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!