ABSTRACT
OBJECTIVE Little is known about the efficacy of using
artificial intelligence to identify laryngeal carcinoma from images of
vocal lesions taken in different hospitals with multiple laryngoscope
systems. This multicenter study was aimed to establish an artificial
intelligence system and provide a reliable auxiliary tool to screen for
laryngeal carcinoma.
Study Design: Multicentre case-control study
Setting: Six tertiary care centers
Participants: The laryngoscopy images were collected from 2179
patients with vocal lesions.
Outcome Measures: An automatic detection system of laryngeal
carcinoma was established based on Faster R-CNN, which was used to
distinguish vocal malignant and benign lesions in 2179 laryngoscopy
images acquired from 6 hospitals with 5 types of laryngoscopy systems.
Pathology was the gold standard to identify malignant and benign vocal
lesions.
Results: Among 89 cases of the malignant group, the classifier
was able to evaluate the laryngeal carcinoma in 66 patients (74.16%,
sensitivity), while the classifier was able to assess the benign
laryngeal lesion in 503 cases among 640 cases of the benign group
(78.59%, specificity). Furthermore, the R-CNN-based classifier achieved
an overall accuracy of 78.05% with a 95.63% negative prediction for
the testing dataset.
Conclusion: This automatic diagnostic system has the potential
to assist clinical laryngeal carcinoma diagnosis, which may improve and
standardize the diagnostic capacity of endoscopists using different
laryngoscopes.
Keywords: Artificial intelligence, Vocal fold lesions,
Laryngoscope, Laryngeal Carcinoma, multicenter study