Background Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal
cancer, whose early diagnosis can significantly improve the prognosis and quality of life of …

Objective To develop a deep convolutional neural network (DCNN) that can automatically
detect laryngeal cancer (LCA) in laryngoscopic images. Methods A DCNN-based diagnostic …

Background Laryngeal squamous cell carcinoma (LSCC) is one of the most common tumors
of the respiratory tract. Currently, the diagnosis of LSCC is mainly based on a laryngoscopy …

Objectives/Hypothesis To develop a deep‐learning–based computer‐aided diagnosis
system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) …

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses
contributing to reduced survival rates. This study explores the application of deep …

Objectives/Hypothesis To develop a deep‐learning‐based automatic diagnosis system for
identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and …

Background There are no published reports evaluating the ability of artificial intelligence (AI)
in the endoscopic diagnosis of superficial laryngopharyngeal cancer (SLPC). We presented …

Objective To investigate the potential of deep learning for automatically delineating
(segmenting) laryngeal cancer superficial extent on endoscopic images and videos …

Objectives The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in
an advanced stage. Although the recent development of narrow‐band imaging (NBI) and …

Objectives To assess a new application of artificial intelligence for real‐time detection of
laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow‐band …