A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.

Background: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images.

Method: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level.

Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.

Objectives: This study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.

Design: A classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset.

Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images.

Objective: To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.

Methods: A DCNN-based diagnostic system was constructed and trained using 13,721 laryngoscopic images of LCA, precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT) and normal tissues (NORM) from 2 tertiary hospitals in China, including 2293 from 206 LCA subjects, 1807 from 203 PRELCA subjects, 6448 from 774 BLT subjects and 3191 from 633 NORM subjects. An independent test set of 1176 laryngoscopic images from other 3 tertiary hospitals in China, including 132 from 44 LCA subjects, 129 from 43 PRELCA subjects, 504 from 168 BLT subjects and 411 from 137 NORM subjects, was applied to the constructed DCNN to evaluate its performance against experienced endoscopists.