Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network

Abstract

Objectives/Hypothesis: Create an autonomous computational system to classify endoscopy findings. Study Design: Computational analysis of vocal fold images at an academic, tertiary-care laryngology practice. Methods: A series of normal and abnormal vocal fold images were obtained from the image database of an academic tertiary care laryngology practice. The benign images included normals, nodules, papilloma, polyps, and webs. A separate set of carcinoma and leukoplakia images comprised a single malignant-premalignant class. All images were classified with their existing labels. Images were randomly withheld from each class for testing. The remaining images were used to train and validate a neural network for classifying vocal fold lesions. Two classifiers were developed. A multiclass system classified the five categories of benign lesions. A separate analysis was performed using a binary classifier trained to distinguish malignantpremalignant from benign lesions.Results: Precision ranged from 71.7% (polyps) to 89.7% (papilloma), and recall ranged from 70.0% (papilloma) to 88.0% (nodules) for the benign classifier. Overall accuracy for the benign classifier was 80.8%. The binary classifier correctly identified 92.0% of the malignant-premalignant lesions with an overall accuracy of 93.0%.Conclusions: Autonomous classification of endoscopic images with artificial intelligence technology is possible. Better network implementations and larger datasets will continue to improve classifier accuracy. A clinically useful optical cancer screening system may require a multimodality approach that incorporates nonvisual spectra.

Artificial Intelligence in laryngeal endoscopy: Systematic Review and Meta-Analysis

“…Differentiation between benign and malignant laryngeal lesions that included 7 studies [23-27, 30, 32];…”

“…Comparison of AI accuracy of white light endoscopy (4 studies) [23, 27, 30, 32] with NBI method (3 studies) [24-26].…”

“…For the second group with images capacity exciding 2500 there was recognised an evident trend of increasing accuracy with the number of included images from 0.88 to 0.94 [22, 23, 27, 32]. This tendency cannot be confirmed statistically yet, due to only four performed on such scale studies so far, but it is worth noting that for these studies with huge material analysed, the pre-processing methods were very simple comparing to the first group and included only choosing images and detection of ROI.…”

Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging

“…The skill of deep learning of surgical navigation is expected to be applied to assistant clinicians in endoscopic examinations. In another study[ 49 ], the authors created an autonomous computational system to classify endoscopy findings and showed that autonomous classification of endoscopic images with AI technology is possible. The overall accuracy for the benign classifier was 80.8%.…”

Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

“…In one previous study, the binary classifier distinguished benign and malignant-premalignant lesions with an overall accuracy of 93.0%. 18 A sensitivity of 89% and a specificity of 99.33% were achieved for the malignancy by autonomous classification of endoscopic images with artificial intelligence technology. 19 However, the studies mentioned above were both carried out in a single center.…”