Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection

TL;DR

This paper introduces a two-stage video analysis method using shift contrastive anomaly detection to identify ear pathologies in pediatric otoscopy videos, outperforming clinicians and aiding diagnosis.

Contribution

The study presents a novel two-stage approach utilizing shift contrastive anomaly detection on otoscopy videos, improving diagnostic accuracy over clinicians.

Findings

Achieved 88.0% AUROC on patient-level classification.

Outperformed average clinician performance in a comparative study.

Developed a method to analyze otoscopy videos for abnormality detection.

Abstract

Ear related concerns and symptoms represents the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered disease of the middle and external presents significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitating the presence or absence of ear pathology to be determined clinically. Research has however demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. With recent advances in computer vision and machine learning, there is an increasing interest in helping clinicians to accurately diagnose middle and external ear pathology with computer-aided systems. It has been shown that AI has the capacity to analyse a single clinical image captured during examination of the ear canal and eardrum from which it can determine the likelihood of a pathognomonic pattern for a specific diagnosis being present. The capture of such an image can however be challenging especially to inexperienced clinicians. To help mitigate this technical challenge we have developed and tested a method using video sequences. We present a two stage method that first, identifies valid frames by detecting and extracting ear drum patches from the video sequence, and second, performs the proposed shift contrastive anomaly detection to flag the otoscopy video sequences as normal or abnormal. Our method achieves an AUROC of 88.0% on the patient-level and also outperforms the average of a group of 25 clinicians in a comparative study, which is the largest of such published to date. We conclude that the presented method achieves a promising first step towards automated analysis of otoscopy video.