The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning

Introduction

Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process.

Methods

An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning.

Results

When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well.

Discussion

Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

Pre-Caesarean Section Vaginal Preparation with Chlorhexidine Solution in Preventing Puerperal Infectious Morbidities: A Randomized Controlled Trial

BACKGROUND

Globally, peripartum or puerperal infections account for about one tenth of maternal mortality, most of which occur in low income countries. Therefore, vaginal preparation with an antiseptic prior to a caesarean delivery could be considered an additional measure to prevent subsequent infectious morbidities.

OBJECTIVES

To evaluate vaginal preparation with 0.3% chlorhexidine solution in the prevention of endometritis, surgical site infection and post-operative fever following emergency caesarean section.

METHODS

This prospective randomized controlled trial (RCT) was conducted among 240 participants planned for emergency caesarean sections (CS) at term in the University of Medical Sciences Teaching Hospital Complex, Ondo State, Nigeria. Participants were randomised into either group "A" (study) or "B" (control). The former had vaginal preparation with 0.3% chlorhexidine gluconate immediately after anaesthesia while the latter received normal saline. Participants were followed up post-operatively during which clinical features of puerperal infectious morbidities were observed for each during admission as well as 8th and 14th days after delivery.

RESULTS

The rate and risk of endometritis were significantly lower in the study group compared to the control; 5.0% versus 13.3%, respectively (chi squared =5.004; p=0.042, RR = 0.38; 95% CI = 0.15-0.94; p = 0.042; RRR = 0.62). Post-operative fever and surgical site infection, were also lower in the study group compared to the controls, but the difference was not statistically significant.

CONCLUSION

When compared to placebo, pre-caesarean section vaginal preparation with 0.3% chlorhexidine solution significantly reduced only the rate and risk of post-operative endometritis among infectious morbidities.

Deep Learning Methods for Anatomical Landmark Detection in Video Capsule Endoscopy Images

Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopy with biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allows gastroenterologists to investigate GI tract abnormalities in detail with visualization of all parts of the GI tract. It captures continuous real time images as it is propelled in the GI tract by gut motility. Even though VCE allows for thorough examination, reviewing and analyzing up to eight hours of images (compiled as videos) is tedious and not cost effective. In order to pave way for automation of VCE-based GI disease diagnosis, detecting the location of the capsule would allow for a more focused analysis as well as abnormality detection in each region of the GI tract. In this paper, we compared four deep Convolutional Neural Network models for feature extraction and detection of the anatomical part within the GI tract captured by VCE images. Our results showed that VGG-Net has superior performance with the highest average accuracy, precision, recall and, F1-score compared to other state of the art architectures: GoogLeNet, AlexNet and, ResNet.