Endoscopic experience improves interobserver agreement in the grading of esophagitis by Los Angeles classification: conventional endoscopy and optimal band image system

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn's disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture's results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

Ambulatory pH-Impedance Findings Confirm That Grade B Esophagitis Provides Objective Diagnosis of Gastroesophageal Reflux Disease

INTRODUCTION

The Lyon Consensus designates Los Angeles (LA) grade C/D esophagitis or acid exposure time (AET) >6% on impedance-pH monitoring (MII-pH) as conclusive for gastroesophageal reflux disease (GERD). We aimed to evaluate proportions with objective GERD among symptomatic patients with LA grade A, B, and C esophagitis on endoscopy.

METHODS

Demographics, clinical data, endoscopy findings, and objective proton-pump inhibitor response were collected from symptomatic prospectively enrolled patients from 2 referral centers. Off-therapy MII-pH parameters included AET, number of reflux episodes, mean nocturnal baseline impedance, and postreflux swallow-induced peristaltic wave index. Objective GERD evidence was compared between LA grades.

RESULTS

Of 155 patients (LA grade A: 74 patients, B: 61 patients, and C: 20 patients), demographics and presentation were similar across LA grades. AET >6% was seen in 1.4%, 52.5%, and 75%, respectively, in LA grades A, B, and C. Using additional MII-pH metrics, an additional 16.2% with LA grade A and 47.5% with LA grade B esophagitis had AET 4%-6% with low mean nocturnal baseline impedance and postreflux swallow-induced peristaltic wave index; there were no additional gains using the number of reflux episodes or symptom-reflux association metrics. Compared with LA grade C (100% conclusive GERD based on endoscopic findings), 100% of LA grade B esophagitis also had objective GERD but only 17.6% with LA grade A esophagitis ( P < 0.001 compared with each). Proton-pump inhibitor response was comparable between LA grades B and C (74% and 70%, respectively) but low in LA grade A (39%, P < 0.001).

DISCUSSION

Grade B esophagitis indicates an objective diagnosis of GERD.

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs convolutional neural networks for automatic classification and interpretation of routine GERD LA grade. The proposed model employs a data augmentation technique, a two-stage no-freezing fine-tuning policy, and an early stopping criterion. As a result, the proposed model exhibits high generalizability. A dataset of images from 464 patients was used for model training and validation. An additional 32 patients served as a test set to evaluate the accuracy of both the model and our trainees. Experimental results demonstrate that the best model for the development set exhibited an overall accuracy of 99.2% (grade A-B), 100% (grade C-D), and 100% (normal group) using narrow-band image (NBI) endoscopy. On the test set, the proposed model resulted in an accuracy of 87.9%, which was significantly higher than the results of the trainees (75.0% and 65.6%). The proposed GERD-VGGNet model can assist automatic classification of GERD in conventional and NBI environments and thereby increase the accuracy of interpretation of the results by inexperienced endoscopists.

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.

Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy

Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful tools for computer assisted diagnosis (CAD) systems. However, training and assessing a computer's ability to diagnose like a human are complex tasks, and successful outcomes depend on various factors. We have focused our work on gastrointestinal (GI) endoscopy because it is a cornerstone for diagnosis and treatment of diseases of the GI tract. About 2.8 million luminal GI (esophageal, stomach, colorectal) cancers are detected globally every year, and although substantial technical improvements in endoscopes have been made over the last 10-15 years, a major limitation of endoscopic examinations remains operator variation. This translates into a substantial inter-observer variation in the detection and assessment of mucosal lesions, causing among other things an average polyp miss-rate of 20% in the colon and thus the subsequent development of a number of post-colonoscopy colorectal cancers. CAD systems might eliminate this variation and lead to more accurate diagnoses. In this editorial, we point out some of the current challenges in the development of efficient computer-based digital assistants. We give examples of proposed tools using various techniques, identify current challenges, and give suggestions for the development and assessment of future CAD systems.

The predictive value of computed tomography in the detection of reflux esophagitis in patients undergoing upper endoscopy

BACKGROUND

Reflux esophagitis (RE) may mimic symptoms requiring cross-sectional imaging.

METHODS

From 565 patients who had CT and esophagogastroduodenoscopy within four days apart, CT scans of 72 patients with RE confirmed by esophagogastroduodenoscopy and 108 matched patients without RE were evaluated for distal esophageal wall characteristics.

RESULTS

In RE patients the distal esophageal wall thickness was greater (5.2±2.0mm) compared to patients without RE (3.5±1.2mm, p<0.0001) with AUC of 0.78 and 56% sensitivity, 88% specificity for a 5.0mm cut-off.

CONCLUSIONS

There is a moderate association between distal esophageal wall thickness on CT and RE diagnosed by esophagogastroduodenoscopy as the reference standard.

25 Years of Proton Pump Inhibitors: A Comprehensive Review

Proton pump inhibitors (PPIs) were clinically introduced more than 25 years ago and have since proven to be invaluable, safe, and effective agents for the management of a variety of acid-related disorders. Although all members in this class act in a similar fashion, inhibiting active parietal cell acid secretion, there are slight differences among PPIs relating to their pharmacokinetic properties, metabolism, and Food and Drug Administration (FDA)-approved clinical indications. Nevertheless, each is effective in managing gastroesophageal reflux disease and uncomplicated or complicated peptic ulcer disease. Despite their overall efficacy, PPIs do have some limitations related to their short plasma half-lives and requirement for meal-associated dosing, which can lead to breakthrough symptoms in some individuals, especially at night. Longer-acting PPIs and technology to prolong conventional PPI activity have been developed to specifically address these limitations and may improve clinical outcomes.

Gastroesophageal reflux disease and obesity: do we need to perform reflux testing in all candidates to bariatric surgery?

INTRODUCTION

Obesity is a strong independent risk factor of gastroesophageal reflux disease (GERD) symptoms and esophageal erosions. However the relationship between obesity and GERD is still a subject of debate. In fact, if in most cases bariatric surgery can diminish reflux by losing a large amount of fat, on the other hand some restrictive procedure can worsen or cause the presence of GERD. Thus, it is unclear if patients candidate to bariatric surgery have to perform pre-operative reflux testing or not.

AIM

of the study was to verify the presence of GERD patterns in patients candidate to surgery and the need of pre-operative reflux testing.

METHODS

All patients underwent to a standardized questionnaire for symptoms severity (GERQ), upper endoscopy, high resolution manometry (HRiM) and impedance pH-monitoring (MII-pH). Patients were stratified into: group 1 (negative for both GERQ and endoscopy), group 2 (positive for GERQ and negative for endoscopy), group 3 (positive for both GERQ and endoscopy). A healthy-volunteers group (HV) was assessed.

RESULTS

One hundred thirty-nine subjects (obese, 124; HV normal weight, 15) were studied. Group 1 showed comparable mean LES pressure, peristaltic function, bolus transport and presence of hiatal hernia than HV. Group 2 showed a reduction of these parameters, while group 3 showed a statistical significant reduction in LES pressure, peristaltic function, bolus transport and increase in presence of hiatal hernia. At MII-pH, Group 1 showed a not significant increase in reflux patterns; group 2 and 3 showed a significant increase in esophageal acid exposure and in number of refluxes (both acid and weakly acid), with group 3 showing the higher grade of reflux pattern.

CONCLUSIONS

Obese subjects with pre-operative presence of GERD symptoms and endoscopical signs could be tested with HRM and MII-pH before undergoing bariatric surgery, especially for restrictive procedures. On the other hand, obese patients without any sign of GERD could not be tested for reflux, showing similar patterns to HV.