Gastroesophageal Reflux Disease Diagnosis Using Hierarchical Heterogeneous Descriptor Fusion Support Vector Machine

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.

Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis

Goal: Diagnosing the corpus-predominant gastritis index (CGI) which is an early precancerous lesion in the stomach has been shown its effectiveness in identifying high gastric cancer risk patients for preventive healthcare. However, invasive biopsies and time-consuming pathological analysis are required for the CGI diagnosis. Methods: We propose a novel gastric section correlation network (GSCNet) for the CGI diagnosis from endoscopic images of three dominant gastric sections, the antrum, body and cardia. The proposed network consists of two dominant modules including the scaling feature fusion module and section correlation module. The front one aims to extract scaling fusion features which can effectively represent the mucosa under variant viewing angles and scale changes for each gastric section. The latter one aims to apply the medical prior knowledge with three section correlation losses to model the correlations of different gastric sections for the CGI diagnosis. Results: The proposed method outperforms competing deep learning methods and achieves high testing accuracy, sensitivity, and specificity of 0.957, 0.938 and 0.962, respectively. Conclusions: The proposed method is the first method to identify high gastric cancer risk patients with CGI from endoscopic images without invasive biopsies and time-consuming pathological analysis.

Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System

OBJECTIVES

Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI).

MATERIALS AND METHODS

A total of 2081 endoscopic images were used for the development of a DL model, and the classification accuracy of the models and endoscopists with different levels of experience was compared.

RESULTS

Some mainstream DL models were utilized, of which DenseNet-121 outperformed. The area under the curve (AUC) of the DenseNet-121 was 0.968, and its classification accuracy (86.7%) was significantly higher than that of junior (71.5%) and experienced (77.4%) endoscopists. An XAI evaluation was also performed to explore the perception consistency between the DL model and endoscopists, which showed meaningful results for real-world applications.

CONCLUSIONS

The DL model showed a potential in improving the accuracy of endoscopists in LA-grading of GERD, and it has noticeable clinical application prospects and is worthy of further promotion.

An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning

Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured.

Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis

Background

Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease.

Methods

We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360).

Findings

We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias.

Interpretation

The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].

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.

Deep Ensemble Feature Network for Gastric Section Classification

In this paper, we propose a novel deep ensemble feature (DEF) network to classify gastric sections from endoscopic images. Different from recent deep ensemble learning methods, which need to train deep features and classifiers individually to obtain fused classification results, the proposed method can simultaneously learn the deep ensemble feature from arbitrary number of convolutional neural networks (CNNs) and the decision classifier in an end-to-end trainable manner. It comprises two sub networks, the ensemble feature network and the decision network. The former sub network learns the deep ensemble feature from multiple CNNs to represent endoscopic images. The latter sub network learns to obtain the classification labels by using the deep ensemble feature. Both sub networks are optimized based on the proposed ensemble feature loss and the decision loss which guide the learning of deep features and decisions. As shown in the experimental results, the proposed method outperforms the state-of-the-art deep learning, ensemble learning, and deep ensemble learning methods.

Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries

BACKGROUND AND OBJECTIVE

Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms.

METHODS

The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis.

RESULTS

This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before.

CONCLUSION

This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.