Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications

The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.

Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of Helicobacter pylori Infection

The Gram-negative bacterium Helicobacter pylori (H. infection) infects the human stomach and is a major cause of gastritis, peptic ulcers, and gastric cancer. With over 50% of the global population affected, early and accurate diagnosis of H. infection infection is crucial for effective treatment and prevention of severe complications. Traditional diagnostic methods, such as endoscopy with biopsy, serology, urea breath tests, and stool antigen tests, are often invasive, costly, and can lack precision. Recent advancements in machine learning (ML) and quantum machine learning (QML) offer promising non-invasive alternatives capable of analyzing complex datasets to identify patterns not easily discernible by human analysis. This research aims to develop and evaluate HeliEns, a novel quantum hybrid ensemble learning algorithm designed for the early and accurate diagnosis of H. infection infection. HeliEns combines the strengths of multiple quantum machine learning models, specifically Quantum K-Nearest Neighbors (QKNN), Quantum Naive Bayes (QNB), and Quantum Logistic Regression (QLR), to enhance diagnostic accuracy and reliability. The development of HeliEns involved rigorous data preprocessing steps, including data cleaning, encoding of categorical variables, and feature scaling, to ensure the dataset’s suitability for quantum machine learning algorithms. Individual models (QKNN, QNB, and QLR) were trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model was then constructed by integrating these quantum models using a hybrid approach that leverages their diverse strengths. The HeliEns model demonstrated superior performance compared to individual models, achieving an accuracy of 94%, precision of 97%, recall of 92%, and an F1-score of 94% in detecting H. infection infection. The quantum ensemble approach effectively mitigated the limitations of individual models, providing a robust and reliable diagnostic tool. HeliEns significantly improved diagnostic accuracy and reliability for early H. infection detection. The integration of multiple quantum ML algorithms within the HeliEns framework enhanced overall model performance. The non-invasive nature of the HeliEns model offers a cost-effective and user-friendly alternative to traditional diagnostic methods. This research underscores the transformative potential of quantum machine learning in healthcare, particularly in enhancing diagnostic efficiency and patient outcomes. HeliEns represents a significant advancement in the early diagnosis of H. infection infection, leveraging quantum machine learning to provide a non-invasive, accurate, and reliable diagnostic tool. This research highlights the importance of QML-driven solutions in healthcare and sets the stage for future research to further refine and validate the HeliEns model in real-world clinical settings.

Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches

Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant'Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach.

Increasing the accuracy of colorectal cancer screening

INTRODUCTION

Colorectal cancer (CRC) is a major health issue, being responsible for nearly 10% of all cancer-related deaths. Since CRC is often an asymptomatic or paucisymptomatic disease until it reaches advanced stages, screening is crucial for the diagnosis of preneoplastic lesions or early CRC.

AREAS COVERED

The aim of this review is to summarize the literature evidence on currently available CRC screening tools, with their pros and cons, focusing on the level of accuracy reached by each test over time. We also provide an overview of novel technologies and scientific advances that are currently being investigated and that in the future may represent real game-changers in the field of CRC screening.

EXPERT OPINION

We suggest that best screening modalities are annual or biennial FIT and colonoscopy every 10 years. We believe that the introduction of artificial intelligence (AI)-tools in the CRC screening field could lead to a significant improvement of the screening efficacy in reducing CRC incidence and mortality in the future. More resources should be put into implementing CRC programmes and support research project to further increase accuracy of CRC screening tests and strategies.

Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents

With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.

Application of Artificial Intelligence in Measuring Novel pH-Impedance Metrics for Optimal Diagnosis of GERD

Novel metrics extracted from pH-impedance monitoring can augment the diagnosis of gastroesophageal reflux disease (GERD). Artificial intelligence (AI) is being widely used to improve the diagnostic capabilities of various diseases. In this review, we update the current literature regarding applications of artificial intelligence in measuring novel pH-impedance metrics. AI demonstrates high performance in the measurement of impedance metrics, including numbers of reflux episodes and post-reflux swallow-induced peristaltic wave index and, furthermore, extracts baseline impedance from the entire pH-impedance study. AI is expected to play a reliable role in facilitating measuring novel impedance metrics in patients with GERD in the near future.

Feasibility of moxifloxacin and proflavine dual fluorescence imaging for detecting gastrointestinal neoplastic lesions: A prospective study

OBJECTIVES

High-contrast and high-resolution imaging techniques would enable real-time sensitive detection of the gastrointestinal lesions. This study aimed to investigate the feasibility of novel dual fluorescence imaging using moxifloxacin and proflavine in the detection of neoplastic lesions of the human gastrointestinal tract.

METHODS

Patients with the colonic and gastric neoplastic lesions were prospectively enrolled. The lesions were biopsied with forceps or endoscopically resected. Dual fluorescence imaging was performed by using custom axially swept wide-field fluorescence microscopy after topical moxifloxacin and proflavine instillation. Imaging results were compared with both confocal imaging with cell labeling and conventional histological examination.

RESULTS

Ten colonic samples (one normal mucosa, nine adenomas) from eight patients and six gastric samples (one normal mucosa, five adenomas) from four patients were evaluated. Dual fluorescence imaging visualized detail cellular structures. Regular glandular structures with polarized cell arrangement were observed in normal mucosa. Goblet cells were preserved in normal colonic mucosa. Irregular glandular structures with scanty cytoplasm and dispersed elongated nuclei were observed in adenomas. Goblet cells were scarce or lost in the colonic lesions. Similarity analysis between moxifloxacin and proflavine imaging showed relatively high correlation values in adenoma compared with those in normal mucosa. Dual fluorescence imaging showed good detection accuracies of 82.3% and 86.0% in the colonic and the gastric lesions, respectively.

CONCLUSIONS

High-contrast and high-resolution dual fluorescence imaging was feasible for obtaining detail histopathological information in the gastrointestinal neoplastic lesions. Further studies are needed to develop dual fluorescence imaging as an in vivo real-time visual diagnostic method.

H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner

Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.

Systematic review and meta-analysis: Artificial intelligence for the diagnosis of gastric precancerous lesions and Helicobacter pylori infection

BACKGROUND

The endoscopic diagnosis of Helicobacter-pylori(H.pylori) infection and gastric precancerous lesions(GPL), namely atrophic-gastritis and intestinal-metaplasia, still remains challenging. Artificial intelligence(AI) may represent a powerful resource for the endoscopic recognition of these conditions.

AIMS

To explore the diagnostic performance(DP) of AI in the diagnosis of GPL and H.pylori infection.

METHODS

A systematic-review was performed by two independent authors up to September 2021. Inclusion criteria were studies focusing on the DP of AI-system in the diagnosis of GPL and H.pylori infection. The pooled accuracy of studies included was reported.

RESULTS

Overall, 128 studies were found (PubMed-Embase-Cochrane Library) and four and nine studies were finally included regarding GPL and H.pylori infection, respectively. The pooled-accuracy(random effects model) was 90.3%(95%CI 84.3-94.9) and 79.6%(95%CI 66.7-90.0) with a significant heterogeneity[I²=90.4%(95%CI 78.5-95.7);I²=97.9%(97.2-98.6)] for GPL and H.pylori infection, respectively. The Begg's-test showed a significant publication-bias(p = 0.0371) only among studies regarding H.pylori infection. The pooled-accuracy(random-effects-model) was similar considering only studies using CNN-model for the diagnosis of H.pylori infection: 74.1%[(95%CI 51.6-91.3);I²=98.9%(95%CI 98.5-99.3)], Begg's-test(p = 0.1416) did not show publication-bias.

CONCLUSION

AI-system seems to be a good resource for an easier diagnosis of GPL and H.pylori infection, showing a pooled-diagnostic-accuracy of 90% and 80%, respectively. However, considering the high heterogeneity, these promising data need an external validation by randomized control trials and prospective real-time studies.

Efficient Synchronous Real-Time CADe for Multicategory Lesions in Gastroscopy by Using Multiclass Detection Model

Often more than one category of lesions in patients’ gastrointestinal tracts need to be found in the endoscopic examination. Therefore, there is a need to establish an efficient synchronous real-time computer-aided detection (CADe) system for multicategory lesion detection. This paper proposes to build a system with a multiclass detection model based on the YOLOv5 to detect multicategory lesions synchronously in real-time. Two joint detection CADe systems using multiple single-class detection models with the same structure in parallel or series are established for comparison. A retrospective dataset containing 31117 images from 3747 patients is used in this study. To train the model, various online data augmentation methods and multiple loss functions are used. The proposed CADe system can synchronously detect cancers, gastrointestinal stromal tumours, polyps, and ulcers from different quality input images with 98% precision, 89% recall, and 90.2% mAP. The detection speed is 47 frames per second with a 0.04 s latency on a PC workstation. Compared to the two joint detection CADe systems, the proposed system is more accurate with faster speed and lower latency. Two extra experiments indicated that the lesion detection model based on YOLOv5x could provide better performance than other common YOLO structures and that different accuracy metrics and lesion categories have different requirements for the number of training images. The proposed synchronous real-time CADe system with the multiclass detection model can detect multicategory lesions with high accuracy and speed and low latency on limited hardware. It expands the clinical application of CADe in endoscopy and uses expensive labelled medical images more efficiently than multiple single-category lesion models for joint detection.

Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease

The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.

Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice

Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.

Pernicious Anemia: The Hematological Presentation of a Multifaceted Disorder Caused by Cobalamin Deficiency

Pernicious anemia is still a neglected disorder in many medical contexts and is underdiagnosed in many patients. Pernicious anemia is linked to but different from autoimmune gastritis. Pernicious anemia occurs in a later stage of autoimmune atrophic gastritis when gastric intrinsic factor deficiency and consequent vitamin B₁₂ deficiency may occur. The multifaceted nature of pernicious anemia is related to the important role of cobalamin, which, when deficient, may lead to several dysfunctions, and thus, the proteiform clinical presentations of pernicious anemia. Indeed, pernicious anemia may lead to potentially serious long-term complications related to micronutrient deficiencies and their consequences and the development of gastric cancer and type 1 gastric neuroendocrine tumors. When not recognized in a timely manner or when pernicious anemia is diagnosed with delay, these complications may be potentially life-threatening and sometimes irreversible. The current review aimed to focus on epidemiology, pathogenesis, and clinical presentations of pernicious anemia in an attempt to look beyond borders of medical specialties. It aimed to focus on micronutrient deficiencies besides the well-known vitamin B₁₂ deficiency, the diagnostic approach for pernicious anemia, its long-term complications and optimal clinical management, and endoscopic surveillance of patients with pernicious anemia.

Artificial intelligence in colorectal cancer screening in patients with inflammatory bowel disease

Artificial intelligence (AI) is a branch of computer science that develops intelligent machines. In recent years, medicine has been contemplated with this recent modality to aid in the diagnosis of diseases in several specialties, including gastroenterology and gastrointestinal endoscopy. This new technology has superior ability to perform tasks mimicking human behavior and can identify possible pathological alterations, such as pre-malignant lesions and dysplasia, precursor lesions of colorectal cancer (CRC), and support medical decision-making. CRC is among the three most prevalent cancer types, and the second most common cause of cancer-related deaths worldwide; in addition, it is a leading cause of death in patients with inflammatory bowel disease (IBD). Patients with IBD tend to have greater inflammatory cell activity in the intestinal mucosa, which can favor cell proliferation and CRC development. AI can contribute to the detection of pre-neoplastic lesions in patients at risk of CRC development, such as those with extensive IBD or when additional CRC risk factors, such as smoking, are present. In fact, AI systems could improve all aspects of care related to both the detection of pre-malignant and malignant lesions and the screening of patients with IBD. In this review, we aimed to show the benefits and innovations of AI in the screening of CRC in patients with IBD. The promising applications of AI have the potential to revolutionize clinical practice and gastrointestinal endoscopy, especially in at-risk patients, such as those with IBD.