Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease

Ultrastructural changes in chronic inflammatory enteropathies-a comparison between dogs and humans

Chronic inflammatory enteropathies (CIEs) are an important group of diseases in dogs and involve complex pathogenetic aspects. Endoscopy and histopathology are vital for documenting the disease but are less useful for subclassifying CIEs and predicting the response to treatment. However, healing of the mucosal disease process (deep remission) and ultrastructural evaluation of the mucosa have received little attention in canine CIE. Given that canine CIE shares many similarities with inflammatory bowel diseases (IBDs) in human patients-and presents a good spontaneous disease model for human IBD-this perspective article evaluates the literature on ultrastructural lesions in canine CIE and human IBD and offers future directions for the study of ultrastructural mucosal lesions in canine CIE. Such lesions might have a higher sensitivity of detection than structural changes revealed upon light microscopy and may even precede or remain after the resolution of the clinical signs and histologic lesions.

Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.

Cross-scale multi-instance learning for pathological image diagnosis

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence

BACKGROUND

Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists.

METHODS AND FINDINGS

Our whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7-10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17-30 times the number of potential human errors were detected within an average of 1.2 days.

CONCLUSIONS

The AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Inflammatory Bowel Disease Management during the COVID-19 Pandemic: A Literature Review

BACKGROUND: Coronavirus disease 2019 (COVID-19) caused a global pandemic. Since its start, widespread safety measures have been adopted by nations worldwide. Crohn's disease (CD) and ulcerative colitis are two forms of inflammatory bowel disease (IBD). IBD is a common inflammatory illness with a high worldwide incidence. Its clinical symptoms include stomach discomfort, diarrhea, anorexia, and weight loss. Genetics, microbes, cigarette smoking, appendectomy, lack of personal hygiene, using anti-inflammatory agents, vitamin D deficiency, and stress are the main risk factors for IBD. COVID-19 pandemic raised concerns about the exacerbation of COVID clinical manifestations in patients with IBD and increasing the risk of mortality. During COVID-19 pandemic, intestinal inflammation, and promoting adherence need to be controlled using medications and vaccinations as a primary goal. In this review, we reviewed unique concerns about IBD risk in the population as well as management of the disease, and the effectiveness of vaccination during COVID-19 pandemic.

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

Background: At present, radical total mesorectal excision after neoadjuvant chemoradiotherapy is crucial for locally advanced rectal cancer. Therefore, the use of histopathological images analysis technology to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer is of great significance for the subsequent treatment of patients. Methods: In this study, we propose a new pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Specifically, we proposed a gated attention normalization mechanism based on the multilayer perceptron, which accelerates the convergence of stochastic gradient descent optimization and can speed up the training process. We also proposed a bilinear attention multi-scale feature fusion mechanism, which organically fuses the global features of the larger receptive fields and the detailed features of the smaller receptive fields and alleviates the problem of pathological images context information loss caused by block sampling. At the same time, we also designed a weighted loss function to alleviate the problem of imbalance between cancerous instances and normal instances. Results: We evaluated our method on a locally advanced rectal cancer dataset containing 150 whole slide images. In addition, to verify our method’s generalization performance, we also tested on two publicly available datasets, Camelyon16 and MSKCC. The results show that the AUC values of our method on the Camelyon16 and MSKCC datasets reach 0.9337 and 0.9091, respectively. Conclusion: Our method has outstanding performance and advantages in predicting the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Clinical and Translational Impact Statement—This study aims to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer to assist clinicians quickly diagnose and formulate personalized treatment plans for patients.

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

Molecular Imaging of Inflammatory Disease

Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.

Defining the Path Forward for Biomarkers to Address Unmet Needs in Inflammatory Bowel Diseases

Despite major advances in the inflammatory bowel diseases field, biomarkers to enable personalized and effective management are inadequate. Disease course and treatment response are highly variable, with some patients experiencing mild disease progression, whereas other patients experience severe or complicated disease. Periodic endoscopy is performed to assess disease activity; as a result, it takes months to ascertain whether a treatment is having a positive impact on disease progression. Minimally invasive biomarkers for prognosis of disease course, prediction of treatment response, monitoring of disease activity, and accurate diagnosis based on improved disease phenotyping and classification could improve outcomes and accelerate the development of novel therapeutics. Rapidly developing technologies have great potential in this regard; however, the discovery, validation, and qualification of biomarkers will require partnerships including academia, industry, funders, and regulators. The Crohn's & Colitis Foundation launched the IBD Biomarker Summit to bring together key stakeholders to identify and prioritize critical unmet needs; prioritize promising technologies and consortium approaches to address these needs; and propose harmonization approaches to improve comparability of data across studies. Here, we summarize the outcomes of the 2018 and 2019 meetings, including consensus-based unmet needs in the clinical and drug development context. We highlight ongoing consortium efforts and promising technologies with the potential to address these needs in the near term. Finally, we summarize actionable recommendations for harmonization, including data collection tools for improved consistency in disease phenotyping; standardization of informed consenting; and development of guidelines for sample management and assay validation. Taken together, these outcomes demonstrate that there is an exceptional alignment of priorities across stakeholders for a coordinated effort to address unmet needs of patients with inflammatory bowel diseases through biomarker science.