CD or not CD, that is the question - a digital interobserver agreement study in coeliac disease

OBJECTIVE: Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb) data improves the inter-observer agreement of diagnosis.DESIGN: We undertook a large study of the concordance in histological examination of duodenal biopsies using digitised WSIs in an entirely virtual reporting setting. Our study was organised in two phases: in phase one, 13 pathologists independently classified 100 duodenal biopsies (40 normal; 40 CD; 20 indeterminate enteropathy) in the absence of any clinical or laboratory data. In phase two, the same pathologists examined the (re-anonymised) WSIs with the inclusion of IgA tTG and Hb data.RESULTS: We found the mean probability of two observers agreeing in the absence of additional data to be 0.73 (±0.08) with a corresponding Cohen's kappa of 0.59 (±0.11). We further showed that the inclusion of additional data increased the concordance to 0.80 (±0.06) with a Cohen's kappa coefficient of 0.67 (±0.09).CONCLUSION: We showed that the addition of serological data significantly improves the quality of CD diagnosis. However, the limited inter-observer agreement in CD diagnosis using digitised WSIs, even after the inclusion of IgA tTG and Hb data, indicates the important of interpreting duodenal biopsy in the appropriate clinical context. It further highlights the unmet need for an objective means of reproducible duodenal biopsy diagnosis, such as the automated analysis of WSIs using AI.<br/

Diagnosis of celiac disease and environmental enteropathy on biopsy images using color balancing on convolutional neural networks

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification