Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples

We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database. All cases were assessed by a specialized gastrointestinal (GI) pathologist for features in the EoEHSS at the time of original diagnosis and rescored by a central GI pathologist (R.K.M.). We subsequently analyzed whole-slide image digital slides using a supervised AI model operating in a cloud-based, deep learning AI platform (Aiforia Technologies) for peak eosinophil count (PEC) and several histopathologic features in the EoEHSS. The correlation and interobserver agreement between the AI model and pathologists (Pearson correlation coefficient [rs] = 0.89 and intraclass correlation coefficient [ICC] = 0.87 vs original pathologist; rs = 0.91 and ICC = 0.83 vs central pathologist) were similar to the correlation and interobserver agreement between pathologists for PEC (rs = 0.88 and ICC = 0.91) and broadly similar to those for most other histologic features in the EoEHSS. The AI model also accurately identified PEC of >15 eosinophils/high-power field by the original pathologist (area under the curve [AUC] = 0.98) and central pathologist (AUC = 0.98) and had similar AUCs for the presence of EoE-related endoscopic features to pathologists' assessment. Average eosinophils per epithelial unit area had similar performance compared to AI high-power field-based analysis. Our newly developed AI model can accurately identify, quantify, and score several of the main histopathologic features in the EoE spectrum, with agreement regarding EoEHSS scoring which was similar to that seen among GI pathologists.

Development of the human pancreas and its exocrine function

The pancreas has both endocrine and exocrine function and plays an important role in digestion and glucose control. Understanding the development of the pancreas, grossly and microscopically, and the genetic factors regulating it provides further insight into clinical problems that arise when these processes fail. Animal models of development are known to have inherent issues when understanding human development. Therefore, in this review, we focus on human studies that have reported gross and microscopic development including acinar-, ductal-, and endocrine cells and the neural network. We review the genes and transcription factors involved in organ formation using data from animal models to bridge current understanding where necessary. We describe the development of exocrine function in the fetus and postnatally. A deeper review of the genes involved in pancreatic formation allows us to describe the development of the different groups (proteases, lipids, and amylase) of enzymes during fetal life and postnatally and describe the genetic defects. We discuss the constellation of gross anatomical, as well as microscopic defects that with genetic mutations lead to pancreatic insufficiency and disease states.

Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis

Background

In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool.

Methods

A total of 10 726 objects and 56.2 mm² of semantic segmentation areas were annotated on whole-slide images, utilizing a cloud-based, deep learning artificial intelligence platform (Aiforia Technologies, Helsinki, Finland). Our training set consisted of 40 carefully selected digitized esophageal biopsy slides which contained the full spectrum of changes typically seen in the setting of esophageal eosinophilia, ranging from normal mucosa to severe abnormalities with regard to each specific features included in our model. A subset of cases was reserved as independent “test sets” in order to assess the validity of the AI model outside the training set. Five specialized experienced gastrointestinal pathologists scored each feature blindly and independently of each other and of AI model results.

Results

The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5–94.8 for AI vs human and 92.6–96.0 for human vs human). Segmentation area features were rated for accuracy using the following scale: 1. “perfect or nearly perfect” (95%–100%, no significant errors), 2. “very good” (80%–95%, only minor errors), 3. “good” (70%–80%, significant errors but still captures the feature well), 4. “insufficient” (less than 70%, significant errors compromising feature recognition). Rating scores for tissue (1.01), spongiosis (1.15), basal layer (1.05), surface layer (1.04), lamina propria (1.15), and collagen (1.11) were in the “very good” to “perfect or nearly perfect” range, while degranulation (2.23) was rated between “good” and “very good”.

Conclusion

Our newly developed AI-based tool showed an excellent performance (non-inferior to a group of experienced GI pathologists) for the recognition of various histologic features in the EoE/esophageal mucosal eosinophilia spectrum. This tool represents an important step in creating an accurate and reproducible method for semi-automated quantitative analysis to be used in the evaluation of esophageal biopsies in this clinical context.