Improved Characterization of Soybean Meal Induced Enteritis Using Machine Learning Automation and Standardization to Score Distal Intestinal Histology Slides
Animal healthAnimal nutritionAquaculture
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
Jacob Bledsoe, University of Idaho
Co-Principal Investigators:
Project Code:
Contributing Organization (Checkoff):
Institution Funded:
Brief Project Summary:
This study aims to develop a machine-learning model to quantify distal intestine enteritis in histology slides, addressing the high cost, limited availability, and inconsistent grading of current human-based approaches in order to reduce hurtles in aquaculture nutrition research aimed at increasing soy inclusion.
Key Beneficiaries:
#aquaculture farmers, #aquaculture nutritionist, #aquaculture researchers, #soybean meal processors
Unique Keywords:
#enteritis, #histology, #machine learning
Information And Results
Project Summary

Enteritis scoring on distal intestine slides are critical measures for industry study reporting on the effects of alternative feed formulation and dietary ingredients. Limited progress has been made in formalizing these measurements through grading rubrics and rules of thumb (Uran 2008). Current scoring relies on manual grading by a professional histopathologist against a prescribed ordinal scale on several anatomical metrics. Due to the slow, expensive, and inconsistent nature of this human-based approach, there is still significant room for improvement in the cost, accessibility, and robustness of enteritis grading. This proposal aims to train a machine-learning model to rapidly, accessibly, cheaply, and robustly quantify distal enteritis in histology slide images in a bias-free and reproducible manner (Guan, 2022). This technology may be applied to every future project involving distal enteritis evaluation. This project addresses priority 2.1 of the SAA RFP: Understanding gastrointestinal barriers to soy inclusion: specifically, enteritis. We propose that a successfully trained model may achieve enteritis grading at or above the level of a board-certified histopathologist, on the order of milliseconds, at zero cost, in a freely available format which is usable by anyone with access to a gpu-enabled laptop.

Project Objectives

(1) Crowdsource 800-1000 commercial salmonid (Atlantic salmon and rainbow trout; approx. equal proportions) distal intestinal slides from multiple industry and research partner laboratories, ranging along the continuum of clinical enteritis from negative (zero inflammation) controls to severe distal enteritis samples, including representatives from all intermediate stages. Efforts will be made to include divergent sources, including domestic and international collaborators (i.e., USA, Norway, Chile, etc.), to improve the variability within the initial training data. Training data variability is expected to increase the robustness and generalizability of the model.
(2) Digitize the obtained slides using a professional-grade slide scanner and assign binary classifications and Uran scores to each digitized slide utilizing two to three independent histopathologists.
(3) Train the ResNet AI model as a binary classifier as proof of concept on these digitized images, to judge each slide as a binary enteritis-positive or enteritis-negative.
(4) Train the model to assign each slide an ordinal grade on each standard anatomical metric, according to the Uran Scale (He, 2016; McCombe, 2021).

Project Deliverables

(1) A freely accessible, easily transferable lightweight machine-learning program known as a ‘ResNet’ that can grade distal intestine slide images on the Uran Scale and return data on the order of milliseconds per slide at zero cost.
(2) A tool to drastically reduce the cost and turn-around time of enteritis data for every scientific investigation into soy-based diets.
(3) Demonstrate the economic value of machine-learning and artificial intelligence-assisted aquaculture tools to farmers, stakeholders, and economic partners (Sveen 2021; Vo 2021).

Progress Of Work

Final Project Results

Benefit To Soybean Farmers

Wet lab tests and farm trials that test effects of any parameter (e.g., breeding programs, supplements, probiotics, antinutritional factors) on distal enteritis are critical for increasing levels of soy protein replacement in fish feed (Booman, 2018). Professional grading of slides costs tens of thousands of dollars and may vary in quality and time requirements. This approach makes the grading process instantaneous, reliable above 95%, and cost-free, removing a hurdle to increase soy utilization in aquaculture.

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.