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.

(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).

(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).

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.