Purpose <p>To develop and evaluate a deep learning model for detecting appendicitis on abdominal CT.</p> Materials and methods <p>This retrospective single-center study included 567 CTs of appendicitis patients (330 males, age range 20–96) obtained between 2011 and 2020, randomly split into training (<i>n</i> = 517) and validation (<i>n</i> = 50) sets. The validation set was supplemented with 50 control CTs performed for acute abdomen. For a test dataset, 100 appendicitis CTs and 100 control CTs were consecutively collected from a separate period after 2021. Exclusion criteria included age &lt; 20, perforation, unclear appendix, and appendix tumors. Appendicitis CTs were annotated with three-dimensional bounding boxes that encompassed inflamed appendices. CT protocols were unenhanced, 5-mm slice-thickness, 512 × 512 pixel matrix. The deep learning algorithm was based on faster region convolutional neural network (Faster R-CNN). Two board-certified radiologists visually graded model predictions on the test dataset using a 5-point Likert scale (0: no detection, 1: false, 2: poor, 3: fair, 4: good), with scores ≥ 3 considered true positives. Inter-rater agreement was assessed using weighted kappa statistics. The effects of intra-abdominal fat, periappendiceal fat-stranding, presence of appendicolith, and appendix diameter on the model’s recall were analyzed using binary logistic regression.</p> Results <p>The model showed a precision of 0.66 (87/132), a recall of 0.87 (87/100), and a false-positive rate per patient of 0.23 (45/200). The inter-rater agreement for Likert scores of 2–4 was <i>κ</i> = 0.76. The logistic regression analysis showed that only intra-abdominal fat had a significant impact on the model’s precision (<i>p</i> = 0.02).</p> Conclusion <p>We developed a model capable of detecting appendicitis on CT with a three-dimensional bounding box.</p>