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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\u2009&lt;\u200920, 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\u2009\u00d7\u2009512 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\u2009\u2265\u20093 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\u2019s recall were analyzed using binary logistic regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u20134 was\n                      <jats:italic>\u03ba<\/jats:italic>\n                      \u2009=\u20090.76. The logistic regression analysis showed that only intra-abdominal fat had a significant impact on the model\u2019s precision (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.02).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed a model capable of detecting appendicitis on CT with a three-dimensional bounding box.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-025-01834-1","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T04:03:50Z","timestamp":1752638630000},"page":"1859-1869","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning for appendicitis: development of a three-dimensional localization model on CT"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-5268","authenticated-orcid":false,"given":"Taku","family":"Takaishi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatsuya","family":"Kawai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshimasa","family":"Kokubo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takumi","family":"Fujinaga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinao","family":"Ojio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatsuhito","family":"Yamamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kana","family":"Hayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusei","family":"Owatari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirotaka","family":"Ito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akio","family":"Hiwatashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"issue":"5","key":"1834_CR1","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1093\/oxfordjournals.aje.a115734","volume":"132","author":"DG Addiss","year":"1990","unstructured":"Addiss DG, Shaffer N, Fowler BS, Tauxe RV. 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