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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The purpose of this study is to develop and retrospectively validate a deep learning system for the classification and anatomically interpretable localization of six acute abdominal emergencies on CT using multi-window Hounsfield Unit (HU) encoding. A publicly available national teleradiology dataset of 1274 patients (42,922 bounding box annotations) was used for training and internal validation (896\/189\/189 patient-level split). Each CT slice was encoded into three diagnostic HU windows (soft tissue, bone\/stone, angio\/liver). A YOLOv11-Large model with a stride-4 (P2) head was trained at\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$1280\\times 1280$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>1280<\/mml:mn>\n                            <mml:mo>\u00d7<\/mml:mo>\n                            <mml:mn>1280<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    . Localization was evaluated using the clinical nine-region abdominal grid, and specificity was assessed in 80 target-negative patients. External validation used a radiologist-adjudicated 280-patient Stanford Merlin cohort (US), with model weights and thresholds applied without modification. Internal macro AUROC was 0.941, and macro F1 was 76.1%. Nine-region localization accuracy was 99.5% among detected cases and 90.9% including missed detections. Specificity in the target-negative cohort was 86.2%. On the external Stanford Merlin cohort, macro AUROC was 0.879 with all six classes\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\ge 0.80$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mo>\u2265<\/mml:mo>\n                            <mml:mn>0.80<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ; AAA reached F1\u00a00.889 at frozen thresholds; macro F1 was 0.545, increasing to 0.648 after recalibration. A multi-window CT detection model classified six acute abdominal emergencies with high discrimination on internal testing and preserved moderate-to-high discrimination on an external Stanford Merlin cohort. Region-level evaluation using the nine-region abdominal grid provided a clinically interpretable localization endpoint complementary to conventional detection metrics. However, variable class-level F1, reduced external operating-point performance, and retrospective design indicate that multisite prospective validation and site-specific threshold calibration are required before clinical deployment.\n                  <\/jats:p>","DOI":"10.1007\/s10278-026-02084-x","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T18:25:57Z","timestamp":1783535157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Anatomically Localized Detection of Six Acute Abdominal Emergencies on CT Using Multi-window Deep Learning: Development and Validation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1619-595X","authenticated-orcid":false,"given":"Hasan Mete","family":"Erdo\u011fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8189-2885","authenticated-orcid":false,"given":"Ural","family":"Ko\u00e7","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"issue":"1","key":"2084_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1159\/000371583","volume":"32","author":"SL Gans","year":"2015","unstructured":"Gans SL, Pols MA, Stoker J, Boermeester MA. 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