{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T20:45:29Z","timestamp":1793306729913,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ewha Womens University Research","award":["2021"],"award-info":[{"award-number":["2021"]}]},{"name":"Ewha Womens University Research","award":["20181090"],"award-info":[{"award-number":["20181090"]}]},{"name":"Ministry of Education and National Research Foundation of Korea and Soonchunhyang University","award":["2021"],"award-info":[{"award-number":["2021"]}]},{"name":"Ministry of Education and National Research Foundation of Korea and Soonchunhyang University","award":["20181090"],"award-info":[{"award-number":["20181090"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>To establish a diagnostic algorithm for predicting complicated appendicitis in children based on CT and clinical features. Methods: This retrospective study included 315 children (&lt;18 years old) who were diagnosed with acute appendicitis and underwent appendectomy between January 2014 and December 2018. A decision tree algorithm was used to identify important features associated with the condition and to develop a diagnostic algorithm for predicting complicated appendicitis, including CT and clinical findings in the development cohort (n = 198). Complicated appendicitis was defined as gangrenous or perforated appendicitis. The diagnostic algorithm was validated using a temporal cohort (n = 117). The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) from the receiver operating characteristic curve analysis were calculated to evaluate the diagnostic performance of the algorithm. Results: All patients with periappendiceal abscesses, periappendiceal inflammatory masses, and free air on CT were diagnosed with complicated appendicitis. In addition, intraluminal air, transverse diameter of the appendix, and ascites were identified as important CT findings for predicting complicated appendicitis. C-reactive protein (CRP) level, white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and body temperature also showed important associations with complicated appendicitis. The AUC, sensitivity, and specificity of the diagnostic algorithm comprising features were 0.91 (95% CI, 0.86\u20130.95), 91.8% (84.5\u201396.4), and 90.0% (82.4\u201395.1) in the development cohort, and 0.7 (0.63\u20130.84), 85.9% (75.0\u201393.4), and 58.5% (44.1\u201371.9) in test cohort, respectively. Conclusion: We propose a diagnostic algorithm based on a decision tree model using CT and clinical findings. This algorithm can be used to differentiate between complicated and noncomplicated appendicitis and to provide an appropriate treatment plan for children with acute appendicitis.<\/jats:p>","DOI":"10.3390\/diagnostics13050923","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T03:03:48Z","timestamp":1677639828000},"page":"923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Diagnostic Algorithm Based on Machine Learning to Predict Complicated Appendicitis in Children Using CT, Laboratory, and Clinical Features"],"prefix":"10.3390","volume":"13","author":[{"given":"Jieun","family":"Byun","sequence":"first","affiliation":[{"name":"Department of Radiology, College of Medicine, Ewha Womans University, Seoul 07804, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seongkeun","family":"Park","sequence":"additional","affiliation":[{"name":"Machine Intelligence Laboratory, Department of Smart Automobile, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sook Min","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul 07441, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s00261-014-0117-x","article-title":"Differentiation of early perforated from nonperforated appendicitis: MDCT findings, MDCT diagnostic performance, and clinical outcome","volume":"39","author":"Kim","year":"2014","journal-title":"Abdom. 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