{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T02:08:50Z","timestamp":1792030130617,"version":"4.4.0"},"reference-count":52,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>\n                    Acute appendicitis is a common but diagnostically challenging surgical emergency in children. Existing linear scoring systems lack sufficient accuracy for standalone use, while advanced imaging is constrained by risks of sedation, contrast, and radiation. Furthermore, no available tools provide prognostic guidance. We introduce\n                    <jats:italic>Dharma<\/jats:italic>\n                    , a machine learning framework consisting of a clinically grounded imputer and two random forest classifiers for diagnosis and severity assessment. Designed for real-world bedside use, Dharma is open-sourced and accessible through a web application. Dharma achieved excellent diagnostic performance, with an AUC-ROC of 0.98 [0.97\u20130.99] and accuracy of 93% [91\u201395]. For prognostic classification, it identified complicated appendicitis with high sensitivity (96% [93\u201399]) and negative predictive value (97% [94\u201399]). Even in cases without appendix visualization\u2014a frequent limitation in resource-constrained settings\u2014Dharma maintained strong performance (AUC-ROC 0.96 [0.93\u20130.99]), with specificity of 97% [93\u2013100] and PPV of 93% [84\u2013100] at a 44% threshold, and sensitivity of 92% [84\u201398] with NPV of 95% [91\u201399] at a 25% threshold. These threshold-dependent trade-offs enable Dharma to support both ruling in and ruling out appendicitis within diverse clinical workflows. Beyond pediatric appendicitis, Dharma\u2019s open-source framework and clinically grounded design also provide a generalizable foundation for developing equitable and practical decision-support systems in healthcare.\n                  <\/jats:p>","DOI":"10.1371\/journal.pdig.0000908","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:37:16Z","timestamp":1769020636000},"page":"e0000908","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis\u2014Diagnosis, severity assessment and evidence-based clinical decision support"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7095-4161","authenticated-orcid":true,"given":"Anup","family":"Thapa Kshetri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4071-1581","authenticated-orcid":true,"given":"Subash","family":"Pahari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6669-7638","authenticated-orcid":true,"given":"Shashank","family":"Timilsina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9061-9307","authenticated-orcid":true,"given":"Binay","family":"Chapagain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"pdig.0000908.ref001","doi-asserted-by":"crossref","first-page":"83","DOI":"10.2147\/PHMT.S120156","article-title":"Managing acute abdominal pain in pediatric patients: current perspectives","volume":"8","author":"NM Hijaz","year":"2017","journal-title":"Pediatric Health Med Ther"},{"issue":"4","key":"pdig.0000908.ref002","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/S1875-9572(08)60027-3","article-title":"Acute abdomen in pediatric patients admitted to the pediatric emergency department","volume":"49","author":"Y-C Tseng","year":"2008","journal-title":"Pediatr Neonatol"},{"issue":"6","key":"pdig.0000908.ref003","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1016\/j.annemergmed.2006.12.016","article-title":"Evaluating appendicitis scoring systems using a prospective pediatric cohort","volume":"49","author":"C Schneider","year":"2007","journal-title":"Ann Emerg Med"},{"issue":"3","key":"pdig.0000908.ref004","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s00383-014-3467-0","article-title":"The evaluation of the validity of Alvarado, Eskelinen, Lintula and Ohmann scoring systems in diagnosing acute appendicitis in children","volume":"30","author":"A Sencan","year":"2014","journal-title":"Pediatr Surg Int"},{"issue":"3","key":"pdig.0000908.ref005","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1097\/PEC.0000000000000375","article-title":"Prospective validation of Alvarado score and pediatric appendicitis score for the diagnosis of acute appendicitis in children","volume":"31","author":"Z Pogoreli\u0107","year":"2015","journal-title":"Pediatr Emerg Care"},{"issue":"7","key":"pdig.0000908.ref006","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1097\/MLR.0b013e31819748e9","article-title":"Trends in the rates of radiography use and important diagnoses in emergency department patients with abdominal pain","volume":"47","author":"JM Pines","year":"2009","journal-title":"Med Care"},{"issue":"1","key":"pdig.0000908.ref007","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13052-017-0335-2","article-title":"Acute appendicitis in young children less than 5 years: review article","volume":"43","author":"HH Almaramhy","year":"2017","journal-title":"Ital J Pediatr"},{"issue":"5","key":"pdig.0000908.ref008","first-page":"887","article-title":"Trends and predictors of pediatric negative appendectomy rates: a single-centre retrospective study","volume":"10","author":"M Juki\u0107","year":"2023","journal-title":"Children (Basel)"},{"key":"pdig.0000908.ref009","first-page":"66","article-title":"Negative appendicectomy: evaluation of ultrasonography and Alvarado score","volume":"61","author":"I Kundiona","year":"2015","journal-title":"Cent Afr J Med"},{"issue":"1","key":"pdig.0000908.ref010","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jss.2011.04.046","article-title":"Pediatric negative appendectomy rate: trend, predictors, and differentials","volume":"173","author":"TA Oyetunji","year":"2012","journal-title":"J Surg Res"},{"issue":"6","key":"pdig.0000908.ref011","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1177\/10935266241255281","article-title":"The variable definition of \u201cNegative Appendicitis\u201d remains a surgical challenge","volume":"27","author":"H Patel","year":"2024","journal-title":"Pediatr Dev Pathol"},{"issue":"1","key":"pdig.0000908.ref012","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/medicina59010021","article-title":"Predictors of complicated appendicitis with evolution to appendicular peritonitis in pediatric patients","volume":"59","author":"L B\u0103l\u0103nescu","year":"2022","journal-title":"Medicina (Kaunas)"},{"issue":"7","key":"pdig.0000908.ref013","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1007\/s00383-021-04872-8","article-title":"Accuracy of ultrasonography for differentiating between simple and complex appendicitis in children","volume":"37","author":"DJ Nijssen","year":"2021","journal-title":"Pediatr Surg Int"},{"issue":"3","key":"pdig.0000908.ref014","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1089\/sur.2019.175","article-title":"Acute appendicitis in children younger than five years of age: diagnostic challenge for pediatric surgeons","volume":"21","author":"Z Pogoreli\u0107","year":"2020","journal-title":"Surg Infect (Larchmt)"},{"issue":"2","key":"pdig.0000908.ref015","doi-asserted-by":"crossref","first-page":"63","DOI":"10.24018\/ejeng.2023.8.2.3025","article-title":"Machine learning in pattern recognition","volume":"8","author":"C Singh","year":"2023","journal-title":"EJENG"},{"issue":"1","key":"pdig.0000908.ref016","doi-asserted-by":"crossref","first-page":"5725","DOI":"10.1038\/s41598-024-55577-6","article-title":"A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals","volume":"14","author":"ME Samadi","year":"2024","journal-title":"Sci Rep"},{"key":"pdig.0000908.ref017","unstructured":"Alonge M, Isreal O. Hybrid AI Models: Combining Machine Learning with Domain Knowledge. [Cited 2025 May 26]. https:\/\/www.researchgate.net\/publication\/392166442_Hybrid_AI_Models_Combining_Machine_Learning_with_Domain_Knowledge"},{"key":"pdig.0000908.ref018","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41746-020-0254-2","article-title":"Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency","volume":"3","author":"CM Cutillo","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pdig.0000908.ref019","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"P Rajpurkar","year":"2022","journal-title":"Nat Med"},{"issue":"6","key":"pdig.0000908.ref020","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/pr.2012.34","article-title":"Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department","volume":"71","author":"L Grigull","year":"2012","journal-title":"Pediatr Res"},{"key":"pdig.0000908.ref021","doi-asserted-by":"crossref","first-page":"662183","DOI":"10.3389\/fped.2021.662183","article-title":"Using machine learning to predict the diagnosis, management and severity of pediatric appendicitis","volume":"9","author":"R Marcinkevics","year":"2021","journal-title":"Front Pediatr"},{"issue":"2","key":"pdig.0000908.ref022","doi-asserted-by":"crossref","first-page":"38","DOI":"10.5604\/01.3001.0053.5994","article-title":"A different way to diagnosis acute appendicitis: machine learning","volume":"96","author":"AT Harmantepe","year":"2023","journal-title":"Pol Przegl Chir"},{"issue":"9","key":"pdig.0000908.ref023","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0222030","article-title":"Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach","volume":"14","author":"J Reismann","year":"2019","journal-title":"PLoS One"},{"issue":"5","key":"pdig.0000908.ref024","doi-asserted-by":"crossref","first-page":"923","DOI":"10.3390\/diagnostics13050923","article-title":"Diagnostic Algorithm Based on Machine Learning to Predict Complicated Appendicitis in Children Using CT, Laboratory, and Clinical Features","volume":"13","author":"J Byun","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"pdig.0000908.ref025","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3109\/00365519509088453","article-title":"Using data preprocessing and single layer perceptron to analyze laboratory data","volume":"222","author":"JJ Forsstr\u00f6m","year":"1995","journal-title":"Scand J Clin Lab Invest Suppl"},{"issue":"1","key":"pdig.0000908.ref026","doi-asserted-by":"crossref","first-page":"24454","DOI":"10.1038\/s41598-024-75896-y","article-title":"An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients","volume":"14","author":"K Chadaga","year":"2024","journal-title":"Sci Rep"},{"key":"pdig.0000908.ref027","first-page":"1","article-title":"Improving Early Prediction of Abnormal Recovery after Appendectomy in Children using Real-world Data from Wearables","volume":"2024","author":"R Hua","year":"2024","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"1","key":"pdig.0000908.ref028","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1038\/s41746-023-00890-z","article-title":"Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy","volume":"6","author":"HMK Ghomrawi","year":"2023","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pdig.0000908.ref029","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13017-023-00527-2","article-title":"Artificial intelligence and acute appendicitis: a systematic review of diagnostic and prognostic models","volume":"18","author":"M Issaiy","year":"2023","journal-title":"World J Emerg Surg"},{"key":"pdig.0000908.ref030","unstructured":"Marcinkevi\u010ds R, Reis Wolfertstetter P, Klimiene U, Ozkan E, Chin-Cheong K, Paschke A, et al. Regensburg Pediatric Appendicitis Dataset Internet. Zenodo; 2023 cited 2025 May 26. https:\/\/zenodo.org\/records\/7669442"},{"key":"pdig.0000908.ref031","author":"R Marcinkevi\u010ds","year":"2024"},{"issue":"5","key":"pdig.0000908.ref032","doi-asserted-by":"crossref","first-page":"532","DOI":"10.5152\/TurkArchPediatr.2022.22076","article-title":"Comparison of appendicitis scoring systems in childhood appendicitis","volume":"57","author":"S Sa\u011f","year":"2022","journal-title":"Turk Arch Pediatr"},{"issue":"5","key":"pdig.0000908.ref033","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1111\/ans.12415","article-title":"When does ultrasonography influence management in suspected appendicitis?","volume":"84","author":"DSG Scrimgeour","year":"2014","journal-title":"ANZ J Surg"},{"issue":"12","key":"pdig.0000908.ref034","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1007\/s00383-018-4350-1","article-title":"The non-visualized appendix and secondary signs on ultrasound for pediatric appendicitis in the community hospital setting","volume":"34","author":"JM Held","year":"2018","journal-title":"Pediatr Surg Int"},{"issue":"2","key":"pdig.0000908.ref035","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1148\/radiol.2242010998","article-title":"CT of appendicitis in children","volume":"224","author":"MJ Callahan","year":"2002","journal-title":"Radiology"},{"issue":"2","key":"pdig.0000908.ref036","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1097\/01.SMJ.0000129927.88863.65","article-title":"Association of clinical and laboratory variables with ultrasound findings in right upper quadrant abdominal pain","volume":"98","author":"LD Mills","year":"2005","journal-title":"South Med J"},{"issue":"6","key":"pdig.0000908.ref037","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s10140-018-1624-9","article-title":"Diagnostic performance of CT for pediatric patients with suspected appendicitis in various clinical settings: a systematic review and meta-analysis","volume":"25","author":"DW Kim","year":"2018","journal-title":"Emerg Radiol"},{"issue":"4","key":"pdig.0000908.ref038","doi-asserted-by":"crossref","first-page":"977","DOI":"10.2214\/ajr.175.4.1750977","article-title":"Imaging evaluation of suspected appendicitis in a pediatric population: effectiveness of sonography versus CT","volume":"175","author":"CJ Sivit","year":"2000","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"pdig.0000908.ref039","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1053\/jpsu.2003.50110","article-title":"The diagnosis of acute appendicitis in a pediatric population: to CT or not to CT","volume":"38","author":"AE Stephen","year":"2003","journal-title":"J Pediatr Surg"},{"issue":"1","key":"pdig.0000908.ref040","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1097\/PEC.0000000000001710","article-title":"Appendicitis: Role of MRI","volume":"35","author":"MK Mittal","year":"2019","journal-title":"Pediatr Emerg Care"},{"issue":"6","key":"pdig.0000908.ref041","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1017\/S1481803500014238","article-title":"Pediatric appendicitis: pathophysiology and appropriate use of diagnostic imaging","volume":"8","author":"GDG Brennan","year":"2006","journal-title":"CJEM"},{"issue":"1","key":"pdig.0000908.ref042","doi-asserted-by":"crossref","DOI":"10.1093\/bjsopen\/zraa034","article-title":"Perforation rate after a diagnosis of uncomplicated appendicitis on CT","volume":"5","author":"K Lastunen","year":"2021","journal-title":"BJS Open"},{"issue":"6","key":"pdig.0000908.ref043","doi-asserted-by":"crossref","first-page":"162151","DOI":"10.1016\/j.jpedsurg.2024.162151","article-title":"Machine-learning-assisted preoperative prediction of pediatric appendicitis severity","volume":"60","author":"A Erman","year":"2025","journal-title":"J Pediatr Surg"},{"issue":"1","key":"pdig.0000908.ref044","doi-asserted-by":"crossref","first-page":"35","DOI":"10.11622\/smedj.2020102","article-title":"Ultrasonographic findings of child acute appendicitis incorporated into a scoring system","volume":"63","author":"GN Pop","year":"2022","journal-title":"Singapore Med J"},{"issue":"1","key":"pdig.0000908.ref045","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1111\/ijlh.14163","article-title":"The role of blood inflammatory markers in the preoperative diagnosis of acute appendicitis","volume":"46","author":"A Adir","year":"2024","journal-title":"Int J Lab Hematol"},{"issue":"22","key":"pdig.0000908.ref046","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1001\/jama.2021.20502","article-title":"Diagnosis and Management of Acute Appendicitis in Adults: A Review","volume":"326","author":"D Moris","year":"2021","journal-title":"JAMA"},{"issue":"1","key":"pdig.0000908.ref047","first-page":"63","article-title":"Profile of signs and symptoms in mild and advanced acute appendicitis","volume":"95","author":"H Yajima","year":"2010","journal-title":"Int Surg"},{"issue":"1","key":"pdig.0000908.ref048","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.amjsurg.2019.04.018","article-title":"Neutrophil-to-lymphocyte ratio predicts acute appendicitis and distinguishes between complicated and uncomplicated appendicitis: A systematic review and meta-analysis","volume":"219","author":"S Hajibandeh","year":"2020","journal-title":"Am J Surg"},{"issue":"4","key":"pdig.0000908.ref049","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1055\/a-2490-1156","article-title":"Utility of urine dipstick testing in pediatric appendicitis: assessing its role in identifying complicated cases and retrocecal appendicitis","volume":"35","author":"CM Biehl","year":"2025","journal-title":"Eur J Pediatr Surg"},{"key":"pdig.0000908.ref050","unstructured":"Thapa A. Dharma-AppendicitisModel. 2025. [Cited 2025 May 26]. https:\/\/github.com\/ajung17\/Dharma-AppendicitisModel"},{"issue":"6","key":"pdig.0000908.ref051","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: machine learning and the future of medicine","volume":"284","author":"GS Handelman","year":"2018","journal-title":"J Intern Med"},{"issue":"4","key":"pdig.0000908.ref052","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s00431-017-2867-2","article-title":"Conservative treatment in uncomplicated acute appendicitis: reassessment of practice safety","volume":"176","author":"Z Steiner","year":"2017","journal-title":"Eur J Pediatr"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000908","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:37:28Z","timestamp":1769020648000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000908"}},"subtitle":[],"editor":[{"given":"Hadi","family":"Ghasemi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,1,21]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,21]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0000908","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,21]]}}}