Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.
Background: To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinic...
| Publicado en: | World Journal of Emergency Surgery Vol. 18; no. 1; pp. 1 - 32 |
|---|---|
| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
BioMed Central
12/19/2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174321416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174321416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17497922 38OG jtl: World Journal of Emergency Surgery issn: 17497922 maglogo: N pubinfo: dt: 12/19/2023 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 174321416 174321416 174321416 10.1186/s13017-023-00527-2 174321416 ppf: 1 ppct: 31 formats: tig: atl: Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models. aug: au: Issaiy, Mahbod Zarei, Diana Saghazadeh, Amene affil: https://ror.org/01c4pz451 School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran sug: subj: Artificial Intelligence Methods Appendicitis Prognosis Acute Disease Prognosis Appendicitis Diagnosis Acute Disease Diagnosis Prediction Models Human Systematic Review PubMed Embase Machine Learning Deep Learning Checklists Neural Networks (Computer) Algorithms Logistic Regression Descriptive Statistics Sepsis Risk Factors Risk Assessment Internal Validity ab: Background: To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. Main body: A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. Results: In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. Conclusion: AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|