Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models
Abstract 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 throug...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-12-01
|
| Series: | World Journal of Emergency Surgery |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13017-023-00527-2 |
| _version_ | 1797377163934040064 |
|---|---|
| author | Mahbod Issaiy Diana Zarei Amene Saghazadeh |
| author_facet | Mahbod Issaiy Diana Zarei Amene Saghazadeh |
| author_sort | Mahbod Issaiy |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T19:48:46Z |
| format | Article |
| id | doaj.art-d3def5eff7df43fca99a300e997a6d7d |
| institution | Directory Open Access Journal |
| issn | 1749-7922 |
| language | English |
| last_indexed | 2024-03-08T19:48:46Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Emergency Surgery |
| spelling | doaj.art-d3def5eff7df43fca99a300e997a6d7d2023-12-24T12:12:38ZengBMCWorld Journal of Emergency Surgery1749-79222023-12-0118113110.1186/s13017-023-00527-2Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic ModelsMahbod Issaiy0Diana Zarei1Amene Saghazadeh2School of Medicine, Tehran University of Medical Sciences (TUMS)School of Medicine, Iran University of Medical SciencesSystematic Review and Meta-Analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN)Abstract 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.https://doi.org/10.1186/s13017-023-00527-2Acute appendicitisAIDeep learningMachine learningSystematic review |
| spellingShingle | Mahbod Issaiy Diana Zarei Amene Saghazadeh Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models World Journal of Emergency Surgery Acute appendicitis AI Deep learning Machine learning Systematic review |
| title | Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models |
| title_full | Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models |
| title_fullStr | Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models |
| title_full_unstemmed | Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models |
| title_short | Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models |
| title_sort | artificial intelligence and acute appendicitis a systematic review of diagnostic and prognostic models |
| topic | Acute appendicitis AI Deep learning Machine learning Systematic review |
| url | https://doi.org/10.1186/s13017-023-00527-2 |
| work_keys_str_mv | AT mahbodissaiy artificialintelligenceandacuteappendicitisasystematicreviewofdiagnosticandprognosticmodels AT dianazarei artificialintelligenceandacuteappendicitisasystematicreviewofdiagnosticandprognosticmodels AT amenesaghazadeh artificialintelligenceandacuteappendicitisasystematicreviewofdiagnosticandprognosticmodels |