[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 20, Issue 4 (Iranian South Medical Journal 2017) ::
Iran South Med J 2017, 20(4): 339-348 Back to browse issues page
Better Diagnosis of Acute Appendicitis by Using Artificial Intelligence
Mir Mekaeal Hosseini * 1, Reza Safdari 2, Lila Shahmoradi 2, Mojtaba Javaherzadeh 3
1- Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran , triplex.mmm@gmail.com
2- Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- General Surgery Department, Shahid Modarres Hospital, Shahid Behehsti Medical University, Tehran, Iran
Abstract:   (1586 Views)
Background: Acute appendicitis is the most common cause for the referral of patients with abdominal pains to the emergency department of hospitals and appendectomy is the most common medical emergency operation. Despite the introduction of the various diagnostic methods, exorbitant appendectomy rate still is significant. Therefore, artificial intelligence and machine learning methods could be used as an adjunct tool to improve the diagnosis and treatment of acute appendicitis. Particularly, it can facilitate earlier and more accurate diagnosis, reduce the length of stay in the hospital and decrease the treatment costs.
Materials and Methods: During this developmental research, literature, and resources related to gastrointestinal diseases were studied, variables contributing to the diagnosis were evaluated and categorized by surgeons. The data collected from 181 cases of patients who underwent appendectomy at the Modarres Hospital during 2015 were used as the research database. Then, the support vector machine systems with different architectures implemented and compared to determine the best diagnostic function. Sensitivity, accuracy, and specificity outcomes were used for verification, evaluation, and defining the optimal diagnostic function.
Results: The output obtained from the system of vector machine indicated 91.7, 96.2, and 95 percent for sensitivity, specificity, and accuracy of respectively, which expresses its sufficient efficiency in detecting acute appendicitis.
Conclusion: The results showed that designed support vector machine could be used for the diagnosis of acute appendicitis, resulting in timely detection of acute appendicitis, prevention of unnecessary appendectomy, reduction in patient's length of stay in the hospital and decreasing health care costs.
 
Keywords: Appendicitis, diagnosis, support vector machine, artificial intelligence, machine learning.
Full-Text [PDF 573 kb]   (427 Downloads)    
Type of Study: Original | Subject: Surgery
Received: 2017/08/27 | Accepted: 2017/08/27 | Published: 2017/08/27
References
1. Schwartz, Seymour I. Brunicardi F. Charles. Schwartz's principles of surgery. New York: McGraw-Hill, 2010.
2. Akbulut S, Ulku A, Senol A, Tas M, Yagmur Y. Left-sided appendicitis: Review of 95 published cases and a case report. World J Gastroenterol 2010; 28(44): 5598-602. [PubMed] [Google Scholar]
3. la'al M, Geranpayeh L, Khodadadi F, et al. Sonography versus the Alvarado Scoring System for the diagnosis of acute appendicitis. Tehran Univ Med J 2008; 66(6): 408-12. (Persian)
4. Ohle R, O'Reilly F, O'Brien KK, et al. The Alvarado score for predicting acute appendicitis: a systematic review. BMC Med 2011; 9: 139. [PubMed] [Google Scholar]
5. de Castro SMM, Ünlü C, Steller EP, et al. Evaluation of the appendicitis inflammatory response score for patients with acute appendicitis. World J Surg 2012; 36(7): 1540-5. [PubMed] [Google Scholar]
6. Andersson M, Andersson RE. The appendicitis inflammatory response score: a tool for the diagnosis of acute appendicitis that outperforms the Alvarado score. World J Surg 2008; 32(8): 1843-9. [PubMed] [Google Scholar]
7. Chong CF, Adi MI, Thien A, et al. Development of the RIPASA score: a new appendicitis scoring system for the diagnosis of acute appendicitis. Singapore Med J 2010; 51(3): 220-5. [PubMed] [Google Scholar]
8. Dey S, Mohanta PK, Baruah AK, et al. Alvarado scoring in acute appendicitis-a clinicopathological correlation. Indian J Surg 2010; 72(4): 290-3. [PubMed] [Google Scholar]
9. Weyant MJ, Eachempati SR, Maluccio MA, et al. Interpretation of computed tomography does not correlate with laboratory or pathologic findings in surgically confirmed acute appendicitis. Surgery 2000; 128(2): 145-52. [PubMed] [Google Scholar]
10. Flum DR, Koepsell T. The clinical and economic correlates of misdiagnosed appendicitis: nationwide analysis. Arch Surg 2002; 137(7): 799-804. [PubMed] [Google Scholar]
11. Flum DR, Morris A, Koepsell T, et al. Has misdiagnosis of appendicitis decreased over time? A population-based analysis. JAMA 2001; 286(14): 1748-53. [PubMed] [Google Scholar]
12. Shergill I, Arya M, Upile T, et al. Surgical emergencies in clinical practice. New York City: Springer, 2012, 20-23 [Google Scholar]
13. Pesonen E, Ohmann C, Eskelinen M, et al. Diagnosis of acute appendicitis in two databases. Evaluation of different neighborhoods with an LVQ neural network. Methods Inf Med 1998; 37(1): 59-63. [PubMed] [Google Scholar]
14. Prabhudesai SG, Gould S, Rekhraj S, et al. Artificial neural networks: useful aid in diagnosing acute appendicitis. World J Surg 2008; 32(2): 305-9. [PubMed] [Google Scholar]
15. Sakai S, Kobayashi K, Toyabe S, et al. Comparison of the levels of accuracy of an artificial neural network model and a logistic regression model for the diagnosis of acute appendicitis. J Med Syst 2007; 31(5): 357-64. [PubMed] [Google Scholar]
16. Ghaderzadeh M, Sadoughi F, Ketabat A. Designing a Clinical Decision Support System Based on Artificial Neural Network for Early Detection of Prostate Cancer and Differentiation from Benign Prostatic Hyperplasia. Health Inf Manage 2012; 9(4): 457-64. (Persian) [Google Scholar]
17. Akbarian M, Paydar Kh, Rostam Niakan Kalhori S, et al. Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran. Tehran Univ Med J 2015; 73(4): 251-9. (Persian) [Google Scholar]
18. Widrow B, Rumelhard DE, Lehr MA. Neural networks: applications in industry, business and science. Commun ACM 1994; 73(3): 93-106. [Google Scholar]
19. Zyluk A, Ostrowski P. An analysis of factors influencing accuracy of the diagnosis of acute appendicitis. Pol przegl Chir 2011; 83(3): 135-43.
20. Hamrahi N, Tohidi N. Modelling the diagnosis of appendix disease using bayesian network. National congress of computer science and engineering. 2013 Feb. 19, Najaf Abad, Iran. Najaf Abad: Islamic azad university of Najaf Abad, 2013, 632-6. (Persian) [PubMed] [Google Scholar]
21. Huddar V, Rajan V, Bhattacharya S, et al. Predicting postoperative acute respiratory failure in critical care using nursing notes and physiological signals. 36th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 26-30 Aug. 2014 ,Chicago, IL, USA , 06 Nov 2014. [PubMed] [Google Scholar]
22. Park SY, Seo JS, Lee SC, et al. Application of an artificial intelligence method for diagnosing acute appendicitis: the support vector machine. In: Park HJJ, Stojmenovic I, Choi M, Xhafa F, editors. Future Information Technology: FutureTech 2013. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014, 85-92. [Google Scholar]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA code


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hosseini M M, Safdari R, Shahmoradi L, Javaherzadeh M. Better Diagnosis of Acute Appendicitis by Using Artificial Intelligence. Iran South Med J. 2017; 20 (4) :339-348
URL: http://ismj.bpums.ac.ir/article-1-886-en.html


Volume 20, Issue 4 (Iranian South Medical Journal 2017) Back to browse issues page
دانشگاه علوم پزشکی بوشهر، طب جنوب ISMJ

Iranian South Medical Journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License which allows users to read,
copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly

Copyright © 2017, Iranian South Medical Journal| All Rights Reserved

Persian site map - English site map - Created in 0.06 seconds with 31 queries by YEKTAWEB 3767