Volume 20, Issue 4 (Iranian South Medical Journal 2017)
Iran South Med J 2017, 20(4): 339-348 |
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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
URL: http://ismj.bpums.ac.ir/article-1-886-en.html
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
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: (6419 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.
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.
Type of Study: Original |
Subject:
Surgery
Received: 2016/08/5 | Accepted: 2017/02/27 | Published: 2017/08/27
Received: 2016/08/5 | Accepted: 2017/02/27 | Published: 2017/08/27
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