Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis
Abstract The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent cli...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46721-9 |
| _version_ | 1797630322051907584 |
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| author | Leila Shahmoradi Reza Safdari Mir Mikail Mirhosseini Sorayya Rezayi Mojtaba Javaherzadeh |
| author_facet | Leila Shahmoradi Reza Safdari Mir Mikail Mirhosseini Sorayya Rezayi Mojtaba Javaherzadeh |
| author_sort | Leila Shahmoradi |
| collection | DOAJ |
| description | Abstract The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system. |
| first_indexed | 2024-03-11T11:05:34Z |
| format | Article |
| id | doaj.art-939043528062404b987d726c73465c9d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T11:05:34Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-939043528062404b987d726c73465c9d2023-11-12T12:13:44ZengNature PortfolioScientific Reports2045-23222023-11-0113111510.1038/s41598-023-46721-9Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitisLeila Shahmoradi0Reza Safdari1Mir Mikail Mirhosseini2Sorayya Rezayi3Mojtaba Javaherzadeh4Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical SciencesHealth Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical SciencesHealth Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical SciencesHealth Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical SciencesGeneral Surgery and Thoracic Surgery, Modarres Hospital, Shahid Beheshti University of Medical SciencesAbstract The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system.https://doi.org/10.1038/s41598-023-46721-9 |
| spellingShingle | Leila Shahmoradi Reza Safdari Mir Mikail Mirhosseini Sorayya Rezayi Mojtaba Javaherzadeh Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis Scientific Reports |
| title | Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis |
| title_full | Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis |
| title_fullStr | Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis |
| title_full_unstemmed | Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis |
| title_short | Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis |
| title_sort | development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis |
| url | https://doi.org/10.1038/s41598-023-46721-9 |
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