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Hariri M, Aydın A, Sıbıç O, Somuncu E, Yılmaz S, Sönmez S, Avşar E. LesionScanNet: dual-path convolutional neural network for acute appendicitis diagnosis. Health Inf Sci Syst 2025; 13:3. [PMID: 39654693 PMCID: PMC11625030 DOI: 10.1007/s13755-024-00321-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Accepted: 11/19/2024] [Indexed: 12/12/2024] Open
Abstract
Acute appendicitis is an abrupt inflammation of the appendix, which causes symptoms such as abdominal pain, vomiting, and fever. Computed tomography (CT) is a useful tool in accurate diagnosis of acute appendicitis; however, it causes challenges due to factors such as the anatomical structure of the colon and localization of the appendix in CT images. In this paper, a novel Convolutional Neural Network model, namely, LesionScanNet for the computer-aided detection of acute appendicitis has been proposed. For this purpose, a dataset of 2400 CT scan images was collected by the Department of General Surgery at Kanuni Sultan Süleyman Research and Training Hospital, Istanbul, Turkey. LesionScanNet is a lightweight model with 765 K parameters and includes multiple DualKernel blocks, where each block contains a convolution, expansion, separable convolution layers, and skip connections. The DualKernel blocks work with two paths of input image processing, one of which uses 3 × 3 filters, and the other path encompasses 1 × 1 filters. It has been demonstrated that the LesionScanNet model has an accuracy score of 99% on the test set, a value that is greater than the performance of the benchmark deep learning models. In addition, the generalization ability of the LesionScanNet model has been demonstrated on a chest X-ray image dataset for pneumonia and COVID-19 detection. In conclusion, LesionScanNet is a lightweight and robust network achieving superior performance with smaller number of parameters and its usage can be extended to other medical application domains.
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Affiliation(s)
- Muhab Hariri
- Electrical and Electronics Engineering Department, Çukurova University, 01330 Adana, Turkey
| | - Ahmet Aydın
- Biomedical Engineering Department, Çukurova University, 01330 Adana, Turkey
| | - Osman Sıbıç
- General Surgery Department, Derik State Hospital, 47800 Mardin, Turkey
| | - Erkan Somuncu
- General Surgery Department, Kanuni Sultan Suleyman Research and Training Hospital, 34303 Istanbul, Turkey
| | - Serhan Yılmaz
- General Surgery Department, Bilkent City Hospital, 06800 Ankara, Turkey
| | - Süleyman Sönmez
- Interventional Radiology Department, Kanuni Sultan Suleyman Research and Training Hospital, 34303 Istanbul, Turkey
| | - Ercan Avşar
- Section for Fisheries Technology, Institute of Aquatic Resources, DTU Aqua, Technical University of Denmark, 9850 Hirtshals, Denmark
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Kucukakcali Z, Akbulut S. Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model. World J Clin Cases 2025; 13:104379. [DOI: 10.12998/wjcc.v13.i22.104379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2024] [Revised: 03/16/2025] [Accepted: 04/11/2025] [Indexed: 05/14/2025] Open
Abstract
BACKGROUND Acute appendicitis (AAp) is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures. Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms; hence, negative AAp and complicated AAp are the primary concerns in research on AAp. In other terms, further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.
AIM To use a Stochastic Gradient Boosting (SGB)-based machine learning (ML) algorithm to tell the difference between AAp patients who are complicated and those who are not, and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.
METHODS This study analyzed an open access data set containing 140 people, including 41 healthy controls, 65 individuals with uncomplicated AAp, and 34 individuals with complicated AAp. We analyzed some demographic data (age, sex) of the patients and the following biochemical blood parameters: White blood cell (WBC) count, neutrophils, lymphocytes, monocytes, platelet count, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, mean platelet volume, neutrophil-to-immature granulocyte ratio, ferritin, total bilirubin, immature granulocyte count, immature granulocyte percent, and neutrophil-to-immature granulocyte ratio. We tested the SGB model using n-fold cross-validation. It was implemented with an 80-20 training-test split. We used variable importance values to identify the variables that were most effective on the target.
RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%, a micro aera under the curve (AUC) of 94.7%, a sensitivity of 94.7%, and a specificity of 100%. In distinguishing complicated AAp patients from uncomplicated ones, the model achieved an accuracy of 78.9%, a micro AUC of 79%, a sensitivity of 83.3%, and a specificity of 76.9%. The most useful biomarkers for confirming the AA diagnosis were WBC (100%), neutrophils (95.14%), and the lymphocyte-monocyte ratio (76.05%). On the other hand, the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin (100%), WBC (96.90%), and the neutrophil-immature granulocytes ratio (64.05%).
CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients. Although the model's accuracy in the classification of complicated AAp is moderate, the high variable importance obtained is clinically significant. We need further prospective validation studies, but the integration of such ML algorithms into clinical practice may improve diagnostic processes.
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Affiliation(s)
- Zeynep Kucukakcali
- Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
| | - Sami Akbulut
- Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
- Surgery and Liver Transplant Institute, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
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Navaei M, Doogchi Z, Gholami F, Tavakoli MK. Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data. Health Sci Rep 2025; 8:e70756. [PMID: 40260048 PMCID: PMC12010561 DOI: 10.1002/hsr2.70756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 04/02/2025] [Accepted: 04/07/2025] [Indexed: 04/23/2025] Open
Abstract
Background and Aims Appendicitis is the most common surgical emergency in pediatric patients, requiring timely diagnosis to prevent complications. This study introduces an innovative approach by integrating clinical, laboratory, and imaging features with advanced machine-learning techniques to enhance diagnostic accuracy in pediatric appendicitis. Methods A retrospective analysis was conducted on 782 pediatric patients from the Regensburg Pediatric Appendicitis Data set. Clinical scores, laboratory markers, and imaging findings were analyzed. Statistical comparisons were performed using independent t-tests and χ 2 tests, with significance set at p < 0.05. Predictive models, including logistic regression and machine learning classifiers, were developed and evaluated using accuracy, precision, recall, and F1-score. Results Significant differences were observed in clinical scores (e.g., Alvarado Score and Pediatric Appendicitis Score) and laboratory markers (e.g., WBC count and neutrophil percentage) between appendicitis (AA) and non-appendicitis (Non-AA) groups (p < 0.001). Imaging features, including appendix diameter, also demonstrated diagnostic value. Among predictive models, the Random Forest classifier achieved the highest accuracy (94.5%), with strong precision (93.8%) and recall (95.2%) for appendicitis diagnosis. Conclusion This study represents a novel application of machine learning models, particularly Random Forest, to enhance diagnostic accuracy for pediatric appendicitis. The integration of clinical, laboratory, and imaging features offers a comprehensive and precise diagnostic framework. Further validation in diverse populations is recommended.
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Affiliation(s)
- Mahdi Navaei
- Department of Information TechnologyUniversity of Applied Science and TechnologyTehranIran
| | - Zohre Doogchi
- Department of Education and ResearchUniversity of Applied Science and TechnologyTehranIran
| | - Fatemeh Gholami
- Department of Computer ScienceAmirkabir University of Technology (Tehran Polytechnic)TehranIran
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Maleš I, Kumrić M, Huić Maleš A, Cvitković I, Šantić R, Pogorelić Z, Božić J. A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review. Diagnostics (Basel) 2025; 15:866. [PMID: 40218216 PMCID: PMC11988987 DOI: 10.3390/diagnostics15070866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2025] [Revised: 03/24/2025] [Accepted: 03/27/2025] [Indexed: 04/14/2025] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources from PubMed/MEDLINE, IEEE Xplore, arXiv, Web of Science, and Scopus, covering publications up to 14 February 2025. AI models have demonstrated potential in triage, enabling rapid differentiation of appendicitis from other causes of abdominal pain. In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. These tools also predict disease severity, aiding decisions between conservative management and surgery. Radiomics further enhances diagnostic precision by analyzing imaging data. Intraoperatively, AI applications are emerging to support real-time decision-making, assess procedural steps, and improve surgical training. Postoperatively, ML models predict complications such as abscess formation and sepsis, facilitating early interventions and personalized recovery plans. This is the first comprehensive review to examine AI's role across the entire appendicitis treatment process, including triage, diagnosis, severity prediction, intraoperative assistance, and postoperative prognosis. Despite its potential, challenges remain regarding data quality, model interpretability, ethical considerations, and clinical integration. Future efforts should focus on developing end-to-end AI-assisted workflows that enhance diagnosis, treatment, and patient outcomes while ensuring equitable access and clinician oversight.
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Affiliation(s)
- Ivan Maleš
- Department of Abdominal Surgery, University Hospital of Split, Spinčićeva 1, 21000 Split, Croatia
| | - Marko Kumrić
- Department of Pathophysiology, School of Medicine, University of Split, Šoltanska 2A, 21000 Split, Croatia
- Laboratory for Cardiometabolic Research, School of Medicine, University of Split, Šoltanska 2A, 21000 Split, Croatia
| | - Andrea Huić Maleš
- Department of Pediatrics, University Hospital of Split, Spinčićeva 1, 21000 Split, Croatia
| | - Ivan Cvitković
- Department of Anesthesiology and Intensive Care, University Hospital of Split, Spinčićeva 1, 21000 Split, Croatia
| | - Roko Šantić
- Department of Pathophysiology, School of Medicine, University of Split, Šoltanska 2A, 21000 Split, Croatia
| | - Zenon Pogorelić
- Department of Surgery, School of Medicine, University of Split, Šoltanska 2A, 21000 Split, Croatia
- Department of Pediatric Surgery, University Hospital of Split, Spinčićeva 1, 21000 Split, Croatia
| | - Joško Božić
- Department of Pathophysiology, School of Medicine, University of Split, Šoltanska 2A, 21000 Split, Croatia
- Laboratory for Cardiometabolic Research, School of Medicine, University of Split, Šoltanska 2A, 21000 Split, Croatia
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Rey R, Gualtieri R, La Scala G, Posfay Barbe K. Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review. Eur J Pediatr Surg 2024; 34:385-391. [PMID: 38290564 DOI: 10.1055/a-2257-5122] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review. METHODS PubMed, Embase, and Web of Science were searched using the following keywords: "pediatric," "artificial intelligence," "standard practices," and "appendicitis," up to September 2023. The risk of bias was assessed using PROBAST. RESULTS A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a "high risk" concerning their overall risk of bias. CONCLUSION We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.
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Affiliation(s)
- Robin Rey
- Department of Human Medicine, Faculty of Medicine, University of Geneva, Genève, Switzerland
| | - Renato Gualtieri
- Department of Pediatrics, Gynecology and Obstetrics, University of Geneva, Genève, Switzerland
| | - Giorgio La Scala
- Division of Pediatric Surgery, Hôpital des enfants, Geneva University Hospitals, Genève, Switzerland
| | - Klara Posfay Barbe
- Division of General Pediatrics, Hôpital des enfants, Geneva University Hospitals, Genève, Switzerland
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An J, Kim IS, Kim KJ, Park JH, Kang H, Kim HJ, Kim YS, Ahn JH. Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings. Sci Rep 2024; 14:22658. [PMID: 39349512 PMCID: PMC11442641 DOI: 10.1038/s41598-024-72889-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 09/11/2024] [Indexed: 10/02/2024] Open
Abstract
This study evaluates the diagnostic efficacy of automated machine learning (AutoGluon) with automated feature engineering and selection (autofeat), focusing on clinical manifestations, and a model integrating both clinical manifestations and CT findings in adult patients with ambiguous computed tomography (CT) results for acute appendicitis (AA). This evaluation was compared with conventional single machine learning models such as logistic regression(LR) and established scoring systems such as the Adult Appendicitis Score(AAS) to address the gap in diagnostic approaches for uncertain AA cases. In this retrospective analysis of 303 adult patients with indeterminate CT findings, the cohort was divided into appendicitis (n = 115) and non-appendicitis (n = 188) groups. AutoGluon and autofeat were used for AA prediction. The AutoGluon-clinical model relied solely on clinical data, whereas the AutoGluon-clinical-CT model included both clinical and CT data. The area under the receiver operating characteristic curve (AUROC) and other metrics for the test dataset, namely accuracy, sensitivity, specificity, PPV, NPV, and F1 score, were used to compare AutoGluon models with single machine learning models and the AAS. The single ML models in this study were LR, LASSO regression, ridge regression, support vector machine, decision tree, random forest, and extreme gradient boosting. Feature importance values were extracted using the "feature_importance" attribute from AutoGluon. The AutoGluon-clinical model demonstrated an AUROC of 0.785 (95% CI 0.691-0.890), and the ridge regression model with only clinical data revealed an AUROC of 0.755 (95% CI 0.649-0.861). The AutoGluon-clinical-CT model (AUROC 0.886 with 95% CI 0.820-0.951) performed better than the ridge model using clinical and CT data (AUROC 0.852 with 95% CI 0.774-0.930, p = 0.029). A new feature, exp(-(duration from pain to CT)3 + rebound tenderness), was identified (importance = 0.049, p = 0.001). AutoML (AutoGluon) and autoFE (autofeat) enhanced the diagnosis of uncertain AA cases, particularly when combining CT and clinical findings. This study suggests the potential of integrating AutoML and autoFE in clinical settings to improve diagnostic strategies and patient outcomes and make more efficient use of healthcare resources. Moreover, this research supports further exploration of machine learning in diagnostic processes.
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Affiliation(s)
- Juho An
- Department of Emergency Medicine, Ajou University School of Medicine, World Cup-ro, Suwon, Gyeonggi-do, 16499, South Korea
| | - Il Seok Kim
- Department of Anesthesiology and Pain Medicine, Kangdong Sacred Hospital, Hallym University College of Medicine, Seongan-ro, Seoul, 05355, South Korea
| | - Kwang-Ju Kim
- Electronics and Telecommunications Research Institute (ETRI), Techno sunhwan-ro, Daegu, 42994, South Korea
| | - Ji Hyun Park
- Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, World Cup-ro, Suwon, Gyeonggi-do, 16499, South Korea
| | - Hyuncheol Kang
- Department of Big Data and AI, Hoseo University, Hoseo-ro, Asan, Chungcheongnam-do, 31499, South Korea
| | - Hyuk Jung Kim
- Department of Radiology, Daejin Medical Center, Bundang Jesaeng General Hospital, Seohyeon-ro, Seongnam, Gyeonggi-do, 13590, South Korea
| | - Young Sik Kim
- Department of Emergency Medicine, Daejin Medical Center, Bundang Jesaeng General Hospital, Seohyeon-ro, Seongnam, Gyeonggi-do, 13590, South Korea
| | - Jung Hwan Ahn
- Department of Emergency Medicine, Ajou University School of Medicine, World Cup-ro, Suwon, Gyeonggi-do, 16499, South Korea.
- Electronics and Telecommunications Research Institute (ETRI), Techno sunhwan-ro, Daegu, 42994, South Korea.
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Gollapalli M, Rahman A, Kudos SA, Foula MS, Alkhalifa AM, Albisher HM, Al-Hariri MT, Mohammad N. Appendicitis Diagnosis: Ensemble Machine Learning and Explainable Artificial Intelligence-Based Comprehensive Approach. BIG DATA AND COGNITIVE COMPUTING 2024; 8:108. [DOI: 10.3390/bdcc8090108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early diagnosis and treatment are crucial. While Alvarado’s clinical scoring system is not sufficient, ultrasound and computed tomography (CT) imaging are effective but have downsides such as operator-dependency and radiation exposure. This study proposes the use of machine learning methods and a locally collected reliable dataset to enhance the identification of acute appendicitis while detecting the differences between complicated and non-complicated appendicitis. Machine learning can help reduce diagnostic errors and improve treatment decisions. This study conducted four different experiments using various ML algorithms, including K-nearest neighbors (KNN), DT, bagging, and stacking. The experimental results showed that the stacking model had the highest training accuracy, test set accuracy, precision, and F1 score, which were 97.51%, 92.63%, 95.29%, and 92.04%, respectively. Feature importance and explainable AI (XAI) identified neutrophils, WBC_Count, Total_LOS, P_O_LOS, and Symptoms_Days as the principal features that significantly affected the performance of the model. Based on the outcomes and feedback from medical health professionals, the scheme is promising in terms of its effectiveness in diagnosing of acute appendicitis.
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Affiliation(s)
- Mohammed Gollapalli
- Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Atta Rahman
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Sheriff A. Kudos
- Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed S. Foula
- Department of Surgery, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Abdullah Mahmoud Alkhalifa
- Department of Surgery, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Hassan Mohammed Albisher
- Department of Surgery, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Taha Al-Hariri
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Nazeeruddin Mohammad
- Cybersecurity Center, Prince Mohammad Bin Fahd University, P.O. Box 1664, Alkhobar 31952, Saudi Arabia
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Wei W, Tongping S, Jiaming W. Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques. Sci Rep 2024; 14:16473. [PMID: 39013966 PMCID: PMC11252286 DOI: 10.1038/s41598-024-67453-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 07/11/2024] [Indexed: 07/18/2024] Open
Abstract
Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.
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Affiliation(s)
- Wang Wei
- The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, China
| | - Shen Tongping
- School of Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
- Graduate School, Angeles University Foundation, Angeles, Philippines.
| | - Wang Jiaming
- School of Information Engineering, Anhui University of Chinese Medicine, Hefei, China
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Harmantepe AT, Dulger UC, Gonullu E, Dikicier E, Şentürk A, Eröz E. A method for predicting mortality in acute mesenteric ischemia: Machine learning. ULUS TRAVMA ACIL CER 2024; 30:487-492. [PMID: 38967529 PMCID: PMC11331353 DOI: 10.14744/tjtes.2024.48074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/02/2024] [Accepted: 06/07/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI). METHODS A total of 122 patients diagnosed with AMI at Sakarya University Training and Research Hospital between January 2011 and June 2023 were included in the study. These patients were divided into a training cohort (n=97) and a validation cohort (n=25), and further categorized as survivors and non-survivors during hospitalization. Serum-based laboratory results served as features. Hyperfeatures were eliminated using Recursive Feature Elimination (RFE) in Python to optimize outcomes. ML algorithms and data analyses were performed using Python (version 3.7). RESULTS Of the patients, 56.5% were male (n=69) and 43.5% were female (n=53). The mean age was 71.9 years (range 39-94 years). The mortality rate during hospitalization was 50% (n=61). To achieve optimal results, the model incorporated features such as age, red cell distribution width (RDW), C-reactive protein (CRP), D-dimer, lactate, globulin, and creatinine. Success rates in test data were as follows: logistic regression (LG), 80%; random forest (RF), 60%; k-nearest neighbor (KN), 52%; multilayer perceptron (MLP), 72%; and support vector classifier (SVC), 84%. A voting classifier (VC), aggregating votes from all models, achieved an 84% success rate. Among the models, SVC (sensitivity 1.0, specificity 0.77, area under the curve (AUC) 0.90, Confidence Interval (95%): (0.83-0.84)) and VC (sensitivity 1.0, specificity 0.77, AUC 0.88, Confidence Interval (95%): (0.83-0.84)) were noted for their effectiveness. CONCLUSION Independent risk factors for mortality were identified in patients with AMI. An efficient and rapid method using various ML models to predict mortality has been developed.
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Affiliation(s)
- Ahmet Tarık Harmantepe
- Department of Gastroenterology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Ugur Can Dulger
- Department of General Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Emre Gonullu
- Department of Gastroenterology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Enis Dikicier
- Department of General Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Adem Şentürk
- Department of Oncology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Erhan Eröz
- Department of General Surgery, Sakarya University Training and Research Hospital, Sakarya-Türkiye
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Yazici H, Ugurlu O, Aygul Y, Ugur MA, Sen YK, Yildirim M. Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians. BMC Emerg Med 2024; 24:101. [PMID: 38886641 PMCID: PMC11184860 DOI: 10.1186/s12873-024-01023-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUNDS Acute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA. METHODS Data regarding operated AA patients between 2012 and 2022 were analyzed retrospectively. Based on operative findings, patients were evaluated under two groups: perforated AA and none-perforated AA. The features that showed statistical significance (p < 0.05) in both univariate and multivariate analysis were included in the prediction models as input features. Five different error metrics and the area under the receiver operating characteristic curve (AUC) were used for model comparison. RESULTS A total number of 1132 patients were included in the study. Patients were divided into training (932 samples), testing (100 samples), and validation (100 samples) sets. Age, gender, neutrophil count, lymphocyte count, Neutrophil to Lymphocyte ratio, total bilirubin, C-Reactive Protein (CRP), Appendix Diameter, and PeriAppendicular Liquid Collection (PALC) were significantly different between the two groups. In the multivariate analysis, age, CRP, and PALC continued to show a significant difference in the perforated AA group. According to univariate and multivariate analysis, two data sets were used in the prediction model. K-Nearest Neighbors and Logistic Regression algorithms achieved the best prediction performance in the validation group with an accuracy of 96%. CONCLUSION The results showed that using only three input features (age, CRP, and PALC), the severity of AA can be predicted with high accuracy. The developed prediction model can be useful in clinical practice.
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Affiliation(s)
- Hilmi Yazici
- General Surgery Department, Marmara University Pendik Research and Training Hospital, Istanbul, Turkey.
| | - Onur Ugurlu
- Faculty of Engineering and Architecture, Izmir Bakircay University, Izmir, Turkey
| | - Yesim Aygul
- Department of Mathematics, Ege University, Izmir, Turkey
| | - Mehmet Alperen Ugur
- General Surgery Department, University of Health Sciences Izmir Bozyaka Research and Training Hospital, Izmir, Turkey
| | - Yigit Kaan Sen
- General Surgery Department, University of Health Sciences Izmir Bozyaka Research and Training Hospital, Izmir, Turkey
| | - Mehmet Yildirim
- General Surgery Department, University of Health Sciences Izmir Bozyaka Research and Training Hospital, Izmir, Turkey
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Males I, Boban Z, Kumric M, Vrdoljak J, Berkovic K, Pogorelic Z, Bozic J. Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis. Sci Rep 2024; 14:12772. [PMID: 38834671 DOI: 10.1038/s41598-024-63513-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 05/29/2024] [Indexed: 06/06/2024] Open
Abstract
The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical presentation, laboratory and radiological imaging. However, utilizing such an approach results in as much as 10-15% of negative appendectomies. Hence, in the present study, we aimed to develop a machine learning (ML) model designed to reduce the number of negative appendectomies in pediatric patients with a high clinical probability of acute appendicitis. The model was developed and validated on a registry of 551 pediatric patients with suspected acute appendicitis that underwent surgical treatment. Clinical, anthropometric, and laboratory features were included for model training and analysis. Three machine learning algorithms were tested (random forest, eXtreme Gradient Boosting, logistic regression) and model explainability was obtained. Random forest model provided the best predictions achieving mean specificity and sensitivity of 0.17 ± 0.01 and 0.997 ± 0.001 for detection of acute appendicitis, respectively. Furthermore, the model outperformed the appendicitis inflammatory response (AIR) score across most sensitivity-specificity combinations. Finally, the random forest model again provided the best predictions for discrimination between complicated appendicitis, and either uncomplicated acute appendicitis or no appendicitis at all, with a joint mean sensitivity of 0.994 ± 0.002 and specificity of 0.129 ± 0.009. In conclusion, the developed ML model might save as much as 17% of patients with a high clinical probability of acute appendicitis from unnecessary surgery, while missing the needed surgery in only 0.3% of cases. Additionally, it showed better diagnostic accuracy than the AIR score, as well as good accuracy in predicting complicated acute appendicitis over uncomplicated and negative cases bundled together. This may be useful in centers that advocate for the conservative treatment of uncomplicated appendicitis. Nevertheless, external validation is needed to support these findings.
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Affiliation(s)
- Ivan Males
- Department of Abdominal Surgery, University Hospital of Split, Spinciceva 1, 21000, Split, Croatia
| | - Zvonimir Boban
- Department of Medical Physics and Biophysics, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia
| | - Marko Kumric
- Department of Pathophysiology, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia
- Laboratory for Cardiometabolic Research, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia
- Laboratory for Cardiometabolic Research, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia
| | - Karlotta Berkovic
- Department of Surgery, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia
| | - Zenon Pogorelic
- Department of Surgery, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia.
- Department of Pediatric Surgery, University Hospital of Split, Spinciceva 1, 21000, Split, Croatia.
| | - Josko Bozic
- Department of Medical Physics and Biophysics, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia.
- Department of Pathophysiology, School of Medicine, University of Split, Soltanska 2A, 21000, Split, Croatia.
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12
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Cappuccio M, Bianco P, Rotondo M, Spiezia S, D'Ambrosio M, Menegon Tasselli F, Guerra G, Avella P. Current use of artificial intelligence in the diagnosis and management of acute appendicitis. Minerva Surg 2024; 79:326-338. [PMID: 38477067 DOI: 10.23736/s2724-5691.23.10156-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
INTRODUCTION Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives. EVIDENCE ACQUISITION We performed a literature search on articles published from 2018 to September 2023. We included only original articles. EVIDENCE SYNTHESIS Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis. CONCLUSIONS AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.
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Affiliation(s)
- Micaela Cappuccio
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Paolo Bianco
- Hepatobiliary and Pancreatic Surgery Unit, Pineta Grande Hospital, Castel Volturno, Caserta, Italy
| | - Marco Rotondo
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Salvatore Spiezia
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Marco D'Ambrosio
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | | | - Germano Guerra
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Pasquale Avella
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy -
- Hepatobiliary and Pancreatic Surgery Unit, Pineta Grande Hospital, Castel Volturno, Caserta, Italy
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Bianchi V, Giambusso M, De Iacob A, Chiarello MM, Brisinda G. Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review. Updates Surg 2024; 76:783-792. [PMID: 38472633 PMCID: PMC11129994 DOI: 10.1007/s13304-024-01801-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 02/24/2024] [Indexed: 03/14/2024]
Abstract
Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.
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Affiliation(s)
- Valentina Bianchi
- Emergency Surgery and Trauma Center, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, IRCCS, Fondazione Policlinico Universitario A Gemelli, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Mauro Giambusso
- General Surgery Operative Unit, Vittorio Emanuele Hospital, 93012, Gela, Italy
| | - Alessandra De Iacob
- Emergency Surgery and Trauma Center, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, IRCCS, Fondazione Policlinico Universitario A Gemelli, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Maria Michela Chiarello
- Department of Surgery, General Surgery Operative Unit, Azienda Sanitaria Provinciale Cosenza, 87100, Cosenza, Italy
| | - Giuseppe Brisinda
- Emergency Surgery and Trauma Center, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, IRCCS, Fondazione Policlinico Universitario A Gemelli, Largo Agostino Gemelli 8, 00168, Rome, Italy.
- Catholic School of Medicine, University Department of Translational Medicine and Surgery, 00168, Rome, Italy.
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14
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Abu-Ashour W, Emil S, Poenaru D. Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis. J Pediatr Surg 2024; 59:783-790. [PMID: 38383177 DOI: 10.1016/j.jpedsurg.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/22/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors. METHODS To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method. RESULTS Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster. CONCLUSION AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection. LEVELS OF EVIDENCE Level III.
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Affiliation(s)
- Waseem Abu-Ashour
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada.
| | - Sherif Emil
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada
| | - Dan Poenaru
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada
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15
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Wang S, Bao C, Pei D. Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults. Yonsei Med J 2024; 65:210-216. [PMID: 38515358 PMCID: PMC10973557 DOI: 10.3349/ymj.2023.0246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/21/2023] [Accepted: 11/07/2023] [Indexed: 03/23/2024] Open
Abstract
PURPOSE The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs. MATERIALS AND METHODS This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health examinations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to China Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naïve Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS. RESULTS The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naïve Bayes (0.824 and 0.831, respectively; p<0.05). CONCLUSION The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system.
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Affiliation(s)
- Shuang Wang
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chenhui Bao
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dongmei Pei
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China.
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16
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Bhandarkar S, Tsutsumi A, Schneider EB, Ong CS, Paredes L, Brackett A, Ahuja V. Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review. Surg Infect (Larchmt) 2024; 25:7-18. [PMID: 38150507 DOI: 10.1089/sur.2023.201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023] Open
Abstract
Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice.
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Affiliation(s)
| | - Ayaka Tsutsumi
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Eric B Schneider
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Chin Siang Ong
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lucero Paredes
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale School of Medicine, New Haven, Connecticut, USA
| | - Vanita Ahuja
- Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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17
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Marcinkevičs R, Reis Wolfertstetter P, Klimiene U, Chin-Cheong K, Paschke A, Zerres J, Denzinger M, Niederberger D, Wellmann S, Ozkan E, Knorr C, Vogt JE. Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Med Image Anal 2024; 91:103042. [PMID: 38000257 DOI: 10.1016/j.media.2023.103042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 11/10/2023] [Accepted: 11/20/2023] [Indexed: 11/26/2023]
Abstract
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.
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Affiliation(s)
- Ričards Marcinkevičs
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland.
| | - Patricia Reis Wolfertstetter
- Department of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Steinmetzstrasse 1-3, Regensburg, 93049, Germany; Faculty of Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany.
| | - Ugne Klimiene
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland
| | - Kieran Chin-Cheong
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland
| | - Alyssia Paschke
- Faculty of Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany
| | - Julia Zerres
- Faculty of Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany
| | - Markus Denzinger
- Department of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Steinmetzstrasse 1-3, Regensburg, 93049, Germany; Faculty of Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany
| | - David Niederberger
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland
| | - Sven Wellmann
- Faculty of Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany; Division of Neonatology, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Steinmetzstrasse 1-3, Regensburg, 93049, Germany
| | - Ece Ozkan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, 02139, USA
| | - Christian Knorr
- Department of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Steinmetzstrasse 1-3, Regensburg, 93049, Germany
| | - Julia E Vogt
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland.
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Shahmoradi L, Safdari R, Mirhosseini MM, Rezayi S, Javaherzadeh M. Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis. Sci Rep 2023; 13:19703. [PMID: 37951984 PMCID: PMC10640605 DOI: 10.1038/s41598-023-46721-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 11/04/2023] [Indexed: 11/14/2023] Open
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.
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Affiliation(s)
- Leila Shahmoradi
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Safdari
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mir Mikail Mirhosseini
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Sorayya Rezayi
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mojtaba Javaherzadeh
- General Surgery and Thoracic Surgery, Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Harmantepe AT, Dikicier E, Gönüllü E, Ozdemir K, Kamburoğlu MB, Yigit M. A different way to diagnosis acute appendicitis: machine learning. POLISH JOURNAL OF SURGERY 2023; 96:38-43. [PMID: 38629278 DOI: 10.5604/01.3001.0053.5994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
<b><br>Indroduction:</b> Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.</br> <b><br>Aim:</b> Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method.</br> <b><br>Materials and methods:</b> Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language.</br> <b><br>Results:</b> Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%.</br> <b><br>Conclusions:</b> The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis.</br>.
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Affiliation(s)
| | - Enis Dikicier
- Sakarya University Faculty of Medicine, Department of General Surgery
| | - Emre Gönüllü
- Sakarya University Education and Research Hospital, Department of General Surgery
| | | | | | - Merve Yigit
- Sakarya University Education and Research Hospital, Department of General Surgery
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20
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Küçükakçali Z, Akbulut S, Çolak C. Value of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learning. ULUS TRAVMA ACIL CER 2023; 29:655-662. [PMID: 37278078 PMCID: PMC10315941 DOI: 10.14744/tjtes.2023.10001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/15/2022] [Indexed: 06/07/2023]
Abstract
BACKGROUND The aim of this study is to apply random forest (RF), one of the machine learning (ML) algorithms, to a dataset consisting of patients with a presumed diagnosis of acute appendicitis (AAp) and to reveal the most important factors associated with the diagnosis of AAp based on the variable importance. METHODS An open-access dataset comparing two patient groups with (n=40) and without (n=44) AAp to predict biomarkers for AAp was used for this case-control study. RF was used for modeling the data set. The data were divided into two training and test dataset (80: 20). Accuracy, balanced accuracy (BC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) performance metrics were appraised for model performance. RESULTS Accuracy, BC, sensitivity, specificity, PPV, NPV, and F1 scores pertaining to the RF model were 93.8%, 93.8%, 87.5%, 100%, 100%, 88.9%, and 93.3%, respectively. Following the variable importance values regarding the model, the variables most associated with the diagnosis and prediction of AAp were fecal calprotectin (100 %), radiological imaging (89.9%), white blood test (51.8%), C-reactive protein (47.1%), from symptoms onset to the hospital visit (19.3%), patients age (18.4%), alanine aminotransferase levels >40 (<1%), fever (<1%), and nausea/vomiting (<1%), respectively. CONCLUSION A prediction model was developed for AAp with the ML method in this study. Thanks to this model, biomarkers that predict AAp with high accuracy were determined. Thus, the decision-making process of clinicians for diagnosing AAp will be facilitated, and the risks of perforation and unnecessary operations will be minimized thanks to the timely diagnosis with high accuracy.
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Affiliation(s)
- Zeynep Küçükakçali
- Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya-Türkiye
| | - Sami Akbulut
- Department of Surgery, Inonu University Faculty of Medicine, Malatya-Türkiye
| | - Cemil Çolak
- Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya-Türkiye
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Akbulut S, Yagin FH, Cicek IB, Koc C, Colak C, Yilmaz S. Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence. Diagnostics (Basel) 2023; 13:1173. [PMID: 36980481 PMCID: PMC10047288 DOI: 10.3390/diagnostics13061173] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 03/22/2023] Open
Abstract
BACKGROUND The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). METHOD A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. RESULTS The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6-90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6-94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. CONCLUSION For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.
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Affiliation(s)
- Sami Akbulut
- Department of Surgery, Liver Transplant Institute, Inonu University Faculty of Medicine, 244280 Malatya, Turkey
- Department of Biostatistics, and Medical Informatics, Inonu University Faculty of Medicine, 44280 Malatya, Turkey
| | - Fatma Hilal Yagin
- Department of Biostatistics, and Medical Informatics, Inonu University Faculty of Medicine, 44280 Malatya, Turkey
| | - Ipek Balikci Cicek
- Department of Biostatistics, and Medical Informatics, Inonu University Faculty of Medicine, 44280 Malatya, Turkey
| | - Cemalettin Koc
- Department of Surgery, Liver Transplant Institute, Inonu University Faculty of Medicine, 244280 Malatya, Turkey
| | - Cemil Colak
- Department of Biostatistics, and Medical Informatics, Inonu University Faculty of Medicine, 44280 Malatya, Turkey
| | - Sezai Yilmaz
- Department of Surgery, Liver Transplant Institute, Inonu University Faculty of Medicine, 244280 Malatya, Turkey
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Phan-Mai TA, Thai TT, Mai TQ, Vu KA, Mai CC, Nguyen DA. Validity of Machine Learning in Detecting Complicated Appendicitis in a Resource-Limited Setting: Findings from Vietnam. BIOMED RESEARCH INTERNATIONAL 2023; 2023:5013812. [PMID: 37090195 PMCID: PMC10121350 DOI: 10.1155/2023/5013812] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/30/2022] [Accepted: 03/28/2023] [Indexed: 04/25/2023]
Abstract
Background Complicated appendicitis, a potentially life-threatening condition, is common. However, the diagnosis of this condition is mainly based on physician's experiences and advanced diagnostic equipment. This study built and validated machine learning models to facilitate the detection of complicated appendicitis. Methods A retrospective cohort study was conducted based on medical charts of all patients undergoing a laparoscopic appendectomy at a city hospital during 2016-2020. The synthetic minority over-sampling technique (SMOTE) was used to adjust for the imbalance. Multiple classification approaches were used to train and validate models including support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), artificial neural network (ANN), and gradient boosting (GB). Results Among 1,950 patients included in the data analysis, there were 483 patients identified as having complicated appendicitis (24.8%). Based on data without SMOTE adjustment for imbalance, the accuracy levels and AUCs were high in all models using different parameters, ranging from 0.687 to 0.815. After adjusting for imbalance data using SMOTE, AUC and accuracy levels in the models using imbalance adjusted data were higher. Of these, the GB had all AUC and accuracy values of approximately 0.8 or more in both adjusted and unadjusted data. Conclusions Machine learning approaches including SVM, DT, logistic, KNN, ANN, and GB have a high level of validity in classifying patients with complicated appendicitis and patients without complicated appendicitis. Among these, GB had the highest level of validity and should be used or further validated. Our study indicates the beneficial potentials of machine learning techniques in a clinical setting in general and in the diagnosis of complicated appendicitis in particular.
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Affiliation(s)
- Tuong-Anh Phan-Mai
- General Surgery Department, Nhan dan Gia Dinh Hospital, 1 No Trang Long Street, Ward 7, Binh Thanh District, Ho Chi Minh City, Vietnam
| | - Truc Thanh Thai
- Department of Medical Statistics and Informatics, University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Vietnam
| | - Thanh Quoc Mai
- Department of Medical Statistics and Informatics, University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Vietnam
| | - Kiet Anh Vu
- Planning Department, Nhan dan Gia Dinh Hospital, 1 No Trang Long Street, Ward 7, Binh Thanh District, Ho Chi Minh City, Vietnam
| | - Cong Chi Mai
- General Surgery Department, Nhan dan Gia Dinh Hospital, 1 No Trang Long Street, Ward 7, Binh Thanh District, Ho Chi Minh City, Vietnam
| | - Dung Anh Nguyen
- General Surgery Department, Nhan dan Gia Dinh Hospital, 1 No Trang Long Street, Ward 7, Binh Thanh District, Ho Chi Minh City, Vietnam
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23
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Mijwil MM, Aggarwal K. A diagnostic testing for people with appendicitis using machine learning techniques. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:7011-7023. [PMID: 35095329 PMCID: PMC8785023 DOI: 10.1007/s11042-022-11939-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/09/2021] [Accepted: 01/03/2022] [Indexed: 05/07/2023]
Abstract
Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.
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Affiliation(s)
- Maad M. Mijwil
- Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
| | - Karan Aggarwal
- Electronics and Communication Engineering Department, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India
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24
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Bektaş M, Tuynman JB, Costa Pereira J, Burchell GL, van der Peet DL. Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review. World J Surg 2022; 46:3100-3110. [PMID: 36109367 PMCID: PMC9636121 DOI: 10.1007/s00268-022-06728-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided. METHODS Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models. RESULTS A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration. CONCLUSIONS Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.
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Affiliation(s)
- Mustafa Bektaş
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jurriaan B. Tuynman
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jaime Costa Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
| | - George L. Burchell
- Medical Library, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Donald L. van der Peet
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
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Teng TZJ, Thong XR, Lau KY, Balasubramaniam S, Shelat VG. Acute appendicitis-advances and controversies. World J Gastrointest Surg 2021; 13:1293-1314. [PMID: 34950421 PMCID: PMC8649565 DOI: 10.4240/wjgs.v13.i11.1293] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/24/2021] [Accepted: 09/29/2021] [Indexed: 02/06/2023] Open
Abstract
Being one of the most common causes of the acute abdomen, acute appendicitis (AA) forms the bread and butter of any general surgeon's practice. With the recent advancements in AA's management, much controversy in diagnostic algorithms, possible differential diagnoses, and weighing the management options has been generated, with no absolute consensus in the literature. Since Alvarado described his eponymous clinical scoring system in 1986 to stratify AA risk, there has been a burgeoning of additional scores for guiding downstream management and mortality assessment. Furthermore, advancing literature on the role of antibiotics, variations in appendicectomy, and its adjuncts have expanded the surgeon's repertoire of management options. Owing to the varied presentation, diagnostic tools, and management of AA have also been proposed in special groups such as pregnant patients, the elderly, and the immunocompromised. This article seeks to raise the critical debates about what is currently known about the above aspects of AA and explore the latest controversies in the field. Considering the ever-evolving coronavirus disease 2019 situation worldwide, we also discuss the pandemic's repercussions on patients and how surgeons' practices have evolved in the context of AA.
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Affiliation(s)
- Thomas Zheng Jie Teng
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Department of Undergraduate Medicine, Lee Kong Chian School of Medicine, Singapore 308232, Singapore
| | - Xuan Rong Thong
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Department of Undergraduate Medicine, Lee Kong Chian School of Medicine, Singapore 308232, Singapore
| | - Kai Yuan Lau
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Department of Undergraduate Medicine, Lee Kong Chian School of Medicine, Singapore 308232, Singapore
| | | | - Vishal G Shelat
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Department of Undergraduate Medicine, Lee Kong Chian School of Medicine, Singapore 308232, Singapore
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26
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Marcinkevics R, Reis Wolfertstetter P, Wellmann S, Knorr C, Vogt JE. Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis. Front Pediatr 2021; 9:662183. [PMID: 33996697 PMCID: PMC8116489 DOI: 10.3389/fped.2021.662183] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/01/2021] [Indexed: 01/07/2023] Open
Abstract
Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children. Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables. Results: A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis. Discussion: This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool. Conclusion: ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.
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Affiliation(s)
| | - Patricia Reis Wolfertstetter
- Department of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Regensburg, Germany
| | - Sven Wellmann
- Division of Neonatology, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), University of Regensburg, Regensburg, Germany
| | - Christian Knorr
- Department of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Regensburg, Germany
| | - Julia E Vogt
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
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