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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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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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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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Issaiy M, Zarei D, Saghazadeh A. Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models. World J Emerg Surg 2023; 18:59. [PMID: 38114983 PMCID: PMC10729387 DOI: 10.1186/s13017-023-00527-2] [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: 11/10/2023] [Accepted: 12/06/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. MAIN BODY A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. RESULTS In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. CONCLUSION AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.
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Affiliation(s)
- Mahbod Issaiy
- School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Systematic Review and Meta-Analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Diana Zarei
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran
| | - Amene Saghazadeh
- Systematic Review and Meta-Analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [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: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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