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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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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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Chongo G, Soldera J. Use of machine learning models for the prognostication of liver transplantation: A systematic review. World J Transplant 2024; 14:88891. [PMID: 38576762 PMCID: PMC10989468 DOI: 10.5500/wjt.v14.i1.88891] [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: 10/13/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 03/15/2024] Open
Abstract
BACKGROUND Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. AIM To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. RESULTS Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.
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Affiliation(s)
- Gidion Chongo
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
| | - Jonathan Soldera
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
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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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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: 0] [Impact Index Per Article: 0] [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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Tian CW, Chen XX, Shi L, Zhu HY, Dai GC, Chen H, Rui YF. Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients. World J Orthop 2023; 14:741-754. [PMID: 37970626 PMCID: PMC10642403 DOI: 10.5312/wjo.v14.i10.741] [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: 07/22/2023] [Revised: 09/08/2023] [Accepted: 09/28/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors. AIM To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. METHODS A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS. RESULTS The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex. CONCLUSION ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.
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Affiliation(s)
- Chu-Wei Tian
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Xiang-Xu Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Liu Shi
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Huan-Yi Zhu
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Guang-Chun Dai
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Hui Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Yun-Feng Rui
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
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Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC MEDICAL EDUCATION 2023; 23:689. [PMID: 37740191 PMCID: PMC10517477 DOI: 10.1186/s12909-023-04698-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023]
Abstract
INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
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Affiliation(s)
- Shuroug A Alowais
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia.
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Sahar S Alghamdi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Nada Alsuhebany
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Tariq Alqahtani
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sumaya N Almohareb
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Atheer Aldairem
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hisham A Badreldin
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Majed S Al Yami
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shmeylan Al Harbi
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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10
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Chervenkov L, Sirakov N, Kostov G, Velikova T, Hadjidekov G. Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging. World J Radiol 2023; 15:136-145. [PMID: 37275303 PMCID: PMC10236970 DOI: 10.4329/wjr.v15.i5.136] [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: 12/19/2022] [Revised: 03/21/2023] [Accepted: 04/10/2023] [Indexed: 05/23/2023] Open
Abstract
Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.
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Affiliation(s)
- Lyubomir Chervenkov
- Department of Diagnostic Imaging, Medical University Plovdiv, Plovdiv 4000, Bulgaria
- Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
| | - Nikolay Sirakov
- Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
- Department of Diagnostic Imaging, Dental Allergology and Physiotherapy, Faculty of Dental Medicine, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Gancho Kostov
- Department of Special Surgery, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Tsvetelina Velikova
- Department of Clinical Immunology, University Hospital Lozenetz, Sofia 1407, Bulgaria
- Department of Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
| | - George Hadjidekov
- Department of Radiology, University Hospital Lozenetz, Sofia 1407, Bulgaria
- Department of Physics, Biophysics and Radiology, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
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11
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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:diagnostics13061173. [PMID: 36980481 PMCID: PMC10047288 DOI: 10.3390/diagnostics13061173] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [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
- Correspondence:
| | - 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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12
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Xu SM, Dong D, Li W, Bai T, Zhu MZ, Gu GS. Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements. World J Clin Cases 2023; 11:1477-1487. [PMID: 36926411 PMCID: PMC10011995 DOI: 10.12998/wjcc.v11.i7.1477] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging (MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.
AIM To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.
METHODS We searched 464 knee MRI cases between January 2019 and December 2020, including FTD (n = 202) and normal trochlea (n = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, etc.) were calculated.
RESULTS The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96. All values were superior to junior doctors and intermediate doctors, similar to senior doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.
CONCLUSION The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.
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Affiliation(s)
- Sheng-Ming Xu
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Dong Dong
- Department of Radiology, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Wei Li
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Tian Bai
- College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
| | - Ming-Zhu Zhu
- College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
| | - Gui-Shan Gu
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
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13
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Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28:6230-6248. [PMID: 36504554 PMCID: PMC9730439 DOI: 10.3748/wjg.v28.i44.6230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/07/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.
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Affiliation(s)
- J Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Marta Alonso-Bernáldez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | | | - Juan A Vargas-Nuñez
- Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
| | - Ana Ramírez de Molina
- Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Alberto Dávalos
- Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
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14
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Cao XJ, Liu XQ. Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges. World J Psychiatry 2022; 12:1287-1297. [PMID: 36389087 PMCID: PMC9641379 DOI: 10.5498/wjp.v12.i10.1287] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/09/2022] [Accepted: 09/22/2022] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence-based technologies are gradually being applied to psych-iatric research and practice. This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents. In terms of the practice of psychosis risk screening, the application of two artificial intelligence-assisted screening methods, chatbot and large-scale social media data analysis, is summarized in detail. Regarding the challenges of psychiatric risk screening, ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence, which must comply with the four biomedical ethical principles of respect for autonomy, nonmaleficence, beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings. By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens, we propose that assuming they meet ethical requirements, there are three directions worth considering in the future development of artificial intelligence-assisted psychosis risk screening in adolescents as follows: nonperceptual real-time artificial intelligence-assisted screening, further reducing the cost of artificial intelligence-assisted screening, and improving the ease of use of artificial intelligence-assisted screening techniques and tools.
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Affiliation(s)
- Xiao-Jie Cao
- Graduate School of Education, Peking University, Beijing 100871, China
| | - Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
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