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Cai J, Chen T, Qi Y, Liu S, Chen R. Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine. Sci Rep 2025; 15:11. [PMID: 39747413 PMCID: PMC11696505 DOI: 10.1038/s41598-024-84695-4] [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: 04/25/2024] [Accepted: 12/26/2024] [Indexed: 01/04/2025] Open
Abstract
The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we proposed two automatic diagnosis systems for non-invasive diagnosis of chronic hepatitis C based on serum indices, an extreme learning machine (ELM) based auto-diagnosis method and a hybrid method using k-means clustering and ELM. The two proposed systems were used to predict the fibrosis stage and inflammatory activity grade of patients with chronic hepatitis C by analyzing their serum index observations. ELM has superiorities such as simple structure and fast calculation speed and can provide good diagnosis performance. To overcome the problem of class-imbalance, outliers and small sample size, we also proposed a method hybridizing k-means and ELM. It employed the k-means clustering to generate new robust training samples and then employed the new generated training samples to train an ELM for chronic hepatitis C diagnosis. The proposed methods were tested on 123 real clinical cases. Experimental results show that the proposed methods outperform the state-of-the-art methods for the fibrosis stage and inflammatory activity grade diagnosis tasks.
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Affiliation(s)
- Jiaxin Cai
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, 361024, China.
| | - Tingting Chen
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, 361024, China
| | - Yang Qi
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China
| | - Siyu Liu
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China
| | - Rongshang Chen
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China
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Lu MY, Chuang WL, Yu ML. The role of artificial intelligence in the management of liver diseases. Kaohsiung J Med Sci 2024; 40:962-971. [PMID: 39440678 DOI: 10.1002/kjm2.12901] [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: 09/11/2024] [Revised: 09/24/2024] [Accepted: 09/24/2024] [Indexed: 10/25/2024] Open
Abstract
Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.
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Affiliation(s)
- Ming-Ying Lu
- Division of Hepatobiliary, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Wan-Long Chuang
- Division of Hepatobiliary, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Lung Yu
- Division of Hepatobiliary, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [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: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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Ye Y, Li M, Pan Q, Fang X, Yang H, Dong B, Yang J, Zheng Y, Zhang R, Liao Z. Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer. Comput Biol Med 2024; 168:107745. [PMID: 38064851 DOI: 10.1016/j.compbiomed.2023.107745] [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: 09/17/2023] [Revised: 10/31/2023] [Accepted: 11/20/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVE We aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells. METHODS All the deubiquitinase (DUB) sequences were classified into USPs and non-USPs. Feature vectors, including 188D, n-gram, and 400D dimensions, were extracted from these sequences and subjected to binary classification via the Weka software. Next, thirty human USPs were also analyzed to identify conserved motifs and ascertained evolutionary relationships. Experimentally, more than 90 unique DUB-encoding plasmids were transfected into HeLa cell lines to assess alterations in KLF6 protein levels and to isolate a specific DUB involved in KLF6 regulation. Subsequent experiments utilized both wild-type (WT) USP26 overexpression and shRNA-mediated USP26 knockdown to examine changes in KLF6 protein levels. The half-life experiment was performed to assess the influence of USP26 on KLF6 protein stability. Immunoprecipitation was applied to confirm the USP26-KLF6 interaction, and ubiquitination assays to explore the role of USP26 in KLF6 deubiquitination. Additional cellular assays were conducted to evaluate the effects of USP26 on HeLa cell proliferation and migration. RESULTS 1. Among the extracted feature vectors of 188D, 400D, and n-gram, all 12 classifiers demonstrated excellent performance. The RandomForest classifier demonstrated superior performance in this assessment. Phylogenetic analysis of 30 human USPs revealed the presence of nine unique motifs, comprising zinc finger and ubiquitin-specific protease domains. 2. Through a systematic screening of the deubiquitinase library, USP26 was identified as the sole DUB associated with KLF6. 3. USP26 positively regulated the protein level of KLF6, as evidenced by the decrease in KLF6 protein expression upon shUSP26 knockdown in both 293T and Hela cell lines. Additionally, half-life experiments demonstrated that USP26 prolonged the stability of KLF6. 4. Immunoprecipitation experiments revealed a strong interaction between USP26 and KLF6. Notably, the functional interaction domain was mapped to amino acids 285-913 of USP26, as opposed to the 1-295 region. 5. WT USP26 was found to attenuate the ubiquitination levels of KLF6. However, the mutant USP26 abrogated its deubiquitination activity. 6. Functional biological assays demonstrated that overexpression of USP26 inhibited both proliferation and migration of HeLa cells. Conversely, knockdown of USP26 was shown to promote these oncogenic properties. CONCLUSIONS 1. At the protein sequence level, members of the USP family can be effectively differentiated from non-USP proteins. Furthermore, specific functional motifs have been identified within the sequences of human USPs. 2. The deubiquitinating enzyme USP26 has been shown to target KLF6 for deubiquitination, thereby modulating its stability. Importantly, USP26 plays a pivotal role in the modulation of proliferation and migration in cervical cancer cells.
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Affiliation(s)
- Ying Ye
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Meng Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Qilong Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Xin Fang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, 350012, China
| | - Hong Yang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Bingying Dong
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Jiaying Yang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Yuan Zheng
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Renxiang Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Zhijun Liao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
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Abd Alla MDA, Dawood RM, Rashed HAELH, El-Dessouky YM, AbuFarrag GA, Ammar IAE, Mahmoud MMAH, Salum GM, Abu-Amer MZ, Sekeen MAEH, Heggazy MMI, Altanbouly AMA, Abd El-Meguid M, El Awady MK. HCV treatment outcome depends on SNPs of IFNL3-Gene polymorphisms (rs12979860) and cirrhotic changes in liver parenchyma. Heliyon 2023; 9:e21194. [PMID: 37928048 PMCID: PMC10623284 DOI: 10.1016/j.heliyon.2023.e21194] [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: 01/31/2023] [Revised: 09/10/2023] [Accepted: 10/18/2023] [Indexed: 11/07/2023] Open
Abstract
The allelic discrimination of IFNL3-(rs12979860 C > T) polymorphism reveals ambiguous associations with the effectiveness of oral HCV treatment. Solitary intra peripheral-blood-mononuclear-cells (PBMCs) HCV-RNA antisense-strands are independently detected in naïve and experienced cases regardless of viremia or hepatic-parenchymal alterations. We examined the frequencies of IFNL3-genetic variants with chronic-HCV-induced liver changes during the sustained virologic response (SVR) by evaluating the PBMCs- HCV-PCR after oral antiviral therapy. Methods: Twelve weeks after finishing oral antiviral therapy, the effects of IFNL3-genetic variants were evaluated in three groups of patients: Group-I (n = 25) showed HCV-RNA negativity in both serum and PBMCs-, group II (n = 52) showed positivity of HCV-RNA in PBMCs, and group-III (n = 25) had positive HCV-RNA in serum. The genetic variants of the IFNL3-gene were estimated for all the enrolled cases and correlated with their hepatic image changes. Results: IFNL3-(rs12979860) genotyping in post-direct acting antivirals (DAAs) SVR and HCV-relapse revealed: a) high frequency of CC-genotype and C-allele in group I compared to group II (P < 0.005) and group III(P ≤ 0.05) when hepatic-parenchyma looks normal by ultrasound b) frequent CT-genotype and T-allele in group II compared with I(P < 0.01) and III(P < 0.05) when liver tissues are bright (early cirrhotic-changes) c) frequent TT-genotype and T-allele in group III relative to I (P < 0.05) and II (P ≤ 0.08) when liver-tissues appear coarse by ultrasound. Conclusion: Outcomes of HCV treatment depend on host IFNL3-gene polymorphism and hepatic-parenchymal changes. A high frequency of wild-CC-genotype and C-allele is observed in patients with normal hepatic parenchyma and that achieved SVR. Solitary relapse in PBMCs occurs on increasing CT-genotype frequency when liver tissues are bright. Serologic relapse is detected when TT-genotype and T-allele are dominant in association with the cirrhotic liver. Therefore, IFNL3-gene-SNP analysis as a genetic predictor in relation to ultra-sonographic hepatic-parenchymal changes could be valuable for selecting the patients with the highest priority for treatment.
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Affiliation(s)
| | - Reham M. Dawood
- Department of Microbial Biotechnology, National Research Centre, Cairo, Egypt
| | - Hassan Abd EL-Hafeth Rashed
- Department of Hepatology, Gastroenterology and Infectious Diseases, Faculty of Medicine, Al-Azhar University, Egypt
| | - Yasser Mohammed El-Dessouky
- Department of Hepatology, Gastroenterology and Infectious Diseases, Faculty of Medicine, Al-Azhar University, Egypt
| | - Galal AbdElhameed AbuFarrag
- Department of Hepatology, Gastroenterology and Infectious Diseases, Faculty of Medicine, Al-Azhar University, Egypt
| | - Islam Abdelmawla Emran Ammar
- Department of Hepatology, Gastroenterology and Infectious Diseases, Faculty of Medicine, Al-Azhar University, Egypt
| | | | - Ghada M. Salum
- Department of Microbial Biotechnology, National Research Centre, Cairo, Egypt
| | - Mohamed Zakaria Abu-Amer
- Department of Hepatology, Gastroenterology and Infectious Diseases, Faculty of Medicine, Al-Azhar University, Egypt
| | | | | | | | - Mai Abd El-Meguid
- Department of Microbial Biotechnology, National Research Centre, Cairo, Egypt
| | - Mostafa K. El Awady
- Department of Microbial Biotechnology, National Research Centre, Cairo, Egypt
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Xie D, Ying M, Lian J, Li X, Liu F, Yu X, Ni C. Serological indices and ultrasound variables in predicting the staging of hepatitis B liver fibrosis: A comparative study based on random forest algorithm and traditional methods. J Cancer Res Ther 2022; 18:2049-2057. [PMID: 36647969 DOI: 10.4103/jcrt.jcrt_1394_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Objective To compare the diagnostic efficacy of serological indices and ultrasound (US) variables in hepatitis B virus (HBV) liver fibrosis staging using random forest algorithm (RFA) and traditional methods. Methods The demographic and serological indices and US variables of patients with HBV liver fibrosis were retrospectively collected and divided into serology group, US group, and serology + US group according to the research content. RFA was used for training and validation. The diagnostic efficacy was compared to logistic regression analysis (LRA) and APRI and FIB-4 indices. Results For the serology group, the diagnostic performance of RFA was significantly higher than that of APRI and FIB-4 indices. The diagnostic accuracy of RFA in the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 79.17%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.99%, 90.69%, and 92.40%, respectively. The area under the curve (AUC) values were 0.945, 0.959, and 0.951, respectively. For the US group, there was no significant difference in diagnostic performance between RFA and LRA. The diagnostic performance of RFA in the serology + US group was significantly better than that of LRA. The diagnostic accuracy of the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 77.21%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.50%, 90.93%, and 93.38%, respectively. The AUC values were 0.948, 0.959, and 0.962, respectively. Conclusion RFA can significantly improve the diagnostic performance of HBV liver fibrosis staging. RFA based on serological indices has a good ability to predict liver fibrosis staging. RFA can help clinicians accurately judge liver fibrosis staging and reduce unnecessary biopsies.
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Affiliation(s)
- Daolin Xie
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou; Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Minghua Ying
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jingru Lian
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin Li
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoling Yu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Caifang Ni
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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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: 5] [Impact Index Per Article: 1.7] [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: 11/16/2022] [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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Lithy RM, Omar Abdelaziz A, Awad A, Ibrahim Shousha H, Omran D, Mahmoud Nabil M, Hosni Abdelmaksoud A, Mahmoud Elbaz T, Mabrouk M. Meta-learning algorithm development to predict outcomes in patients with hepatitis C virus-related hepatocellular carcinoma. Arab J Gastroenterol 2022; 23:230-234. [PMID: 36400702 DOI: 10.1016/j.ajg.2022.10.008] [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: 09/10/2020] [Revised: 06/06/2022] [Accepted: 10/17/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND STUDY AIMS Prediction of prognosis and treatment outcomes for patients with hepatocellular carcinoma (HCC) is complex for most patients. Machine learning predictive analysis can be used to explore the rich information in electronic health records to discover hidden patterns and relationships. We aimed to develop a noninvasive algorithm for predicting outcome treatment options for patients with HCC. PATIENTS AND METHODS This cross-sectional study included 1298 patients with Hepatitis C virus-related HCC attending an HCC multidisciplinary clinic, Kasr Al-Aini Hospital, Cairo University, between 2009 and 2016. Using machine learning analysis, we constructed Reduced Error Pruning (REP) decision tree algorithms and applied Auto-WEKA to select the best classifier out of 39 algorithms. RESULTS The REP-tree algorithm predicted HCC management outcomes with a recall (sensitivity) of 0.658 and a precision (specificity) of 0.653 using only routine data. 854 (65.8%) instances were correctly identified, and 444 (34.2%) instances were incorrectly classified. Out of 31 attributes, liver decompensation was selected by REP-tree as the best predictor of HCC outcome (root node). With Auto-WEKA, the random subspace classifier was chosen as the best predictive algorithm with a recall (sensitivity) of 0.750 and a precision (specificity) of 0.75. There were 974 (75%) correctly classified instances and 324 (25%) incorrectly classified instances, which was better than REP-tree. CONCLUSION Machine learning analysis explores data to discover hidden patterns and trends and enables the development of models to predict HCC treatment outcomes utilizing simple laboratory data. The random subspace classifier predicted the outcome more accurately than REP-tree.
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Affiliation(s)
- Rania M Lithy
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ashraf Omar Abdelaziz
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Abobakr Awad
- Computer Science Department, Faculty of Computers and Information, Cairo University, Cairo, Egypt
| | - Hend Ibrahim Shousha
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Dalia Omran
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohamed Mahmoud Nabil
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ahmed Hosni Abdelmaksoud
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Tamer Mahmoud Elbaz
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mahasen Mabrouk
- Endemic Medicine and Hepato-gastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
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9
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Fan G, Zhang S, Wu Q, Song Y, Jia A, Li D, Yue Y, Wang Q. A machine learning-based approach for low-density lipoprotein cholesterol calculation using age, and lipid parameters. Clin Chim Acta 2022; 535:53-60. [PMID: 35970405 DOI: 10.1016/j.cca.2022.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/05/2022] [Accepted: 08/04/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Low-density lipoprotein cholesterol (LDL-C) is a critical biomarker for cardiovascular disease. However, no consensus exists on the best method for estimating LDL-C in Chinese laboratories. This study aimed to develop a machine learning (ML) method for LDL-C estimation. METHODS An extensive data set of 111,448 samples were randomized into five equal subsets. ML-based equations were developed using age, sex, and lipid parameters based on five-fold cross-validation. The trained ML equations were externally validated in three different data sets. The performance of the ML equations was compared with the Friedewald, Martin/Hopkins, and Sampson equations. RESULTS The selected ML equations showed less bias with direct LDL-C than other LDL-C equations in the Chinese population, including those with triglycerides (TG) ≥ 400 mg / dL and LDL-C < 40 mg / dL. The performance of the ML equations was less susceptible to age. External validation showed the generalization of the ML equations. CONCLUSIONS This study highlights the potential of integrating sex, age, and lipid parameters into the ML equations to obtain a more robust and reliable LDL-C calculation.
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Affiliation(s)
- Gaowei Fan
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Shunli Zhang
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Qisheng Wu
- Division of Pathology & Laboratory Medicine, Lu Daopei Hospital, Beijing, China
| | - Yan Song
- Department of Clinical Laboratory, Beijing Shangdi Hospital, Beijing, China
| | - Anqi Jia
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Di Li
- Laboratory of Clinical Microbiology and Infectious Diseases, Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Yuhong Yue
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Qingtao Wang
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
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10
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Manduchi E, Romano JD, Moore JH. The promise of automated machine learning for the genetic analysis of complex traits. Hum Genet 2022; 141:1529-1544. [PMID: 34713318 PMCID: PMC9360157 DOI: 10.1007/s00439-021-02393-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/22/2021] [Indexed: 12/24/2022]
Abstract
The genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.
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Affiliation(s)
- Elisabetta Manduchi
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph D Romano
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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11
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Carteri RB, Grellert M, Borba DL, Marroni CA, Fernandes SA. Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases. Artif Intell Gastroenterol 2022; 3:80-87. [DOI: 10.35712/aig.v3.i3.80] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/15/2022] [Accepted: 05/08/2022] [Indexed: 02/06/2023] Open
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12
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Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13:1977-1990. [PMID: 35070002 PMCID: PMC8727218 DOI: 10.4254/wjh.v13.i12.1977] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/09/2021] [Accepted: 11/25/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.
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Affiliation(s)
- Fadl H Veerankutty
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Govind Jayan
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Manish Kumar Yadav
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Krishnan Sarojam Manoj
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Abhishek Yadav
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Sindhu Radha Sadasivan Nair
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - T U Shabeerali
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Varghese Yeldho
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Madhu Sasidharan
- Gastroenterology and Hepatology, Kerala Institute of Medical Sciences, Thiruvananthapuram 695029, India
| | - Shiraz Ahmad Rather
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
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13
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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14
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Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR, Bihorac A, Loftus TJ. Machine Learning Applications in Solid Organ Transplantation and Related Complications. Front Immunol 2021; 12:739728. [PMID: 34603324 PMCID: PMC8481939 DOI: 10.3389/fimmu.2021.739728] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
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Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Daniel Delitto
- Department of Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida Health, Gainesville, FL, United States.,Department of Orthopedics, University of Florida Health, Gainesville, FL, United States.,Department of Information Systems/Operations Management, University of Florida Health, Gainesville, FL, United States
| | - Ali Zarrinpar
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Philip A Efron
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.,Department of Computer and Information Science and Engineering University of Florida, Gainesville, FL, United States.,Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Azra Bihorac
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States.,Department of Medicine, University of Florida Health, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
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15
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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16
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey
- Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M. A. Jabbar
- Vardhaman College of Engineering, Kacharam, India
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17
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Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021; 73:2546-2563. [PMID: 33098140 DOI: 10.1002/hep.31603] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/15/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022]
Abstract
Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
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Affiliation(s)
- Joseph C Ahn
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | | | | | | | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
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18
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Hashem A, Awad A, Shousha H, Alakel W, Salama A, Awad T, Mabrouk M. Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study. Arab J Gastroenterol 2021; 22:88-92. [PMID: 33985905 DOI: 10.1016/j.ajg.2021.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 02/01/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND STUDY AIM The study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predicts the extent of liver fibrosis. PATIENTS AND METHODS This retrospective multicenter study included 69,106 patients with chronic hepatitis C planned for antiviral therapy from January 2010-December 2014 with liver biopsy results. FIB-4 and APRI scores were calculated and their performance for predicting significant liver fibrosis (F3-F4) assessed against the Metavir scoring system. ML was used for feature selection and reduction to identify the most relevant attributes (CfsSubseteval/best first) for prediction. RESULTS In this study, 57,492 (83.2%) patients were F0-F2, and 11,615 (16.8%) patients were F3-F4. The revalidation of FIB-4 and APRI showed lower accuracy and higher disagreement with the biopsy results, with AUCs of 0.68 and 0.58, respectively. FIB-4 diagnosed fewer (14%) F3-F4 patients, and the high specificity and negative predictive values of FIB-4 and APRI reflected the low prevalence of F3-F4 in the study population. Out of 15 attributes, age (>35 years), AFP (>6.5 ng/ml), and platelet count (<150,000/mm3) were the most relevant risk attributes, and patients with one or more of these risk factors were likely to be F3-F4, with a classification accuracy of ≤ 92% and receiver operating characteristics area of 0.74. CONCLUSION FIB-4 and APRI scores were not very accurate and missed diagnosing most of the F3-F4 patients. ML implementation improved medical decisions and minimized the required clinical data to three risk factors.
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Affiliation(s)
- Ahmed Hashem
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Abubakr Awad
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK
| | - Hend Shousha
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
| | - Wafaa Alakel
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt; National Hepatology and Tropical Medicine Research Institute, Ministry of Health and Population, Cairo, Egypt
| | - Ahmed Salama
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Tahany Awad
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mahasen Mabrouk
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
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19
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Rauff B, Amar A, Chudhary SA, Mahmood S, Tayyab GUN, Hanif R. Interferon-λ rs12979860 genotype association with liver fibrosis in chronic hepatitis C (CHC) patients in the Pakistani population. Arch Virol 2021; 166:1047-1056. [PMID: 33528661 DOI: 10.1007/s00705-020-04901-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/15/2020] [Indexed: 12/18/2022]
Abstract
Risk and progression of liver fibrosis and cirrhosis in chronic hepatitis C (CHC) patients is significantly influenced by host genetic factors in a polygenic manner. The rs12979860 genetic polymorphism in the interferon-λ3-interferon-λ4 (IFNL3-IFNL4) region has been found to be a major determinant of hepatic inflammatory and fibrotic progression in CHC patients of mainly Caucasian origin; however, it is not known if this association applies to other ethnicities, including Pakistani CHC patients. Here, we genotyped IFNL3-IFNL4 rs12979860 genetic variants in a sample set of 502 Pakistani patients with CHC and used logistic regression analysis to determine its association with the risk and progression of HCV-related fibrosis and cirrhosis. We demonstrate that the rs12979860 major (CC) genotype, despite not determining the risk of stage-specific hepatic fibrosis independently, is associated with a marginally significant risk of liver cirrhosis (OR: 1.64, p = 0.049) after an adjustment for age, gender, body mass index, HCV viral load, and liver enzymes. In a subgroup of CHC patients with sustained ALT levels of <60 IU/L, a more pronounced impact of the IFNL3-IFNL4 rs12979860 major (CC) genotype on advanced liver fibrosis (OR: 4.99, p = 0.017) and cirrhosis (OR: 3.34, p = 0.005) was seen. The present study suggests that IFNL3-IFNL4 rs12979860 polymorphism may also be a significant predictor of hepatic fibrosis and cirrhosis in Pakistani CHC patients, especially in those with normal or near-normal liver enzyme levels.
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Affiliation(s)
- Bisma Rauff
- Storr Liver Centre, The Westmead Institute for Medical Research, The University of Sydney and Westmead Hospital, Sydney, Australia
- Institute of Biomedical and Allied Health Sciences (IBAHS), University of Health Sciences (UHS), Lahore, Pakistan
| | - Ali Amar
- Department of Human Genetics and Molecular Biology, University of Health Sciences (UHS), Lahore, Pakistan
| | - Shafiq Ahmad Chudhary
- Institute of Biomedical and Allied Health Sciences (IBAHS), University of Health Sciences (UHS), Lahore, Pakistan
| | - Saqib Mahmood
- Institute of Biomedical and Allied Health Sciences (IBAHS), University of Health Sciences (UHS), Lahore, Pakistan
| | | | - Rumeza Hanif
- Department of Healthcare Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
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20
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Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021; 21:10. [PMID: 33407169 PMCID: PMC7788739 DOI: 10.1186/s12876-020-01585-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/16/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance. METHODS We systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95% CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities, population and AI classifiers were performed. RESULTS We included 19 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography, magnetic resonance imaging and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.71-0.85), 0.89 (0.81-0.94), 0.72 (0.58-0.83), 0.92 (0.88-0.94) and 31.58 (11.84-84.25), respectively, for cirrhosis; 0.86 (0.80-0.90), 0.87 (0.80-0.92), 0.85 (0.75-0.91), 0.88 (0.82-0.92) and 37.79 (16.01-89.19), respectively; for advanced fibrosis; and 0.86 (0.78-0.92), 0.81 (0.77-0.84), 0.88 (0.80-0.93), 0.77 (0.58-0.89) and 26.79 (14.47-49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76-1.00), 0.91 (0.78-0.97), 0.95 (0.87-0.98), 0.93 (0.80-0.98) and 191.52 (38.82-944.81), respectively, for the diagnosis of liver steatosis. CONCLUSIONS AI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice. TRIAL REGISTRATION The protocol was registered with PROSPERO (CRD42020183295).
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Affiliation(s)
| | - Roongruedee Chaiteerakij
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok, 10330 Thailand
- Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - Sombat Treeprasertsuk
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok, 10330 Thailand
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21
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Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis. J Pers Med 2020; 10:jpm10020021. [PMID: 32244292 PMCID: PMC7354442 DOI: 10.3390/jpm10020021] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/09/2020] [Accepted: 03/23/2020] [Indexed: 02/07/2023] Open
Abstract
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.
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Affiliation(s)
- Gopi Battineni
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Correspondence: ; Tel.: +39-333-172-8206
| | - Getu Gamo Sagaro
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Nalini Chinatalapudi
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Francesco Amenta
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Research Department, International Medical Radio Center Foundation (C.I.R.M.), 00144 Roma, Italy
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22
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Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology 2020; 71:1093-1105. [PMID: 31907954 DOI: 10.1002/hep.31103] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 12/05/2019] [Indexed: 12/13/2022]
Abstract
Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.
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Affiliation(s)
- Ashley Spann
- Division of Gastroenterology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Justin Kang
- Multi Organ Transplant Program, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Kymberly Watt
- Division of Gastroenterology, Mayo Clinic, Rochester, MN
| | - Bo Wang
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Anna Goldenberg
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Mamatha Bhat
- Multi Organ Transplant Program, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.,Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, ON, Canada
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23
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Abstract
Fibrosis is a highly conserved and coordinated wound healing response to injury. In the liver, injury is promoted by immune effector mechanisms that are common across various disease etiologies and even between organs such as lungs, kidneys, heart, and other organs. Thus, the liver represents a useful model to study inflammation and repair, particularly as it is frequently biopsied in clinical contexts. Currently, strong evidence implicates IFNL3/4 polymorphisms and interferon (IFN)-λ3 levels as determinants of the extent of hepatic inflammation and fibrosis in viral and nonviral liver diseases, as well as in governing the severity of nonhepatotropic viral diseases. Interestingly, IFNL3/4 polymorphisms and IFN-λ3 levels correlate with fibrosis extent in other organs such as the lung and kidney. In this review, we discuss the association between IFN-λ and tissue inflammation and fibrosis in human disease and the potential clinical utility of the findings.
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Affiliation(s)
- Mohammed Eslam
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, Australia
| | - Golo Ahlenstiel
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, Australia
- Blacktown Medical School, Western Sydney University, Blacktown, Australia
| | - Jacob George
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, Australia
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24
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Wei R, Wang J, Wang X, Xie G, Wang Y, Zhang H, Peng CY, Rajani C, Kwee S, Liu P, Jia W. Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning. EBioMedicine 2018; 35:124-132. [DOI: https:/doi.org/10.1016/j.ebiom.2018.07.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023] Open
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25
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Wei R, Wang J, Wang X, Xie G, Wang Y, Zhang H, Peng CY, Rajani C, Kwee S, Liu P, Jia W. Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning. EBioMedicine 2018; 35:124-132. [PMID: 30100397 PMCID: PMC6154783 DOI: 10.1016/j.ebiom.2018.07.041] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 07/25/2018] [Accepted: 07/30/2018] [Indexed: 12/14/2022] Open
Abstract
Clinical prediction of advanced hepatic fibrosis (HF) and cirrhosis has long been challenging due to the gold standard, liver biopsy, being an invasive approach with certain limitations. Less invasive blood test tandem with a cutting-edge machine learning algorithm shows promising diagnostic potential. In this study, we constructed and compared machine learning methods with the FIB-4 score in a discovery dataset (n = 490) of hepatitis B virus (HBV) patients. Models were validated in an independent HBV dataset (n = 86). We further employed these models on two independent hepatitis C virus (HCV) datasets (n = 254 and 230) to examine their applicability. In the discovery data, gradient boosting (GB) stably outperformed other methods as well as FIB-4 scores (p < .001) in the prediction of advanced HF and cirrhosis. In the HBV validation dataset, for classification between early and advanced HF, the area under receiver operating characteristic curves (AUROC) of GB model was 0.918, while FIB-4 was 0.841; for classification between non-cirrhosis and cirrhosis, GB showed AUROC of 0.871, while FIB-4 was 0.830. Additionally, GB-based prediction demonstrated good classification capacity on two HCV datasets while higher cutoffs for both GB and FIB-4 scores were required to achieve comparable specificity and sensitivity. Using the same parameters as FIB-4, the GB-based prediction system demonstrated steady improvements relative to FIB-4 in HBV and HCV cohorts with different cutoff values required in different etiological groups. A user-friendly web tool, LiveBoost, makes our prediction models freely accessible for further clinical studies and applications.
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Affiliation(s)
- Runmin Wei
- University of Hawaii Cancer Center, Honolulu, HI, USA; Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Jingye Wang
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Xiaoning Wang
- E-Institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201204, China
| | - Guoxiang Xie
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Yixing Wang
- E-Institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201204, China
| | - Hua Zhang
- E-Institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201204, China
| | - Cheng-Yuan Peng
- School of Medicine, China Medical University, Taichung, Taiwan; Division of Hepatogastroenterology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
| | | | - Sandi Kwee
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Ping Liu
- E-Institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201204, China.
| | - Wei Jia
- University of Hawaii Cancer Center, Honolulu, HI, USA.
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