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Suárez M, Martínez-Blanco P, Gil-Rojas S, Torres AM, Torralba-González M, Mateo J. Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance. Bioengineering (Basel) 2024; 11:762. [PMID: 39199720 PMCID: PMC11351615 DOI: 10.3390/bioengineering11080762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/19/2024] [Accepted: 07/25/2024] [Indexed: 09/01/2024] Open
Abstract
Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods.
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Affiliation(s)
- Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Pablo Martínez-Blanco
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Sergio Gil-Rojas
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Ana M. Torres
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Miguel Torralba-González
- Internal Medicine Unit, University Hospital of Guadalajara, 19002 Guadalajara, Spain
- Faculty of Medicine, Universidad de Alcalá de Henares, 28801 Alcalá de Henares, Spain
- Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
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2
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Yeslam HE, Freifrau von Maltzahn N, Nassar HM. Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review. PeerJ 2024; 12:e17793. [PMID: 39040936 PMCID: PMC11262301 DOI: 10.7717/peerj.17793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 07/01/2024] [Indexed: 07/24/2024] Open
Abstract
Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; "Artificial Intelligence", "Machine Learning", "Neural Networks", "Virtual Reality", "Digital Dentistry", "CAD/CAM", and "Restorative Dentistry". Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners' CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field.
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Affiliation(s)
- Hanin E. Yeslam
- Department of Restorative Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Hani M. Nassar
- Department of Restorative Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
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3
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Ou J, Jiang L, Bai T, Zhan P, Liu R, Xiao H. ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans. Comput Biol Med 2024; 177:108625. [PMID: 38823365 DOI: 10.1016/j.compbiomed.2024.108625] [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: 07/23/2023] [Revised: 04/15/2024] [Accepted: 05/18/2024] [Indexed: 06/03/2024]
Abstract
Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists using a slice-by-slice method. However, this process is time-consuming and error-prone, depending on the radiologist's experience. In this paper, we propose a new end-to-end automatic liver segmentation framework, named ResTransUNet, which exploits the transformer's ability to capture global context for remote interactions and spatial relationships, as well as the excellent performance of the original U-Net architecture. The main contribution of this paper lies in proposing a novel fusion network that combines Unet and Transformer architectures. In the encoding structure, a dual-path approach is utilized, where features are extracted separately using both convolutional neural networks (CNNs) and Transformer networks. Additionally, an effective feature enhancement unit is designed to transfer the global features extracted by the Transformer network to the CNN for feature enhancement. This model aims to address the drawbacks of traditional Unet-based methods, such as feature loss during encoding and poor capture of global features. Moreover, it avoids the disadvantages of pure Transformer models, which suffer from large parameter sizes and high computational complexity. The experimental results on the LiTS2017 dataset demonstrate remarkable performance for our proposed model, with Dice coefficients, volumetric overlap error (VOE), and relative volume difference (RVD) values for liver segmentation reaching 0.9535, 0.0804, and -0.0007, respectively. Furthermore, to further validate the model's generalization capability, we conducted tests on the 3Dircadb, Chaos, and Sliver07 datasets. The experimental results demonstrate that the proposed method outperforms other closely related models with higher liver segmentation accuracy. In addition, significant improvements can be achieved by applying our method when handling liver segmentation with small and discontinuous liver regions, as well as blurred liver boundaries. The code is available at the website: https://github.com/Jouiry/ResTransUNet.
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Affiliation(s)
- Jiajie Ou
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Linfeng Jiang
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China; School of Computing and College of Design and Engineering, National University of Singapore, Singapore.
| | - Ting Bai
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Peidong Zhan
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Ruihua Liu
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Hanguang Xiao
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
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4
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Hellen DJ, Fay ME, Lee DH, Klindt-Morgan C, Bennett A, Pachura KJ, Grakoui A, Huppert SS, Dawson PA, Lam WA, Karpen SJ. BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images. Am J Physiol Gastrointest Liver Physiol 2024; 327:G1-G15. [PMID: 38651949 PMCID: PMC11376979 DOI: 10.1152/ajpgi.00058.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed, error prone, and lack architectural context or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine-learning model (BiliQML) able to quantify biliary forms in the liver of anti-keratin 19 antibody-stained whole slide images. Training utilized 5,019 researcher-labeled biliary forms, which following feature selection, and algorithm optimization, generated an F score of 0.87. Application of BiliQML on seven separate cholangiopathy models [genetic (Afp-CRE;Pkd1l1null/Fl, Alb-CRE;Rbp-jkfl/fl, and Albumin-CRE;ROSANICD), surgical (bile duct ligation), toxicological (3,5-diethoxycarbonyl-1,4-dihydrocollidine), and therapeutic (Cyp2c70-/- with ileal bile acid transporter inhibition)] allowed for a means to validate the capabilities and utility of this platform. The results from BiliQML quantification revealed biological and pathological differences across these seven diverse models, indicating a highly sensitive, robust, and scalable methodology for the quantification of distinct biliary forms. BiliQML is the first comprehensive machine-learning platform for biliary form analysis, adding much-needed morphologic context to standard immunofluorescence-based histology, and provides clinical and basic science researchers with a novel tool for the characterization of cholangiopathies.NEW & NOTEWORTHY BiliQML is the first comprehensive machine-learning platform for biliary form analysis in whole slide histopathological images. This platform provides clinical and basic science researchers with a novel tool for the improved quantification and characterization of biliary tract disorders.
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Affiliation(s)
- Dominick J Hellen
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Meredith E Fay
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States
| | - David H Lee
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Caroline Klindt-Morgan
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Ashley Bennett
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Kimberly J Pachura
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Arash Grakoui
- Emory National Primate Research Center, Division of Microbiology and Immunology, Emory Vaccine Center, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Stacey S Huppert
- Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
| | - Paul A Dawson
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
| | - Wilbur A Lam
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Saul J Karpen
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States
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Hassoun S, Bruckmann C, Ciardullo S, Perseghin G, Marra F, Curto A, Arena U, Broccolo F, Di Gaudio F. NAIF: A novel artificial intelligence-based tool for accurate diagnosis of stage F3/F4 liver fibrosis in the general adult population, validated with three external datasets. Int J Med Inform 2024; 185:105373. [PMID: 38395017 DOI: 10.1016/j.ijmedinf.2024.105373] [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: 10/05/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVE The purpose of this study was to determine the effectiveness of a new AI-based tool called NAIF (NAFLD-AI-Fibrosis) in identifying individuals from the general population with advanced liver fibrosis (stage F3/F4). We compared NAIF's performance to two existing risk score calculators, aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (Fib4). METHODS To set up the algorithm for diagnosing severe liver fibrosis (defined as Fibroscan® values E ≥ 9.7 KPa), we used 19 blood biochemistry parameters and two demographic parameters in a group of 5,962 individuals from the NHANES population (2017-2020 pre-pandemic, public database). We then assessed the algorithm's performance by comparing its accuracy, precision, sensitivity, specificity, and F1 score values to those of APRI and Fib4 scoring systems. RESULTS In a kept-out sub dataset of the NHANES population, NAIF achieved a predictive precision of 72 %, a sensitivity of 61 %, and a specificity of 77 % in correctly identifying adults (aged 18-79 years) with severe liver fibrosis. Additionally, NAIF performed well when tested with two external datasets of Italian patients with a Fibroscan® score E ≥ 9.7 kPa, and with an external dataset of patients with diagnosis of severe liver fibrosis through biopsy. CONCLUSIONS The results of our study suggest that NAIF, using routinely available parameters, outperforms in sensitivity existing scoring methods (Fib4 and APRI) in diagnosing severe liver fibrosis, even when tested with external validation datasets. NAIF uses routinely available parameters, making it a promising tool for identifying individuals with advanced liver fibrosis from the general population. Word count abstract: 236.
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Affiliation(s)
- Samir Hassoun
- Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, viale Strasburgo 233, 90146 Palermo, Italy.
| | - Chiara Bruckmann
- Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, viale Strasburgo 233, 90146 Palermo, Italy.
| | - Stefano Ciardullo
- Department of Medicine and Surgery, University of Milano-Bicocca, via Modigliani 10, 20900 Monza, Italy; Department of Medicine and Rehabilitation, Policlinico di Monza, Monza, via Modigliani 10, 20900 Monza, Italy
| | - Gianluca Perseghin
- Department of Medicine and Surgery, University of Milano-Bicocca, via Modigliani 10, 20900 Monza, Italy; Department of Medicine and Rehabilitation, Policlinico di Monza, Monza, via Modigliani 10, 20900 Monza, Italy
| | - Fabio Marra
- Dipartimento di Medicina Sperimentale e Clinica, University of Florence, Largo Giovanni Alessandro Brambilla, 3, 50134 Firenze Italy
| | - Armando Curto
- Dipartimento di Medicina Sperimentale e Clinica, University of Florence, Largo Giovanni Alessandro Brambilla, 3, 50134 Firenze Italy
| | - Umberto Arena
- Dipartimento di Medicina Sperimentale e Clinica, University of Florence, Largo Giovanni Alessandro Brambilla, 3, 50134 Firenze Italy
| | - Francesco Broccolo
- Department of Experimental Medicine, University of Salento, 73100 Lecce, Italy.
| | - Francesca Di Gaudio
- Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, viale Strasburgo 233, 90146 Palermo, Italy; PROMISE-Promotion of Health, Maternal-Childhood, Internal and Specialized Medicine of Excellence G. D'Alessandro, Piazza delle Cliniche, 2, 90127 Palermo, Italy
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6
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Fang M, Fu M, Liao B, Lei X, Wu FX. Deep integrated fusion of local and global features for cervical cell classification. Comput Biol Med 2024; 171:108153. [PMID: 38364660 DOI: 10.1016/j.compbiomed.2024.108153] [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: 09/14/2023] [Revised: 02/08/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
Cervical cytology image classification is of great significance to the cervical cancer diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual transformer have been adopted as two branches to learn the features for image classification by simply adding local and global features. However, such the simple addition may not be effective to integrate these features. In this study, we explore the synergy of local and global features for cytology images for classification tasks. Specifically, we design a Deep Integrated Feature Fusion (DIFF) block to synergize local and global features of cytology images from a CNN branch and a transformer branch. Our proposed method is evaluated on three cervical cell image datasets (SIPaKMeD, CRIC, Herlev) and another large blood cell dataset BCCD for several multi-class and binary classification tasks. Experimental results demonstrate the effectiveness of the proposed method in cervical cell classification, which could assist medical specialists to better diagnose cervical cancer.
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Affiliation(s)
- Ming Fang
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada
| | - Minghan Fu
- Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, Haikou, 571158, Hainan, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, 620 West Chang'an Avenue, Xi'an, 710119, Shaanxi, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada; Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada; Department of Computer Science, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada.
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7
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Zamanian H, Shalbaf A, Zali MR, Khalaj AR, Dehghan P, Tabesh M, Hatami B, Alizadehsani R, Tan RS, Acharya UR. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107932. [PMID: 38008040 DOI: 10.1016/j.cmpb.2023.107932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND OBJECTIVES Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data. METHODS We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information". RESULTS We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches. CONCLUSION AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.
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Affiliation(s)
- H Zamanian
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - M R Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A R Khalaj
- Tehran obesity treatment center, Department of Surgery, Faculty of Medicine, Shahed University, Tehran, Iran
| | - P Dehghan
- Department of Radiology, Imaging Department, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - M Tabesh
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research, Tehran University of Medical Sciences, Tehran, Iran
| | - B Hatami
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - R Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC, Australia
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore 169609, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia; Centre for Health Research, University of Southern Queensland, Australia
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8
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Tang VH, Duong STM, Nguyen CDT, Huynh TM, Duc VT, Phan C, Le H, Bui T, Truong SQH. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison. Sci Rep 2023; 13:19559. [PMID: 37950031 PMCID: PMC10638447 DOI: 10.1038/s41598-023-46695-8] [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: 02/09/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve.
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Affiliation(s)
- Van Ha Tang
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam
| | - Soan T M Duong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam.
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam.
| | - Chanh D Tr Nguyen
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Thanh M Huynh
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Vo T Duc
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Chien Phan
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Huyen Le
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Trung Bui
- Adobe Research, San Francisco, CA, 94103, USA
| | - Steven Q H Truong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
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Iyer K, Elhabian S. Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14220:615-625. [PMID: 38659613 PMCID: PMC11036176 DOI: 10.1007/978-3-031-43907-0_59] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population. The presence of substantial non-linear variability in human anatomy often makes the traditional shape modeling process challenging. Deep learning techniques can learn complex non-linear representations of shapes and generate statistical shape models that are more faithful to the underlying population-level variability. However, existing deep learning models still have limitations and require established/optimized shape models for training. We propose Mesh2SSM, a new approach that leverages unsupervised, permutation-invariant representation learning to estimate how to deform a template point cloud to subject-specific meshes, forming a correspondence-based shape model. Mesh2SSM can also learn a population-specific template, reducing any bias due to template selection. The proposed method operates directly on meshes and is computationally efficient, making it an attractive alternative to traditional and deep learning-based SSM approaches.
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Affiliation(s)
- Krithika Iyer
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, US
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, USA
| | - Shireen Elhabian
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, US
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, USA
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10
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Khan RA, Fu M, Burbridge B, Luo Y, Wu FX. A multi-modal deep neural network for multi-class liver cancer diagnosis. Neural Netw 2023; 165:553-561. [PMID: 37354807 DOI: 10.1016/j.neunet.2023.06.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/21/2023] [Accepted: 06/07/2023] [Indexed: 06/26/2023]
Abstract
Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This study proposes a multi-modal deep neural network for multi-class malignant liver diagnosis. In parallel with the portal venous computed tomography (CT) scans, pathology data is utilized to prognosticate primary liver cancer variants and metastasis. The processed CT scans are fed to the deep dilated convolution neural network to explore salient features. The residual connections are further added to address vanishing gradient problems. Correspondingly, five pathological features are learned using a wide and deep network that gives a benefit of memorization with generalization. The down-scaled hierarchical features from CT scan and pathology data are concatenated to pass through fully connected layers for classification between liver cancer variants. In addition, the transfer learning of pre-trained deep dilated convolution layers assists in handling insufficient and imbalanced dataset issues. The fine-tuned network can predict three-class liver cancer variants with an average accuracy of 96.06% and an Area Under Curve (AUC) of 0.832. To the best of our knowledge, this is the first study to classify liver cancer variants by integrating pathology and image data, hence following the medical perspective of malignant liver diagnosis. The comparative analysis on the benchmark dataset shows that the proposed multi-modal neural network outperformed most of the liver diagnostic studies and is comparable to others.
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Affiliation(s)
- Rayyan Azam Khan
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Minghan Fu
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Brent Burbridge
- College of Medicine and Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Yigang Luo
- College of Medicine and Department of Surgery, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, Department of Computer Science and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
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11
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Manjunath RV, Ghanshala A, Kwadiki K. Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-18. [PMID: 37362702 PMCID: PMC10183675 DOI: 10.1007/s11042-023-15627-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/10/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.
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Affiliation(s)
- R. V. Manjunath
- Department of Electronics &Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore-82, India
| | | | - Karibasappa Kwadiki
- Department of CS&IT, Graphic Era Deemed to be University, Dehradun, 248002 India
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12
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Anisha A, Jiji G, Ajith Bosco Raj T. Deep feature fusion and optimized feature selection based ensemble classification of liver lesions. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2185430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Affiliation(s)
- A. Anisha
- Department of Computer Science and Engineering, St. Xavier’s Catholic College of Engineering, Nagercoil, Tamil Nadu, India
| | - G. Jiji
- Department of Electronics and Communication Engineering, Lord Jegannath College of Engineering and Technology, Nagercoil, Tamil Nadu, India
| | - T. Ajith Bosco Raj
- Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
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13
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Khan RA, Luo Y, Wu FX. Multi-level GAN based enhanced CT scans for liver cancer diagnosis. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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14
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An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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15
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Xiong M, Xu Y, Zhao Y, He S, Zhu Q, Wu Y, Hu X, Liu L. Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis. Front Oncol 2023; 13:990306. [PMID: 36874099 PMCID: PMC9978515 DOI: 10.3389/fonc.2023.990306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 02/03/2023] [Indexed: 02/18/2023] Open
Abstract
Objective To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study. Methods In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles. Results 1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer. Conclusion AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.
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Affiliation(s)
- Ming Xiong
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yaona Xu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yang Zhao
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Si He
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Qihan Zhu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yi Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaofei Hu
- Department of Nuclear Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Li Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China.,Department of Ultrasound, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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16
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Magazzù G, Zampieri G, Angione C. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods. Comput Biol Med 2022; 151:106244. [PMID: 36343407 DOI: 10.1016/j.compbiomed.2022.106244] [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: 06/15/2022] [Revised: 10/07/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far. METHODS Here, we propose and implement an interpretable multi-omics pipeline that combines mechanistic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma. RESULTS AND CONCLUSIONS While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics.
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Affiliation(s)
- Giuseppe Magazzù
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom
| | - Guido Zampieri
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Department of Biology, University of Padova, Padova, Italy
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Centre for Digital Innovation, Teesside University, Middlesbrough, England, United Kingdom; National Horizons Centre, Teesside University, Darlington, England, United Kingdom.
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17
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Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis. REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE 2022. [DOI: 10.1007/s40883-022-00273-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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18
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Adeoye J, Akinshipo A, Thomson P, Su YX. Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements. J Glob Health 2022; 12:03017. [PMID: 35493779 PMCID: PMC9022723 DOI: 10.7189/jogh.12.03017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- John Adeoye
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Oral Cancer Research Theme, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Abdulwarith Akinshipo
- Department of Oral and Maxillofacial Pathology and Biology, Faculty of Dentistry, University of Lagos, Lagos, Nigeria
| | - Peter Thomson
- College of Medicine and Dentistry, James Cook University, Cairns, Queensland, Australia
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Oral Cancer Research Theme, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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19
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Liver Disease Detection – Evaluation of Machine Learning Algorithms Performances with Optimal Thresholds. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2022. [DOI: 10.4018/ijhisi.299956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.
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