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Meroueh C, Warasnhe K, Tizhoosh HR, Shah VH, Ibrahim SH. Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials. Hepatology 2024:01515467-990000000-00815. [PMID: 38517078 DOI: 10.1097/hep.0000000000000866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.
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Affiliation(s)
- Chady Meroueh
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Khaled Warasnhe
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hamid R Tizhoosh
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Samar H Ibrahim
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
- Division of Pediatric Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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2
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Tsai HW, Chiou CY, Yang WJ, Hsieh TA, Chen CY, Hsu CW, Lin YJ, Hsieh ME, Yeh MM, Chen CC, Shen MR, Chung PC. Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:261-270. [PMID: 38766544 PMCID: PMC11100940 DOI: 10.1109/ojemb.2024.3379479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/18/2023] [Accepted: 03/12/2024] [Indexed: 05/22/2024] Open
Abstract
Goal: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. Methods: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. Results: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). Conclusions: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.
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Affiliation(s)
- Hung-Wen Tsai
- Department of Pathology, National Cheng Kung University Hospital, College of MedicineNational Cheng Kung UniversityTainan701Taiwan
| | - Chien-Yu Chiou
- Department of Electrical EngineeringNational Cheng Kung UniversityTainan701Taiwan
| | - Wei-Jong Yang
- Department of Artificial Intelligence and Computer EngineeringNational Chin-Yi University of TechnologyTaichung411030Taiwan
| | - Tsan-An Hsieh
- Institute of Computer and Communication EngineeringNational Cheng Kung UniversityTainan701Taiwan
| | - Cheng-Yi Chen
- Department of Cell Biology and AnatomyCollege of MedicineNational Cheng Kung UniversityTainan701Taiwan
| | - Che-Wei Hsu
- Department of Pathology, National Cheng Kung University Hospital, College of MedicineNational Cheng Kung UniversityTainan701Taiwan
| | - Yih-Jyh Lin
- Department of Surgery, National Cheng Kung University Hospital, College of MedicineNational Cheng Kung UniversityTainan701Taiwan
| | - Min-En Hsieh
- Department of Electrical EngineeringNational Cheng Kung UniversityTainan701Taiwan
| | - Matthew M. Yeh
- Department of Laboratory Medicine and PathologyUniversity of Washington School of MedicineSeattleWA98195USA
| | - Chin-Chun Chen
- Department of StatisticsNational Cheng Kung UniversityTainan701Taiwan
| | - Meng-Ru Shen
- Department of Pharmacology, National Cheng Kung University Hospital, College of MedicineNational Cheng Kung UniversityTainan701Taiwan
| | - Pau-Choo Chung
- Department of Electrical EngineeringNational Cheng Kung UniversityTainan701Taiwan
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3
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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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4
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Zheng TL, Sha JC, Deng Q, Geng S, Xiao SY, Yang WJ, Byrne CD, Targher G, Li YY, Wang XX, Wu D, Zheng MH. Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning. Liver Int 2024; 44:330-343. [PMID: 38014574 DOI: 10.1111/liv.15799] [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: 08/21/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/29/2023]
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.
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Affiliation(s)
- Tian-Lei Zheng
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jun-Cheng Sha
- Department of Interventional Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Qian Deng
- Department of Histopathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shu-Yuan Xiao
- Department of Pathology, University of Chicago Medicine, Chicago, Illinois, USA
| | - Wen-Jun Yang
- Department of Pathology, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Christopher D Byrne
- Southampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, and University of Southampton, Southampton, UK
| | - Giovanni Targher
- Department of Medicine, University of Verona, Verona, Italy
- IRCSS Sacro Cuore - Don Calabria Hospital, Negrar di Valpolicella, Italy
| | - Yang-Yang Li
- Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiang-Xue Wang
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Di Wu
- Department of Pathology, Xuzhou Central Hospital, Xuzhou, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
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5
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Sanyal AJ, Jha P, Kleiner DE. Digital pathology for nonalcoholic steatohepatitis assessment. Nat Rev Gastroenterol Hepatol 2024; 21:57-69. [PMID: 37789057 DOI: 10.1038/s41575-023-00843-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/23/2023] [Indexed: 10/05/2023]
Abstract
Histological assessment of nonalcoholic fatty liver disease (NAFLD) has anchored knowledge development about the phenotypes of the condition, their natural history and their clinical course. This fact has led to the use of histological assessment as a reference standard for the evaluation of efficacy of drug interventions for nonalcoholic steatohepatitis (NASH) - the more histologically active form of NAFLD. However, certain limitations of conventional histological assessment systems pose challenges in drug development. These limitations have spurred intense scientific and commercial development of machine learning and digital approaches towards the assessment of liver histology in patients with NAFLD. This research field remains an area in rapid evolution. In this Perspective article, we summarize the current conventional assessment of NASH and its limitations, the use of specific digital approaches for histological assessment, and their application to the study of NASH and its response to therapy. Although this is not a comprehensive review, the leading tools currently used to assess therapeutic efficacy in drug development are specifically discussed. The potential translation of these approaches to support routine clinical assessment of NAFLD and an agenda for future research are also discussed.
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Affiliation(s)
- Arun J Sanyal
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.
| | - Prakash Jha
- Food and Drug Administration, Silver Spring, MD, USA
| | - David E Kleiner
- Post-Mortem Section Laboratory of Pathology Center for Cancer Research National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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6
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Naik SN, Forlano R, Manousou P, Goldin R, Angelini ED. Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning. BIOLOGICAL IMAGING 2023; 3:e17. [PMID: 38510166 PMCID: PMC10951930 DOI: 10.1017/s2633903x23000144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/08/2023] [Accepted: 06/23/2023] [Indexed: 03/22/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of , an F1 score of and an AUC of . These results set new state-of-the-art benchmarks for this application.
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Affiliation(s)
- Sneha N. Naik
- ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, United Kingdom
- Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Roberta Forlano
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
| | - Pinelopi Manousou
- Department of Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Robert Goldin
- Section for Pathology, Imperial College, London, United Kingdom
| | - Elsa D. Angelini
- ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, United Kingdom
- Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
- Telecom Paris, Institut Polytechnique de Paris, LTCI, Palaiseau, France
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7
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Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort. Int J Med Inform 2023; 170:104932. [PMID: 36459836 DOI: 10.1016/j.ijmedinf.2022.104932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population. MATERIALS AND METHODS Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population). RESULTS For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs. CONCLUSIONS We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis.
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8
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Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies. Sci Rep 2022; 12:19236. [PMID: 36357500 PMCID: PMC9649648 DOI: 10.1038/s41598-022-23905-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advanced form non-alcoholic steatohepatitis (NASH), and ultimately to cirrhosis or liver cancer. The current gold standard for diagnosis and assessment of NAFLD/NASH is liver biopsy followed by microscopic analysis by a pathologist. The Kleiner score is frequently used for a semi-quantitative assessment of disease progression. In this scoring system the features of active injury (steatosis, inflammation, and ballooning) and a separated fibrosis score are quantified. The procedure is time consuming for pathologists, scores have limited resolution and are subject to variation. We developed an automated deep learning method that provides full reproducibility and higher resolution. The system was established with 296 human liver biopsies and tested on 171 human liver biopsies with pathologist ground truth scores. The method is inspired by the way pathologist's analyze liver biopsies. First, the biopsies are analyzed microscopically for the relevant histopathological features. Subsequently, histopathological features are aggregated to a per-biopsy score. Scores are in the identical numeric range as the pathologist's ballooning, inflammation, steatosis, and fibrosis scores, but on a continuous scale. Resulting scores followed a pathologist's ground truth (quadratic weighted Cohen's κ on the test set: for steatosis 0.66, for inflammation 0.24, for ballooning 0.43, for fibrosis 0.62, and for the NAFLD activity score (NAS) 0.52. Mean absolute errors on a test set: for steatosis 0.29, for inflammation 0.53, for ballooning 0.61, for fibrosis 0.78, and for the NAS 0.77).
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9
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Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2022; 23:387-400. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 10/20/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.
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Affiliation(s)
- Yifang Li
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Xuetao Wang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jun Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Shanshan Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jian Jiao
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China.
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10
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Gunasekharan A, Jiang J, Nickerson A, Jalil S, Mumtaz K. Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis. Artif Intell Gastroenterol 2022; 3:46-53. [DOI: 10.35712/aig.v3.i2.46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/18/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide. It is critical to develop reliable methods of predicting progression to fibrosis, cirrhosis, and decompensated liver disease. Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data. Artificial intelligence (AI) provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis, and to identify patients at high risk for disease progression. We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis. Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis. We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction (MELD-Na score, liver biopsy, FIB-4 score, and biomarkers). They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy, for which there are currently no established prediction tools. These studies show that AI could be a helpful adjunct to existing techniques for diagnosing, monitoring, and treating both NAFLD and viral hepatitis.
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Affiliation(s)
| | - Joanna Jiang
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Ashley Nickerson
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Sajid Jalil
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Khalid Mumtaz
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
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11
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Abedin N, Tannapfel A, Wild PJ, Tischoff I. [Histopathological evaluation of nonalcoholic fatty liver disease : Automated evaluation of liver biopsies]. DER PATHOLOGE 2022; 43:157-166. [PMID: 35171314 DOI: 10.1007/s00292-022-01052-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is one of the most frequent chronic liver diseases with a rising incidence in industrial countries. This is accompanied by an increased prevalence for NAFLD-associated liver cirrhosis and an increased risk for developing hepatocellular carcinoma. The current gold standard in the diagnostics is a liver biopsy. The histopathological evaluation is performed through semiquantitative scoring. To optimize the standardization and quantification of the existing scoring systems, in the coming years procedures with artificial intelligence, such as deep learning models could be used. Fields of application could be the supplementation of conventional histopathological diagnostics, the identification of new predictive parameters for estimating the prognosis and the prediction of a possible response to treatment.
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Affiliation(s)
- Nada Abedin
- Medizinische Klinik 1, Universitätsklinikum Frankfurt, Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Deutschland.
| | - Andrea Tannapfel
- Institut für Pathologie, Ruhr-Universität Bochum, Bochum, Deutschland
| | - Peter J Wild
- Dr. Senckenbergisches Institut für Pathologie, Universitätsklinikum Frankfurt, Goethe-Universität, Frankfurt am Main, Deutschland
| | - Iris Tischoff
- Institut für Pathologie, Ruhr-Universität Bochum, Bochum, Deutschland
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12
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Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100443. [PMID: 35243281 PMCID: PMC8867112 DOI: 10.1016/j.jhepr.2022.100443] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/26/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022]
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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Sorino P, Campanella A, Bonfiglio C, Mirizzi A, Franco I, Bianco A, Caruso MG, Misciagna G, Aballay LR, Buongiorno C, Liuzzi R, Cisternino AM, Notarnicola M, Chiloiro M, Fallucchi F, Pascoschi G, Osella AR. Development and validation of a neural network for NAFLD diagnosis. Sci Rep 2021; 11:20240. [PMID: 34642390 PMCID: PMC8511336 DOI: 10.1038/s41598-021-99400-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/24/2021] [Indexed: 12/18/2022] Open
Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) affects about 20–30% of the adult population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Liver ultrasound (US) is widely used as a noninvasive method to diagnose NAFLD. However, the intensive use of US is not cost-effective and increases the burden on the healthcare system. Electronic medical records facilitate large-scale epidemiological studies and, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. Our goal was to develop and validate a simple Neural Network (NN)-based web app that could be used to predict NAFLD particularly its absence. The study included 2970 subjects; training and testing of the neural network using a train–test-split approach was done on 2869 of them. From another population consisting of 2301 subjects, a further 100 subjects were randomly extracted to test the web app. A search was made to find the best parameters for the NN and then this NN was exported for incorporation into a local web app. The percentage of accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall and f1-score were verified. After that, Explainability (XAI) was analyzed to understand the diagnostic reasoning of the NN. Finally, in the local web app, the specificity and sensitivity values were checked. The NN achieved a percentage of accuracy during testing of 77.0%, with an area under the ROC curve value of 0.82. Thus, in the web app the NN evidenced to achieve good results, with a specificity of 1.00 and sensitivity of 0.73. The described approach can be used to support NAFLD diagnosis, reducing healthcare costs. The NN-based web app is easy to apply and the required parameters are easily found in healthcare databases.
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Affiliation(s)
- Paolo Sorino
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Angelo Campanella
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Caterina Bonfiglio
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Antonella Mirizzi
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Isabella Franco
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Antonella Bianco
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Maria Gabriella Caruso
- Laboratory of Nutritional Biochemistry, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Giovanni Misciagna
- Scientific and Ethical Committee, Polyclinic Hospital, University of Bari, Piazza Giulio Cesare, 11, 70124, Bari, BA, Italy
| | - Laura R Aballay
- Human Nutrition Research Center (CenINH), School of Nutrition, Faculty of Medical Sciences, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Claudia Buongiorno
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Rosalba Liuzzi
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Anna Maria Cisternino
- Clinical Nutrition Outpatient Clinic, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Maria Notarnicola
- Laboratory of Nutritional Biochemistry, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy
| | - Marisa Chiloiro
- San Giacomo Hospital, Largo S. Veneziani, 21, 70043, Monopoli, BA, Italy
| | - Francesca Fallucchi
- Department of Engineering Sciences, Guglielmo Marconi University, Via plinio 44, 00193, Rome, Italy
| | - Giovanni Pascoschi
- Department of Electrical and Information Engineering, Polytechnic of Bari, Via Re David, 200, 70125, Bari, BA, Italy
| | - Alberto Rubén Osella
- Laboratory of Epidemiology and Biostatistics, National Institute of Gastroenterology, "S de Bellis" Research Hospital, Via Turi 27, 70013, Castellana Grotte, BA, Italy.
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