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Pyo JH, Cho SJ, Choi SC, Jee JH, Yun J, Hwang JA, Park G, Kim K, Kang W, Kang M, Byun YH. Diagnostic performance of quantitative ultrasonography for hepatic steatosis in a health screening program: a prospective single-center study. Ultrasonography 2024; 43:250-262. [PMID: 38898634 PMCID: PMC11222130 DOI: 10.14366/usg.24040] [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: 03/14/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) as the reference standard. METHODS This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. RESULTS TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). CONCLUSION QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.
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Affiliation(s)
- Jeung Hui Pyo
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Soo Jin Cho
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung Chul Choi
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae Hwan Jee
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeeyeong Yun
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Ah Hwang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Goeun Park
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
| | - Kyunga Kim
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea
| | - Wonseok Kang
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Mira Kang
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea
- Digital Transformation Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Young hye Byun
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Punn NS, Patel B, Banerjee I. Liver fibrosis classification from ultrasound using machine learning: a systematic literature review. Abdom Radiol (NY) 2024; 49:69-80. [PMID: 37950068 DOI: 10.1007/s00261-023-04081-y] [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: 04/12/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with advancements in medical technology and increasing awareness of potential complications, the reliance on liver biopsy has diminished. Ultrasound is gaining popularity due to its wider availability and cost-effectiveness. This study examined the machine learning / deep learning (ML/DL) models for non-invasive liver fibrosis classification from ultrasound. METHODS Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, we searched five academic databases using the query. We defined population, intervention, comparison, outcomes, and study design (PICOS) framework for the inclusion. Furthermore, Joana Briggs Institute (JBI) checklist for analytical cross-sectional studies is used for quality assessment. RESULTS Among the 188 screened studies, 17 studies are selected. The methods are categorized as off-the-shelf (OTS), attention, generative, and ensemble classifiers. Most studies used OTS classifiers that combined pre-trained ML/DL methods with radiomics features to determine fibrosis staging. Although machine learning shows potential for fibrosis classification, there are limited external comparisons of interventions and prospective clinical trials, which limits their applicability. CONCLUSION With the recent success of ML/DL toward biomedical image analysis, automated solutions using ultrasound are developed for predicting liver diseases. However, their applicability is bounded by the limited and imbalanced retrospective studies having high heterogeneity. This challenge could be addressed by generating a standard protocol for study design by selecting appropriate population, interventions, outcomes, and comparison.
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Affiliation(s)
| | - Bhavik Patel
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
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Zhang XY, Wei Q, Wu GG, Tang Q, Pan XF, Chen GQ, Zhang D, Dietrich CF, Cui XW. Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review. Front Oncol 2023; 13:1197447. [PMID: 37333814 PMCID: PMC10272784 DOI: 10.3389/fonc.2023.1197447] [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: 03/31/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.
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Affiliation(s)
- Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wei
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ge-Ge Wu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Tang
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Di Zhang
- Department of Medical Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | | | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Lei P, Hu N, Wu Y, Tang M, Lin C, Kong L, Zhang L, Luo P, Chan LW. Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases. IRADIOLOGY 2023; 1:167-189. [DOI: 10.1002/ird3.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/18/2023] [Indexed: 01/04/2025]
Abstract
AbstractThe liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.
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Affiliation(s)
- Pinggui Lei
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
- School of Public Health Guizhou Medical University Guiyang Guizhou China
| | - Na Hu
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Yuhui Wu
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Maowen Tang
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Chong Lin
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Luoyi Kong
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| | - Lingfeng Zhang
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| | - Peng Luo
- School of Public Health Guizhou Medical University Guiyang Guizhou China
| | - Lawrence Wing‐Chi Chan
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
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Gheorghe EC, Nicolau C, Kamal A, Udristoiu A, Gruionu L, Saftoiu A. Artificial Intelligence (AI)-Enhanced Ultrasound Techniques Used in Non-Alcoholic Fatty Liver Disease: Are They Ready for Prime Time? APPLIED SCIENCES 2023; 13:5080. [DOI: 10.3390/app13085080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease, affecting approximately 2 billion individuals worldwide with a spectrum that can range from simple steatosis to cirrhosis. Typically, the diagnosis of NAFLD is based on imaging studies, but the gold standard remains liver biopsies. Hence, the use of artificial intelligence (AI) in this field, which has recently undergone rapid development in various aspects of medicine, has the potential to accurately diagnose NAFLD and steatohepatitis (NASH). This paper provides an overview of the latest research that employs AI for the diagnosis and staging of NAFLD, as well as applications for future developments in this field.
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Affiliation(s)
- Elena Codruta Gheorghe
- Department of Family Medicine, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
| | - Carmen Nicolau
- Lotus Image Medical Center, ActaMedica SRL Târgu Mureș, 540084 Târgu Mureș, Romania
| | - Adina Kamal
- Department of Internal Medicine, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
| | - Anca Udristoiu
- Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania
| | - Lucian Gruionu
- Faculty of Mechanics, University of Craiova, 200512 Craiova, Romania
| | - Adrian Saftoiu
- Department of Gastroenterology and Hepatology, University of Medicine and Pharmacy Carol Davila Bucharest, 050474 Bucharest, Romania
- Department of Gastroenterology, Ponderas Academic Hospital, 014142 Bucharest, Romania
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Kamada Y, Nakamura T, Isobe S, Hosono K, Suama Y, Ohtakaki Y, Nauchi A, Yasuda N, Mitsuta S, Miura K, Yamamoto T, Hosono T, Yoshida A, Kawanishi I, Fukushima H, Kinoshita M, Umeda A, Kinoshita Y, Fukami K, Miyawaki T, Fujii H, Yoshida Y, Kawanaka M, Hyogo H, Morishita A, Hayashi H, Tobita H, Tomita K, Ikegami T, Takahashi H, Yoneda M, Jun DW, Sumida Y, Okanoue T, Nakajima A. SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT Forum. J Gastroenterol 2023; 58:79-97. [PMID: 36469127 PMCID: PMC9735102 DOI: 10.1007/s00535-022-01932-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/12/2022] [Indexed: 12/11/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD can progress to liver cirrhosis and hepatocellular carcinoma (HCC). Recently, the prognosis of NAFLD/NASH has been reported to be dependent on liver fibrosis degree. Liver biopsy remains the gold standard, but it has several issues that must be addressed, including its invasiveness, cost, and inter-observer diagnosis variability. To solve these issues, a variety of noninvasive tests (NITs) have been in development for the assessment of NAFLD progression, including blood biomarkers and imaging methods, although the use of NITs varies around the world. The aim of the Japan NASH NIT (JANIT) Forum organized in 2020 is to advance the development of various NITs to assess disease severity and/or response to treatment in NAFLD patients from a scientific perspective through multi-stakeholder dialogue with open innovation, including clinicians with expertise in NAFLD/NASH, companies that develop medical devices and biomarkers, and professionals in the pharmaceutical industry. In addition to conventional NITs, artificial intelligence will soon be deployed in many areas of the NAFLD landscape. To discuss the characteristics of each NIT, we conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis in this study with the 36 JANIT Forum members (16 physicians and 20 company representatives). Based on this SWOT analysis, the JANIT Forum identified currently available NITs able to accurately select NAFLD patients at high risk of NASH for HCC surveillance/therapeutic intervention and evaluate the effectiveness of therapeutic interventions.
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Affiliation(s)
- Yoshihiro Kamada
- Department of Advanced Metabolic Hepatology, Osaka University Graduate School of Medicine, 1-7, Yamadaoka, Suita, Osaka, 565-0871 Japan
| | - Takahiro Nakamura
- Medicine Division, Nippon Boehringer Ingelheim Co., Ltd., 2-1-1, Osaki, Shinagawa-Ku, Tokyo, 141-6017 Japan
| | - Satoko Isobe
- FibroScan Division, Integral Corporation, 2-25-2, Kamiosaki, Shinagawa-Ku, Tokyo, 141-0021 Japan
| | - Kumiko Hosono
- Immunology, Hepatology & Dermatology Medical Franchise Dept., Medical Division, Novartis Pharma K.K., 1-23-1, Toranomon, Minato-Ku, Tokyo, 105-6333 Japan
| | - Yukiko Suama
- Medical Information Services, Institute of Immunology Co., Ltd., 1-1-10, Koraku, Bunkyo-Ku, Tokyo, 112-0004 Japan
| | - Yukie Ohtakaki
- Product Development 1St Group, Product Development Dept., Fujirebio Inc., 2-1-1, Nishishinjuku, Shinjuku-Ku, Tokyo, 163-0410 Japan
| | - Arihito Nauchi
- Academic Department, GE Healthcare Japan, 4-7-127, Asahigaoka, Hino, Tokyo, 191-8503 Japan
| | - Naoto Yasuda
- Ultrasound Business Area, Siemens Healthcare KK, 1-11-1, Osaki, Shinagawa-Ku, Tokyo, 141-8644 Japan
| | - Soh Mitsuta
- FibroScan Division, Integral Corporation, 2-25-2, Kamiosaki, Shinagawa-Ku, Tokyo, 141-0021 Japan
| | - Kouichi Miura
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, 3311-1, Yakushiji, Shimotsuke, Tochigi, 329-0498 Japan
| | - Takuma Yamamoto
- Cardiovascular and Diabetes, Product Marketing Department, Kowa Company, Ltd., 3-4-10, Nihonbashi Honcho, Chuo-Ku, Tokyo, 103-0023 Japan
| | - Tatsunori Hosono
- Clinical Development & Operations Japan, Nippon Boehringer Ingelheim Co., Ltd., 2-1-1, Osaki, Shinagawa-Ku, Tokyo, 141-6017 Japan
| | - Akihiro Yoshida
- Medical Affairs Department, Kowa Company, Ltd., 3-4-14, Nihonbashi Honcho, Chuo-Ku, Tokyo, 103-8433 Japan
| | - Ippei Kawanishi
- R&D Planning Department, EA Pharma Co., Ltd., 2-1-1, Irifune, Chuo-Ku, Tokyo, 104-0042 Japan
| | - Hideaki Fukushima
- Diagnostics Business Area, Siemens Healthcare Diagnostics KK, 1-11-1, Osaki, Shinagawa-Ku, Tokyo, 141-8673 Japan
| | - Masao Kinoshita
- Marketing Dep. H.U. Frontier, Inc., Shinjuku Mitsui Building, 2-1-1, Nishishinjuku, Shinjuku-Ku, Tokyo, 163-0408 Japan
| | - Atsushi Umeda
- Clinical Development Dept, EA Pharma Co., Ltd., 2-1-1, Irifune, Chuo-Ku, Tokyo, 104-0042 Japan
| | - Yuichi Kinoshita
- Global Drug Development Division, Novartis Pharma KK, 1-23-1, Toranomon, Minato-Ku, Tokyo, 105-6333 Japan
| | - Kana Fukami
- 2Nd Product Planning Dept, 2Nd Product Planning Division, Fujirebio Inc, 2-1-1, Nishishinjuku, Shinjuku-Ku, Tokyo, 163-0410 Japan
| | - Toshio Miyawaki
- Medical Information Services, Institute of Immunology Co., Ltd., 1-1-10, Koraku, Bunkyo-Ku, Tokyo, 112-0004 Japan
| | - Hideki Fujii
- Departments of Hepatology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-Machi, Abeno-Ku, Osaka, Osaka 545-8585 Japan
| | - Yuichi Yoshida
- Department of Gastroenterology and Hepatology, Suita Municipal Hospital, 5-7, Kishibe Shinmachi, Suita, Osaka 564-8567 Japan
| | - Miwa Kawanaka
- Department of General Internal Medicine2, Kawasaki Medical School, Kawasaki Medical Center, 2-6-1, Nakasange, Kita-Ku, Okayama, Okayama 700-8505 Japan
| | - Hideyuki Hyogo
- Department of Gastroenterology, JA Hiroshima Kouseiren General Hospital, 1-3-3, Jigozen, Hatsukaichi, Hiroshima 738-8503 Japan ,Hyogo Life Care Clinic Hiroshima, 6-34-1, Enkobashi-Cho, Minami-Ku, Hiroshima, Hiroshima 732-0823 Japan
| | - Asahiro Morishita
- Department of Gastroenterology and Neurology, Faculty of Medicine, Kagawa University, 1750-1, Oaza Ikenobe, Miki-Cho, Kita-Gun, Kagawa 761-0793 Japan
| | - Hideki Hayashi
- Department of Gastroenterology and Hepatology, Gifu Municipal Hospital, 7-1, Kashima-Cho, Gifu, Gifu 500-8513 Japan
| | - Hiroshi Tobita
- Division of Hepatology, Shimane University Hospital, 89-1, Enya-Cho, Izumo, Shimane 693-8501 Japan
| | - Kengo Tomita
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Defense Medical College, 3-2, Namiki, Tokorozawa, Saitama 359-8513 Japan
| | - Tadashi Ikegami
- Division of Gastroenterology and Hepatology, Tokyo Medical University Ibaraki Medical Center, 3-20-1, Chuo, Ami-Machi, Inashiki-Gun, Ibaraki, 300-0395 Japan
| | - Hirokazu Takahashi
- Liver Center, Faculty of Medicine, Saga University Hospital, Saga University, 5-1-1, Nabeshima, Saga, Saga 849-8501 Japan
| | - Masato Yoneda
- Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine Graduate School of Medicine, 3-9, Fukuura, Kanazawa-Ku, Yokohama, Kanagawa 236-0004 Japan
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul, 04763 Korea
| | - Yoshio Sumida
- Division of Hepatology and Pancreatology, Department of Internal Medicine, Aichi Medical University, 21 Yazako Karimata, Nagakute, Aichi, 480-1195, Japan.
| | - Takeshi Okanoue
- Department of Gastroenterology & Hepatology, Saiseikai Suita Hospital, Osaka, 1-2, Kawazono-Cho, Suita, Osaka 564-0013 Japan
| | - Atsushi Nakajima
- Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine Graduate School of Medicine, 3-9, Fukuura, Kanazawa-Ku, Yokohama, Kanagawa 236-0004 Japan
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Fujita Y, Ishihara K, Nakata K, Hamamoto Y, Segawa M, Sakaida I, Mitani Y, Terai S. Weakly Supervised Multiple Instance Learning for Liver Cirrhosis Classification using Ultrasound Images. 2022 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATICS AND BIOMEDICAL SCIENCE (ICIIBMS) 2022:225-229. [DOI: 10.1109/iciibms55689.2022.9971604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
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Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol 2022; 28:3398-3409. [PMID: 36158262 PMCID: PMC9346461 DOI: 10.3748/wjg.v28.i27.3398] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/18/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.
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Affiliation(s)
- Liu-Liu Cao
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Mei Peng
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xiang Xie
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
| | - Shu-Yan Huang
- Department of Medical Ultrasound, The First People's Hospital of Huaihua, Huaihua 418000, Hunan Province, China
| | - Jia-Yu Wang
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3626, Switzerland
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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: 15] [Impact Index Per Article: 5.0] [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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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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Anteby R, Klang E, Horesh N, Nachmany I, Shimon O, Barash Y, Kopylov U, Soffer S. Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver Int 2021; 41:2269-2278. [PMID: 34008300 DOI: 10.1111/liv.14966] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/23/2021] [Accepted: 05/13/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND AIMS While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging. METHODS Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were "liver or hepatic," "fibrosis or cirrhosis," and "neural or DL networks." Risk of bias and applicability were evaluated using the QUADAS-2 tool. RESULTS Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the "ground truth" reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability. CONCLUSIONS Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.
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Affiliation(s)
- Roi Anteby
- School of Public Health, Harvard University, Boston, MA, USA
| | - Eyal Klang
- Department of Population Health Science and Policy, Institute for Healthcare Delivery Science, New York, NY, USA.,Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.,Deep Vision Lab, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel
| | - Nir Horesh
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.,Department of Surgery and Transplantation B, Sheba Medical Center, Tel Hashomer, Israel
| | - Ido Nachmany
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.,Department of Surgery and Transplantation B, Sheba Medical Center, Tel Hashomer, Israel
| | - Orit Shimon
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.,Department of Anesthesia, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| | - Yiftach Barash
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.,Deep Vision Lab, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel
| | - Uri Kopylov
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.,Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel
| | - Shelly Soffer
- Deep Vision Lab, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Internal Medicine B, Assuta Medical Center, Ashdod, Israel.,Ben-Gurion University of the Negev, Be'er Sheva, Israel
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12
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Hogan DE, Ma M, Kadosh D, Menon A, Chin K, Swaminath A. Endo-hepatology: An emerging field. World J Gastrointest Endosc 2021; 13:296-301. [PMID: 34512877 PMCID: PMC8394184 DOI: 10.4253/wjge.v13.i8.296] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/13/2021] [Accepted: 07/16/2021] [Indexed: 02/06/2023] Open
Abstract
Gastroenterologists have long been spearheading the care of patients with various forms of liver disease. The diagnosis and management of liver disease has traditionally been a combination of clinical, laboratory, and imaging findings coupled with percutaneous and intravascular procedures with endoscopy largely limited to screening for and therapy of esophageal and gastric varices. As the applications of diagnostic and therapeutic endoscopic ultrasound (EUS) have evolved, it has found a particular niche within hepatology now coined endo-hepatology. Here we discuss several EUS-guided procedures such as liver biopsy, shear wave elastography, direct portal pressure measurement, paracentesis, as well as EUS-guided therapies for variceal hemorrhage.
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Affiliation(s)
- Daniel E Hogan
- Division of Gastroenterology, Lenox Hill Hospital, Northwell Health, New York, NY 10075, United States
| | - Michael Ma
- Division of Gastroenterology, Lenox Hill Hospital, Northwell Health, New York, NY 10075, United States
| | - David Kadosh
- Department of Internal Medicine, Lenox Hill Hospital, Northwell Health, New York, NY 10075, United States
| | - Alisha Menon
- Department of Internal Medicine, Lenox Hill Hospital, Northwell Health, New York, NY 10075, United States
| | - Kana Chin
- Department of Internal Medicine, Long Island Jewish Forest Hills, Northwell Health, Forest Hills, NY 11375, United States
| | - Arun Swaminath
- Division of Gastroenterology, Lenox Hill Hospital, Northwell Health, New York, NY 10075, United States
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13
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Utility of convolutional neural network-based algorithm in medical images for liver fibrosis assessment. Chin Med J (Engl) 2021; 134:2255-2257. [PMID: 34553704 PMCID: PMC8478382 DOI: 10.1097/cm9.0000000000001536] [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: 11/25/2022] Open
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14
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Brattain LJ, Ozturk A, Telfer BA, Dhyani M, Grajo JR, Samir AE. Image Processing Pipeline for Liver Fibrosis Classification Using Ultrasound Shear Wave Elastography. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:2667-2676. [PMID: 32622685 PMCID: PMC7483774 DOI: 10.1016/j.ultrasmedbio.2020.05.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 05/28/2023]
Abstract
The purpose of this study was to develop an automated method for classifying liver fibrosis stage ≥F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference approach consists of manually selecting a region of interest from each of eight or more SWE images, computing the mean tissue stiffness within each of the regions of interest and computing a resulting stiffness value as the median of the means. The 527-subject database consisted of 5526 SWE images and pathologist-scored biopsies, with data collected from a single system at a single site. The automated method integrates three modules that assess SWE image quality, select a region of interest from each SWE measurement and perform machine learning-based, multi-image SWE classification for fibrosis stage ≥F2. Several classification methods were developed and tested using fivefold cross-validation with training, validation and test sets partitioned by subject. Performance metrics were area under receiver operating characteristic curve (AUROC), specificity at 95% sensitivity and number of SWE images required. The final automated method yielded an AUROC of 0.93 (95% confidence interval: 0.90-0.94) versus 0.69 (95% confidence interval: 0.65-0.72) for the reference method, 71% specificity with 95% sensitivity versus 5% and four images per decision versus eight or more. In conclusion, the automated method reported in this study significantly improved the accuracy for ≥F2 classification of SWE measurements as well as reduced the number of measurements needed, which has the potential to reduce clinical workflow.
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Affiliation(s)
- Laura J Brattain
- MIT Lincoln Laboratory, Lexington, Massachusetts, USA; Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
| | - Arinc Ozturk
- Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Manish Dhyani
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts, USA
| | - Joseph R Grajo
- Abdominal Imaging Division, Department of Radiology, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Anthony E Samir
- Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
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