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Chatzipanagiotou OP, Loukas C, Vailas M, Machairas N, Kykalos S, Charalampopoulos G, Filippiadis D, Felekouras E, Schizas D. Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature. J Gastroenterol Hepatol 2024; 39:1994-2005. [PMID: 38923550 DOI: 10.1111/jgh.16663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/26/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND AIM Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis. METHODS A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information. RESULTS Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970. CONCLUSIONS AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.
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Affiliation(s)
- Odysseas P Chatzipanagiotou
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Constantinos Loukas
- Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Michail Vailas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Nikolaos Machairas
- Second Department of Propaedeutic Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Stylianos Kykalos
- Second Department of Propaedeutic Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Georgios Charalampopoulos
- Second Department of Radiology, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Dimitrios Filippiadis
- Second Department of Radiology, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Evangellos Felekouras
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Dimitrios Schizas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
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Wang L, Fatemi M, Alizad A. Artificial intelligence techniques in liver cancer. Front Oncol 2024; 14:1415859. [PMID: 39290245 PMCID: PMC11405163 DOI: 10.3389/fonc.2024.1415859] [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: 04/11/2024] [Accepted: 08/15/2024] [Indexed: 09/19/2024] Open
Abstract
Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.
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Affiliation(s)
- Lulu Wang
- Department of Engineering, School of Technology, Reykjavık University, Reykjavík, Iceland
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
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Zhou Z, Islam MT, Xing L. Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7351-7362. [PMID: 37028335 PMCID: PMC11779602 DOI: 10.1109/tnnls.2023.3250490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Deep learning-based diagnosis is becoming an indispensable part of modern healthcare. For high-performance diagnosis, the optimal design of deep neural networks (DNNs) is a prerequisite. Despite its success in image analysis, existing supervised DNNs based on convolutional layers often suffer from their rudimentary feature exploration ability caused by the limited receptive field and biased feature extraction of conventional convolutional neural networks (CNNs), which compromises the network performance. Here, we propose a novel feature exploration network named manifold embedded multilayer perceptron (MLP) mixer (ME-Mixer), which utilizes both supervised and unsupervised features for disease diagnosis. In the proposed approach, a manifold embedding network is employed to extract class-discriminative features; then, two MLP-Mixer-based feature projectors are adopted to encode the extracted features with the global reception field. Our ME-Mixer network is quite general and can be added as a plugin to any existing CNN. Comprehensive evaluations on two medical datasets are performed. The results demonstrate that their approach greatly enhances the classification accuracy in comparison with different configurations of DNNs with acceptable computational complexity.
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Tandon R, Agrawal S, Rathore NPS, Mishra AK, Jain SK. A systematic review on deep learning-based automated cancer diagnosis models. J Cell Mol Med 2024; 28:e18144. [PMID: 38426930 PMCID: PMC10906380 DOI: 10.1111/jcmm.18144] [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: 06/28/2023] [Revised: 12/08/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024] Open
Abstract
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
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Affiliation(s)
| | | | | | - Abhinava K. Mishra
- Molecular, Cellular and Developmental Biology DepartmentUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
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Wang X, Cai S, Wang H, Li J, Yang Y. Deep-learning-based renal artery stenosis diagnosis via multimodal fusion. J Appl Clin Med Phys 2024; 25:e14298. [PMID: 38373294 DOI: 10.1002/acm2.14298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/19/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
PURPOSE Diagnosing Renal artery stenosis (RAS) presents challenges. This research aimed to develop a deep learning model for the computer-aided diagnosis of RAS, utilizing multimodal fusion technology based on ultrasound scanning images, spectral waveforms, and clinical information. METHODS A total of 1485 patients received renal artery ultrasonography from Peking Union Medical College Hospital were included and their color doppler sonography (CDS) images were classified according to anatomical site and left-right orientation. The RAS diagnosis was modeled as a process involving feature extraction and multimodal fusion. Three deep learning (DL) models (ResNeSt, ResNet, and XCiT) were trained on a multimodal dataset consisted of CDS images, spectrum waveform images, and individual basic information. Predicted performance of different models were compared with senior physician and evaluated on a test dataset (N = 117 patients) with renal artery angiography results. RESULTS Sample sizes of training and validation datasets were 3292 and 169 respectively. On test data (N = 676 samples), predicted accuracies of three DL models were more than 80% and the ResNeSt achieved the accuracy 83.49% ± 0.45%, precision 81.89% ± 3.00%, and recall 76.97% ± 3.7%. There was no significant difference between the accuracy of ResNeSt and ResNet (82.84% ± 1.52%), and the ResNeSt was higher than the XCiT (80.71% ± 2.23%, p < 0.05). Compared to the gold standard, renal artery angiography, the accuracy of ResNest model was 78.25% ± 1.62%, which was inferior to the senior physician (90.09%). Besides, compared to the multimodal fusion model, the performance of single-modal model on spectrum waveform images was relatively lower. CONCLUSION The DL multimodal fusion model shows promising results in assisting RAS diagnosis.
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Affiliation(s)
- Xin Wang
- Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Sheng Cai
- Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Hongyan Wang
- Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jianchu Li
- Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yuqing Yang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Wei Q, Tan N, Xiong S, Luo W, Xia H, Luo B. Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:5701. [PMID: 38067404 PMCID: PMC10705136 DOI: 10.3390/cancers15235701] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/25/2023] [Accepted: 11/29/2023] [Indexed: 06/24/2024] Open
Abstract
(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87-91), the specificity was 90% (95% CI: 87-92), and the AUC was 0.95 (95% CI: 0.93-0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.
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Affiliation(s)
- Qiuxia Wei
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
| | - Nengren Tan
- School of Electronic and Information Engineering, Guangxi Normal University, 15 Qixing District, Guilin 541004, China;
| | - Shiyu Xiong
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
| | - Wanrong Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
| | - Haiying Xia
- School of Electronic and Information Engineering, Guangxi Normal University, 15 Qixing District, Guilin 541004, China;
| | - Baoming Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
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Kamalanathan A, Muthu B, Kuniyil Kaleena P. Artificial Intelligence (AI) Game Changer in Cancer Biology. MARVELS OF ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE IN LIFE SCIENCES 2023:62-87. [DOI: 10.2174/9789815136807123010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
Healthcare is one of many industries where the most modern technologies,
such as artificial intelligence and machine learning, have shown a wide range of
applications. Cancer, one of the most prevalent non-communicable diseases in modern
times, accounts for a sizable portion of worldwide mortality. Investigations are
continuously being conducted to find ways to reduce cancer mortality and morbidity.
Artificial Intelligence (AI) is currently being used in cancer research, with promising
results. Two main features play a vital role in improving cancer prognosis: early
detection and proper diagnosis using imaging and molecular techniques. AI's use as a
tool in these sectors has demonstrated its capacity to precisely detect and diagnose,
which is one of AI's many applications in cancer research. The purpose of this chapter
is to review the literature and find AI applications in a range of cancers that are
commonly seen.
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Affiliation(s)
- Ashok Kamalanathan
- Department of Microbiology and Biotechnology, Faculty of Arts and Science, Bharath Institute
of Higher Education and Research (BIHER), Chennai- 600 073, Tamil Nadu, India
| | - Babu Muthu
- Department of Microbiology and Biotechnology, Faculty of Arts and Science, Bharath Institute
of Higher Education and Research (BIHER), Chennai- 600 073, Tamil Nadu, India
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Vetter M, Waldner MJ, Zundler S, Klett D, Bocklitz T, Neurath MF, Adler W, Jesper D. Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2023; 44:395-407. [PMID: 37001563 DOI: 10.1055/a-2066-9372] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.
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Affiliation(s)
- Marcel Vetter
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Maximilian J Waldner
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Sebastian Zundler
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Daniel Klett
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Thomas Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-Universitat Jena, Jena, Germany
- Leibniz-Institute of Photonic Technology, Friedrich Schiller University Jena, Jena, Germany
| | - Markus F Neurath
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Werner Adler
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Daniel Jesper
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
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Singh S, Hoque S, Zekry A, Sowmya A. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review. J Med Syst 2023; 47:73. [PMID: 37432493 PMCID: PMC10335966 DOI: 10.1007/s10916-023-01968-7] [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: 10/18/2022] [Accepted: 07/02/2023] [Indexed: 07/12/2023]
Abstract
Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.
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Affiliation(s)
- Sonit Singh
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia.
| | - Shakira Hoque
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Amany Zekry
- St George and Sutherland Clinical Campus, School of Clinical Medicine, UNSW, High St, Kensington, 2052, NSW, Australia
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Arcot Sowmya
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia
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Moroney J, Trivella J, George B, White SB. A Paradigm Shift in Primary Liver Cancer Therapy Utilizing Genomics, Molecular Biomarkers, and Artificial Intelligence. Cancers (Basel) 2023; 15:2791. [PMID: 37345129 PMCID: PMC10216313 DOI: 10.3390/cancers15102791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/02/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Primary liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related death. Conventional therapies offer limited survival benefit despite improvements in locoregional liver-directed therapies, which highlights the underlying complexity of liver cancers. This review explores the latest research in primary liver cancer therapies, focusing on developments in genomics, molecular biomarkers, and artificial intelligence. Attention is also given to ongoing research and future directions of immunotherapy and locoregional therapies of primary liver cancers.
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Affiliation(s)
- James Moroney
- Division of Vascular and Interventional Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Juan Trivella
- Division of Gastroenterology and Hepatology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ben George
- Division of Hematology and Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Sarah B. White
- Division of Vascular and Interventional Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Hüseynova M, Bayramov N, Məmmədova M. РОЛЬ АЛГОРИТМОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ. AZERBAIJAN MEDICAL JOURNAL 2023:164-171. [DOI: 10.34921/amj.2023.2.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
Hepatosellülyar karsinoma (HSK) ən çox yayılan bədxassəli törəmələr arasında beşinci yeri tutur və dünyada xərçənglə əlaqəli ölümün üçüncü ən çox yayılmış səbəbidir. Süni intellekt (Sİ) sürətlə artan maraq sahəsidir. Müəlliflər HSK-ın diaqnostikasında və qiymətləndirilməsində Sİ-nin tətbiqi barədə məlumat verən məqalələri araşdırmışlar. Bu məqsədlə 27 məqalə təhlil edilmişdir. Təhlil edilmiş məqalələrdən KT görüntülərinin tədqiqinə dair 19 məqalədə (41,30%), USQ görüntülərinin öyrənilməsini əks etdirən 20 (43,47%) və MRT görüntülərindən bəhs edən 7 məqalədə (15,21%) müxtəlif Sİ alqoritmləri qəbul edilmişdir. Heç bir məqalədə PET və rentgen texnologiyasında süni intellektin istifadəsi müzakirə edilməyib. Sistematik yanaşma göstərmişdir ki, HSK-nin diaqnostikası və qiymətləndirilməsi üzrə əvvəlki işlərdə USQ, KT və MRT istifadə edilərək ənənəvi şərhin maşın öyrənməsi ilə müqayisəliliyi qiymətləndirilmişdir. Təhlillərimizdə görüntüləmə üsullarının istifadəsi HSK diaqnostikası üçün tibbi görüntüləmənin faydalılığını və təkamülünü əks etdirir. Bundan əlavə, nəticələrimiz lazımsız təkrarlanmanı və resursların israfını minimuma endirmək üçün birgə məlumat bazasında məlumat mübadiləsinə qaçılmaz ehtiyac olduğunu vurğulayır.
Гепатоцеллюлярная карцинома является пятым по распространенности злокачественным новообразованием и третьей по частоте причиной смерти от рака во всём мире. Искусственный интеллект — это быстрорастущая область интересов. Авторами были рассмотрены статьи, в которых сообщается о применении алгоритмов ИИ в диагностике и оценке ГЦК. Для этого проанализированы 27 статей. В проанализированных статьях в 19 статьях, посвящённых КТ-изображениям (41,30%), в 20 статьях, посвящённых изображениям УЗИ (43,47%), и в 7 статьях, посвящённым МРТ-изображениям (15,21%), использовали разные алгоритмы ИИ. Ни в одной статье не обсуждалось использование искусственного интеллекта в ПЭТ и рентгеновские технологии. Системный подход показал, что предыдущая работа по диагностике и оценке ГЦК оценивала сопоставимость традиционной интерпретации с машинным обучением с использованием УЗИ, КТ и МРТ. Использование методов визуализации в проведенном анализе отражает полезность и эволюцию медицинской визуализации для диагностики ГЦК. Кроме того, результаты поиска литературы подчёркивают острую необходимость совместного использования данных в совместных базах данных, чтобы свести к минимуму ненужное дублирование и растрату ресурсов.
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer death worldwide. Artificial intelligence (AI) is a rapidly growing area of interest. We have reviewed articles reporting the application of AI algorithms in the diagnosis and evaluation of HCC. To do this, we analyzed 27 articles. In the analyzed articles, 19 articles on CT images (41.30%), 20 articles on ultrasound images (43.47%), and 7 articles on MRI images (15.21%) used different AI algorithms. None of the articles discussed the use of artificial intelligence in PET and X-ray technologies. Our systematic approach showed that previous work on the diagnosis and evaluation of HCC assessed the comparability of traditional interpretation with machine learning using ultrasound, CT, and MRI. The use of imaging modalities in our analysis reflects the usefulness and evolution of medical imaging for diagnosing HCC. In addition, our results highlight the critical need to share data across collaborative databases to minimize unnecessary duplication and waste of resources.
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Xu Y, Zheng B, Liu X, Wu T, Ju J, Wang S, Lian Y, Zhang H, Liang T, Sang Y, Jiang R, Wang G, Ren J, Chen T. Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames. Brief Bioinform 2023; 24:bbac569. [PMID: 36575566 PMCID: PMC10390801 DOI: 10.1093/bib/bbac569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/07/2022] [Accepted: 11/22/2022] [Indexed: 12/29/2022] Open
Abstract
Recent developments of deep learning methods have demonstrated their feasibility in liver malignancy diagnosis using ultrasound (US) images. However, most of these methods require manual selection and annotation of US images by radiologists, which limit their practical application. On the other hand, US videos provide more comprehensive morphological information about liver masses and their relationships with surrounding structures than US images, potentially leading to a more accurate diagnosis. Here, we developed a fully automated artificial intelligence (AI) pipeline to imitate the workflow of radiologists for detecting liver masses and diagnosing liver malignancy. In this pipeline, we designed an automated mass-guided strategy that used segmentation information to direct diagnostic models to focus on liver masses, thus increasing diagnostic accuracy. The diagnostic models based on US videos utilized bi-directional convolutional long short-term memory modules with an attention-boosted module to learn and fuse spatiotemporal information from consecutive video frames. Using a large-scale dataset of 50 063 US images and video frames from 11 468 patients, we developed and tested the AI pipeline and investigated its applications. A dataset of annotated US images is available at https://doi.org/10.5281/zenodo.7272660.
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Affiliation(s)
- Yiming Xu
- Department of Computer Science and Technology & Institute of Artificial Intelligence & BNRist, Tsinghua University, Beijing, China
| | - Bowen Zheng
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiaohong Liu
- Department of Computer Science and Technology & Institute of Artificial Intelligence & BNRist, Tsinghua University, Beijing, China
| | - Tao Wu
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jinxiu Ju
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shijie Wang
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yufan Lian
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Hongjun Zhang
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Tong Liang
- Foshan Traditional Chinese Medicine Hospital, Foshan, Guangdong, China
| | - Ye Sang
- The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang 443003, China
| | - Rui Jiang
- Department of Automation & BNRist, Tsinghua University, Beijing, China
| | - Guangyu Wang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jie Ren
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Ting Chen
- Department of Computer Science and Technology & Institute of Artificial Intelligence & BNRist, Tsinghua University, Beijing, China
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Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T 1 and T 2 Relaxation Times with Application to Cancer Cell Culture. Int J Mol Sci 2023; 24:ijms24021554. [PMID: 36675075 PMCID: PMC9861169 DOI: 10.3390/ijms24021554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
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Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
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Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
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Ma L, Wang R, He Q, Huang L, Wei X, Lu X, Du Y, Luo J, Liao H. Artificial intelligence-based ultrasound imaging technologies for hepatic diseases. ILIVER 2022; 1:252-264. [DOI: 10.1016/j.iliver.2022.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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19
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Martinino A, Aloulou M, Chatterjee S, Scarano Pereira JP, Singhal S, Patel T, Kirchgesner TPE, Agnes S, Annunziata S, Treglia G, Giovinazzo F. Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review. J Clin Med 2022; 11:6368. [PMID: 36362596 PMCID: PMC9655417 DOI: 10.3390/jcm11216368] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 09/21/2023] Open
Abstract
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.
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Affiliation(s)
| | | | - Surobhi Chatterjee
- Department of Internal Medicine, King George’s Medical University, Lucknow 226003, Uttar Pradesh, India
| | | | - Saurabh Singhal
- Department of HPB Surgery and Liver Transplantation, BLK-MAX Superspeciality Hospital, New Delhi 110005, Delhi, India
| | - Tapan Patel
- Department of Surgery, Baroda Medical College and SSG Hospital, Vadodara 390001, Gujarat, India
| | - Thomas Paul-Emile Kirchgesner
- Département of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1348 Brussels, Belgium
| | - Salvatore Agnes
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Salvatore Annunziata
- Unit of Nuclear Medicine, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Giorgio Treglia
- Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Francesco Giovinazzo
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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20
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Feng H, Tang Q, Yu Z, Tang H, Yin M, Wei A. A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:1526540. [PMID: 36299601 PMCID: PMC9592196 DOI: 10.1155/2022/1526540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/19/2022] [Accepted: 09/28/2022] [Indexed: 11/18/2022]
Abstract
For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People's Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.
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Affiliation(s)
- Hao Feng
- Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, China
| | - Qian Tang
- Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, China
| | - Zhengyu Yu
- Faculty of Engineering and IT, University of Technology, Sydney, Sydney, NSW 2007, Australia
| | - Hua Tang
- Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, China
| | - Ming Yin
- Department of Dermatology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, China
| | - An Wei
- Department of Ultrasound, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, China
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21
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Survarachakan S, Prasad PJR, Naseem R, Pérez de Frutos J, Kumar RP, Langø T, Alaya Cheikh F, Elle OJ, Lindseth F. Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions. Artif Intell Med 2022; 130:102331. [DOI: 10.1016/j.artmed.2022.102331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022]
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22
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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: 06/19/2022] [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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23
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Kalejahi BK, Meshgini S, Danishvar S, Khorram S. Diagnosis of liver disease by computer- assisted imaging techniques: A literature review. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-216379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician’s or doctor’s experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach.
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Affiliation(s)
- Behnam Kiani Kalejahi
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Saeed Meshgini
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sebelan Danishvar
- Department of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
| | - Sara Khorram
- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
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24
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Gong X, Zhao X, Fan L, Li T, Guo Y, Luo J. BUS-net: a bimodal ultrasound network for breast cancer diagnosis. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01596-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Zhou B, Yang X, Curran WJ, Liu T. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1329-1342. [PMID: 34467542 DOI: 10.1002/jum.15819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 08/01/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.
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Affiliation(s)
- Boran Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
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Dadoun H, Rousseau AL, de Kerviler E, Correas JM, Tissier AM, Joujou F, Bodard S, Khezzane K, de Margerie-Mellon C, Delingette H, Ayache N. Deep Learning for the Detection, Localization, and Characterization of Focal Liver Lesions on Abdominal US Images. Radiol Artif Intell 2022; 4:e210110. [PMID: 35652113 DOI: 10.1148/ryai.210110] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 02/08/2022] [Accepted: 02/16/2022] [Indexed: 12/24/2022]
Abstract
Purpose To train and assess the performance of a deep learning-based network designed to detect, localize, and characterize focal liver lesions (FLLs) in the liver parenchyma on abdominal US images. Materials and Methods In this retrospective, multicenter, institutional review board-approved study, two object detectors, Faster region-based convolutional neural network (Faster R-CNN) and Detection Transformer (DETR), were fine-tuned on a dataset of 1026 patients (n = 2551 B-mode abdominal US images obtained between 2014 and 2018). Performance of the networks was analyzed on a test set of 48 additional patients (n = 155 B-mode abdominal US images obtained in 2019) and compared with the performance of three caregivers (one nonexpert and two experts) blinded to the clinical history. The sign test was used to compare accuracy, specificity, sensitivity, and positive predictive value among all raters. Results DETR achieved a specificity of 90% (95% CI: 75, 100) and a sensitivity of 97% (95% CI: 97, 97) for the detection of FLLs. The performance of DETR met or exceeded that of the three caregivers for this task. DETR correctly localized 80% of the lesions, and it achieved a specificity of 81% (95% CI: 67, 91) and a sensitivity of 82% (95% CI: 62, 100) for FLL characterization (benign vs malignant) among lesions localized by all raters. The performance of DETR met or exceeded that of two experts and Faster R-CNN for these tasks. Conclusion DETR demonstrated high specificity for detection, localization, and characterization of FLLs on abdominal US images. Supplemental material is available for this article. RSNA, 2022Keywords: Computer-aided Diagnosis (CAD), Ultrasound, Abdomen/GI, Liver, Tissue Characterization, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN).
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Affiliation(s)
- Hind Dadoun
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Anne-Laure Rousseau
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Eric de Kerviler
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Jean-Michel Correas
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Anne-Marie Tissier
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Fanny Joujou
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Sylvain Bodard
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Kemel Khezzane
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Constance de Margerie-Mellon
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Hervé Delingette
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
| | - Nicholas Ayache
- Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France (H. Dadoun, H. Delingette, N.A.); Department of Vascular Surgery, Georges Pompidou European Hospital APHP, Université de Paris, Paris, France (A.L.R.); NHance.ngo, Saint Germain en Laye, France (A.L.R.); Department of Radiology, Hôpital Saint Louis APHP, Université de Paris, Paris, France (E.d.K., F.J., K.K., C.d.M.M.); and Department of Adult Radiology, Université de Paris and Université de l'Hôpital Necker, Paris, France (J.M.C., A.M.T., S.B.)
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A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083773] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.
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SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation. MATHEMATICS 2022. [DOI: 10.3390/math10050796] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Precise vertebrae segmentation is essential for the image-related analysis of spine pathologies such as vertebral compression fractures and other abnormalities, as well as for clinical diagnostic treatment and surgical planning. An automatic and objective system for vertebra segmentation is required, but its development is likely to run into difficulties such as low segmentation accuracy and the requirement of prior knowledge or human intervention. Recently, vertebral segmentation methods have focused on deep learning-based techniques. To mitigate the challenges involved, we propose deep learning primitives and stacked Sparse autoencoder-based patch classification modeling for Vertebrae segmentation (SVseg) from Computed Tomography (CT) images. After data preprocessing, we extract overlapping patches from CT images as input to train the model. The stacked sparse autoencoder learns high-level features from unlabeled image patches in an unsupervised way. Furthermore, we employ supervised learning to refine the feature representation to improve the discriminability of learned features. These high-level features are fed into a logistic regression classifier to fine-tune the model. A sigmoid classifier is added to the network to discriminate the vertebrae patches from non-vertebrae patches by selecting the class with the highest probabilities. We validated our proposed SVseg model on the publicly available MICCAI Computational Spine Imaging (CSI) dataset. After configuration optimization, our proposed SVseg model achieved impressive performance, with 87.39% in Dice Similarity Coefficient (DSC), 77.60% in Jaccard Similarity Coefficient (JSC), 91.53% in precision (PRE), and 90.88% in sensitivity (SEN). The experimental results demonstrated the method’s efficiency and significant potential for diagnosing and treating clinical spinal diseases.
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Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts. J Gastroenterol 2022; 57:309-321. [PMID: 35220490 PMCID: PMC8938378 DOI: 10.1007/s00535-022-01849-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/07/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND Ultrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US. METHODS We constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images. RESULTS The diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%-69.1%) and 47.3% (45.5%-47.3%) by human experts and non-experts, respectively. CONCLUSION The performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.
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Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13:1977-1990. [PMID: 35070002 PMCID: PMC8727218 DOI: 10.4254/wjh.v13.i12.1977] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/09/2021] [Accepted: 11/25/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.
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Affiliation(s)
- Fadl H Veerankutty
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Govind Jayan
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Manish Kumar Yadav
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Krishnan Sarojam Manoj
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Abhishek Yadav
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Sindhu Radha Sadasivan Nair
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - T U Shabeerali
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Varghese Yeldho
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Madhu Sasidharan
- Gastroenterology and Hepatology, Kerala Institute of Medical Sciences, Thiruvananthapuram 695029, India
| | - Shiraz Ahmad Rather
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
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Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13:2039-2051. [PMID: 35070007 PMCID: PMC8727204 DOI: 10.4254/wjh.v13.i12.2039] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.
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Affiliation(s)
- Joseph C Ahn
- Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States
| | - Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Amit G Singal
- Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Ju-Dong Yang
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
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Vaz K, Goodwin T, Kemp W, Roberts S, Majeed A. Artificial Intelligence in Hepatology: A Narrative Review. Semin Liver Dis 2021; 41:551-556. [PMID: 34327698 DOI: 10.1055/s-0041-1731706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
There has been a tremendous growth in data collection in hepatology over the last decade. This wealth of "big data" lends itself to the application of artificial intelligence in the development of predictive and diagnostic models with potentially greater accuracy than standard biostatistics. As processing power of computing systems has improved and data are made more accessible through the large databases and electronic health record, these more contemporary techniques for analyzing and interpreting data have garnered much interest in the field of medicine. This review highlights the current evidence base for the use of artificial intelligence in hepatology, focusing particularly on the areas of diagnosis and prognosis of advanced chronic liver disease and hepatic neoplasia.
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Affiliation(s)
- Karl Vaz
- Department of Gastroenterology and Hepatology, Austin Health, Melbourne, Australia
| | - Thomas Goodwin
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
| | - William Kemp
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| | - Stuart Roberts
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| | - Ammar Majeed
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
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Ryu H, Shin SY, Lee JY, Lee KM, Kang HJ, Yi J. Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur Radiol 2021; 31:8733-8742. [PMID: 33881566 PMCID: PMC8523410 DOI: 10.1007/s00330-021-07850-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 03/02/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. METHODS In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). RESULTS We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. CONCLUSIONS The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. KEY POINTS • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.
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Affiliation(s)
- Hwaseong Ryu
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | | | - Jae Young Lee
- Department of Radiology and the Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Kyoung Mu Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
| | - Hyo-Jin Kang
- Department of Radiology and the Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jonghyon Yi
- Medical Imaging R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Republic of Korea
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Bhatt C, Kumar I, Vijayakumar V, Singh KU, Kumar A. The state of the art of deep learning models in medical science and their challenges. MULTIMEDIA SYSTEMS 2021; 27:599-613. [DOI: 10.1007/s00530-020-00694-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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Xiang K, Jiang B, Shang D. The overview of the deep learning integrated into the medical imaging of liver: a review. Hepatol Int 2021; 15:868-880. [PMID: 34264509 DOI: 10.1007/s12072-021-10229-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022]
Abstract
Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. This advanced algorithm takes part in various sections of imaging processing such as liver segmentation, lesion delineation, disease classification, process optimization, etc. The DL optimized imaging diagnosis shows a broad prospect instead of the pathological biopsy for the advantages of convenience, safety, and inexpensiveness. In this paper, we reviewed the published representative DL-related hepatic imaging works, described the general situation of this new-rising technology in medical liver imaging and explored the future direction of DL development.
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Affiliation(s)
- Kailai Xiang
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China.,Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China
| | - Baihui Jiang
- Department of Ophthalmology, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China
| | - Dong Shang
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China. .,Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China.
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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Komatsu M, Sakai A, Dozen A, Shozu K, Yasutomi S, Machino H, Asada K, Kaneko S, Hamamoto R. Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines 2021; 9:720. [PMID: 34201827 PMCID: PMC8301304 DOI: 10.3390/biomedicines9070720] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/13/2021] [Accepted: 06/18/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.
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Affiliation(s)
- Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Akira Sakai
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Wan Y, Zheng Z, Liu R, Zhu Z, Zhou H, Zhang X, Boumaraf S. A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation. Life (Basel) 2021; 11:life11060582. [PMID: 34207262 PMCID: PMC8234101 DOI: 10.3390/life11060582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/10/2021] [Accepted: 06/16/2021] [Indexed: 02/08/2023] Open
Abstract
Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for liver lesion diagnosis on magnetic resonance images, termed as MMF-CNN. We introduce a multi-scale representation strategy to encode both the local and semi-local complementary information of the images. To take advantage of the complementary information of multi-scale representations, we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning. We further explore the explanation of the diagnosis decision of the deep neural network through visualizing the areas of interest of the network. A new scoring method is designed to evaluate whether the attention maps can highlight the relevant radiological features. The explanation and visualization make the decision-making process of the deep neural network transparent for the clinicians. We apply our proposed approach to various state-of-the-art deep learning architectures. The experimental results demonstrate the effectiveness of our approach.
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Affiliation(s)
- Yuchai Wan
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; (H.Z.); (X.Z.)
- Correspondence: (Y.W.); (Z.Z.)
| | - Zhongshu Zheng
- Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China;
| | - Ran Liu
- China South-to-North Water Diversion Corporation Limited, Beijing 100038, China;
| | - Zheng Zhu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17, Panjiayuan NanLi, Chaoyang District, Beijing 100021, China
- Correspondence: (Y.W.); (Z.Z.)
| | - Hongen Zhou
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; (H.Z.); (X.Z.)
| | - Xun Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; (H.Z.); (X.Z.)
| | - Said Boumaraf
- Centre d’Exploitation des Systèmes de Télécommunications Spatiales (CESTS), Agence Spatiale Algérienne, Algiers, Algeria;
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Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis. SENSORS 2021; 21:s21124126. [PMID: 34208548 PMCID: PMC8235629 DOI: 10.3390/s21124126] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/04/2021] [Accepted: 06/10/2021] [Indexed: 12/15/2022]
Abstract
Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).
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Tiyarattanachai T, Apiparakoon T, Marukatat S, Sukcharoen S, Geratikornsupuk N, Anukulkarnkusol N, Mekaroonkamol P, Tanpowpong N, Sarakul P, Rerknimitr R, Chaiteerakij R. Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. PLoS One 2021; 16:e0252882. [PMID: 34101764 PMCID: PMC8186767 DOI: 10.1371/journal.pone.0252882] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/24/2021] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted.
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Affiliation(s)
| | - Terapap Apiparakoon
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Sanparith Marukatat
- Image Processing and Understanding Team, Artificial Intelligence Research Group, National Electronics and Computer Technology Center, Pathum Thani, Thailand
| | - Sasima Sukcharoen
- Division of Gastroenterology, Department of Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Nopavut Geratikornsupuk
- Department of Medicine, Queen Savang Vadhana Memorial Hospital, The Thai Red Cross Society, Chonburi, Thailand
| | | | - Parit Mekaroonkamol
- Division of Gastroenterology, Department of Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Natthaporn Tanpowpong
- Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | | | - Rungsun Rerknimitr
- Division of Gastroenterology, Department of Medicine, Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Roongruedee Chaiteerakij
- Division of Gastroenterology, Department of Medicine, Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- * E-mail:
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Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10023-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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44
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Marya NB, Powers PD, Fujii-Lau L, Abu Dayyeh BK, Gleeson FC, Chen S, Long Z, Hough DM, Chandrasekhara V, Iyer PG, Rajan E, Sanchez W, Sawas T, Storm AC, Wang KK, Levy MJ. Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses. Gastrointest Endosc 2021; 93:1121-1130.e1. [PMID: 32861752 DOI: 10.1016/j.gie.2020.08.024] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 08/24/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Detection and characterization of focal liver lesions (FLLs) is key for optimizing treatment for patients who may have a primary hepatic cancer or metastatic disease to the liver. This is the first study to develop an EUS-based convolutional neural network (CNN) model for the purpose of identifying and classifying FLLs. METHODS A prospective EUS database comprising cases of FLLs visualized and sampled via EUS was reviewed. Relevant still images and videos of liver parenchyma and FLLs were extracted. Patient data were then randomly distributed for the purpose of CNN model training and testing. Once a final model was created, occlusion heatmap analysis was performed to assess the ability of the EUS-CNN model to autonomously identify FLLs. The performance of the EUS-CNN for differentiating benign and malignant FLLs was also analyzed. RESULTS A total of 210,685 unique EUS images from 256 patients were used to train, validate, and test the CNN model. Occlusion heatmap analyses demonstrated that the EUS-CNN model was successful in autonomously locating FLLs in 92.0% of EUS video assets. When evaluating any random still image extracted from videos or physician-captured images, the CNN model was 90% sensitive and 71% specific (area under the receiver operating characteristic [AUROC], 0.861) for classifying malignant FLLs. When evaluating full-length video assets, the EUS-CNN model was 100% sensitive and 80% specific (AUROC, 0.904) for classifying malignant FLLs. CONCLUSIONS This study demonstrated the capability of an EUS-CNN model to autonomously identify FLLs and to accurately classify them as either malignant or benign lesions.
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Affiliation(s)
- Neil B Marya
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | | | - Larissa Fujii-Lau
- Department of Gastroenterology, The Queen's Medical Center, Honolulu, Hawaii
| | - Barham K Abu Dayyeh
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Ferga C Gleeson
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Shigao Chen
- Division of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Zaiyang Long
- Division of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - David M Hough
- Division of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Prasad G Iyer
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Elizabeth Rajan
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - William Sanchez
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Tarek Sawas
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Andrew C Storm
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Kenneth K Wang
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Michael J Levy
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol 2021; 36:539-542. [PMID: 33709605 DOI: 10.1111/jgh.15409] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/16/2021] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science that attempts to mimic human intelligence, such as learning and problem-solving skills. The use of AI in hepatology occurred later than in gastroenterology. Nevertheless, studies on applying AI to liver disease have recently increased. AI in hepatology can be applied for detecting liver fibrosis, differentiating focal liver lesions, predicting prognosis of chronic liver disease, and diagnosing of nonalcoholic fatty liver disease. We expect that AI will eventually help manage patients with liver disease, predict the clinical outcomes, and reduce medical errors. However, there are several hurdles that need to be overcome. Here, we will briefly review the areas of liver disease to which AI can be applied.
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Affiliation(s)
- Hye Won Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea.,Yonsei Liver Center, Severance Hospital, Seoul, South Korea
| | - Joseph J Y Sung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sang Hoon Ahn
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea.,Yonsei Liver Center, Severance Hospital, Seoul, South Korea
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Lupsor-Platon M, Serban T, Silion AI, Tirpe GR, Tirpe A, Florea M. Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease. Cancers (Basel) 2021; 13:790. [PMID: 33672827 PMCID: PMC7918928 DOI: 10.3390/cancers13040790] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/14/2020] [Accepted: 02/09/2021] [Indexed: 12/12/2022] Open
Abstract
Global statistics show an increasing percentage of patients that develop non-alcoholic fatty liver disease (NAFLD) and NAFLD-related hepatocellular carcinoma (HCC), even in the absence of cirrhosis. In the present review, we analyzed the diagnostic performance of ultrasonography (US) in the non-invasive evaluation of NAFLD and NAFLD-related HCC, as well as possibilities of optimizing US diagnosis with the help of artificial intelligence (AI) assistance. To date, US is the first-line examination recommended in the screening of patients with clinical suspicion of NAFLD, as it is readily available and leads to a better disease-specific surveillance. However, the conventional US presents limitations that significantly hamper its applicability in quantifying NAFLD and accurately characterizing a given focal liver lesion (FLL). Ultrasound contrast agents (UCAs) are an essential add-on to the conventional B-mode US and to the Doppler US that further empower this method, allowing the evaluation of the enhancement properties and the vascular architecture of FLLs, in comparison to the background parenchyma. The current paper also explores the new universe of AI and the various implications of deep learning algorithms in the evaluation of NAFLD and NAFLD-related HCC through US methods, concluding that it could potentially be a game changer for patient care.
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Affiliation(s)
- Monica Lupsor-Platon
- Medical Imaging Department, Regional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania
| | - Teodora Serban
- Medical Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania; (T.S.); (A.I.S.)
| | - Alexandra Iulia Silion
- Medical Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania; (T.S.); (A.I.S.)
| | - George Razvan Tirpe
- County Emergency Hospital Cluj-Napoca, 3-5 Clinicilor Street, 400000 Cluj-Napoca, Romania;
| | - Alexandru Tirpe
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 23 Marinescu Street, 400337 Cluj-Napoca, Romania;
| | - Mira Florea
- Community Medicine Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400001 Cluj-Napoca, Romania;
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Nishida N, Kudo M. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor. Front Oncol 2020; 10:594580. [PMID: 33409151 PMCID: PMC7779763 DOI: 10.3389/fonc.2020.594580] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/16/2020] [Indexed: 12/15/2022] Open
Abstract
Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
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Kather JN, Krause J, Luedde T. [Artificial intelligence in gastroenterology]. Dtsch Med Wochenschr 2020; 145:1450-1454. [PMID: 33022724 DOI: 10.1055/a-1013-6593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Artificial intelligence (AI) is currently transforming all aspects of our daily life, including the practice of medicine. Artificial neural networks are a key method of AI. They can very effectively detect subtle patterns in imaging data and speech or text data. This is highly relevant for the practice of gastroenterology. Here, we summarize the state of the art in AI in gastroenterology and outline major clinical applications. Our focus is on AI-based analysis of endoscopy images, non-invasive imaging and histology images. In these applications, AI can support human pattern recognition. Beyond detection and classification of pathological findings, AI can predict clinical outcome from subtle image features.
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Affiliation(s)
| | | | - Tom Luedde
- Medizinische Klinik III, Universitätsklinikum Aachen
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Song KD. Current status of deep learning applications in abdominal ultrasonography. Ultrasonography 2020; 40:177-182. [PMID: 33242931 PMCID: PMC7994733 DOI: 10.14366/usg.20085] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications.
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Affiliation(s)
- Kyoung Doo Song
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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