| For: | Li B, Tai DI, Yan K, Chen YC, Chen CJ, Huang SF, Hsu TH, Yu WT, Xiao J, Le L, Harrison AP. Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning. World J Gastroenterol 2022; 28(22): 2494-2508 [PMID: 35979264 DOI: 10.3748/wjg.v28.i22.2494] |
|---|---|
| URL: | https://www.wjgnet.com/1007-9327/full/v28/i22/2494.htm |
| Number | Citing Articles |
| 1 |
Jiawen Li, Jianhui Chen, Xiaohong Zeng, Guorong Lyu, Shu Lin, Shaozheng He. Update of machine learning for ultrasound diagnosis of metabolic dysfunction-associated steatotic liver disease: a bright future for deep learning. PeerJ 2025; 13 doi: 10.7717/peerj.19645
|
| 2 |
Zejun Ma, Bo Liu, Shuyu Zhou, Shuai Yang, Xiaojie Sun, Xinping Jiang, Xiaofeng Sun. Advances in quantitative ultrasound for metabolic dysfunction-associated steatotic liver disease diagnosis. Frontiers in Physiology 2026; 17 doi: 10.3389/fphys.2026.1802284
|
| 3 |
Simone Kresevic, Mauro Giuffrè, Milos Ajcevic, Lory Saveria Crocè, Agostino Accardo. 9th European Medical and Biological Engineering Conference. IFMBE Proceedings 2024; 113 doi: 10.1007/978-3-031-61628-0_22
|
| 4 |
Jiamei Song, Dan Liu, Jitong Li, Haoru Cong, Ruixue Deng, Yihan Lu, Jiayi Sun, Jingzhou Zhang. Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2026; 28 doi: 10.2196/78310
|
| 5 |
Ioannis Lamprinakos, Myrsini Orfanidou, Vasileios Rafailidis, Stergios A. Polyzos. Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians. Hormones 2026; doi: 10.1007/s42000-026-00802-2
|
| 6 |
Xinyue Niu, Yujie Zhou, Jin Xu, Qin Xue, Xiaoyan Xu, Jia Li, Ling Wang, Tianyu Tang. Deep learning in the precise assessment of primary Sjögren’s syndrome based on ultrasound images. Rheumatology 2025; 64(4) doi: 10.1093/rheumatology/keae312
|
| 7 |
Maria Chiara Brunese, Maria Rita Fantozzi, Roberta Fusco, Federica De Muzio, Michela Gabelloni, Ginevra Danti, Alessandra Borgheresi, Pierpaolo Palumbo, Federico Bruno, Nicoletta Gandolfo, Andrea Giovagnoni, Vittorio Miele, Antonio Barile, Vincenza Granata. Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma. Diagnostics 2023; 13(8) doi: 10.3390/diagnostics13081488
|
| 8 |
Kumar Mohit, Ankit Shukla, Rajeev Gupta, Pramod Kumar Singh, Kushagra Agarwal, Basant Kumar. Contrastive Learning Embedded Siamese Neural Network for the Assessment of Fatty Liver. TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON) 2023; doi: 10.1109/TENCON58879.2023.10322413
|
| 9 |
Jing-Wen Cai, Wei-Long Wang, Dong-Ling Lin, Shu-Feng Ren, Qian-Qian Jia, Xiao-Xuan He, Xue-Xia Yang, Wen Cai, Hui Hou. Global Trends in Non-Invasive Techniques for the Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease: A Bibliometric and Visualization Analysis. Journal of Multidisciplinary Healthcare 2025; doi: 10.2147/JMDH.S525751
|
| 10 |
Bowen Li, Zongwei Zhou, Alan L. Yuille, Max Allan, Jonathan McLeod, Nick Bottenus, Christian Boehm. Ultra-TransUNet: ultrasound segmentation framework with spatial-temporal context feature fusion. Medical Imaging 2024: Ultrasonic Imaging and Tomography 2024; doi: 10.1117/12.3006940
|
| 11 |
Fahad Muflih Alshagathrh, Mowafa Said Househ. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review. Bioengineering 2022; 9(12) doi: 10.3390/bioengineering9120748
|
| 12 |
Priyanka Sengar, Jagendra Singh, Abhay Bansal. Deep learning for non-invasive NAFLD detection and staging: A comprehensive review. Journal of Liver Transplantation 2026; 21 doi: 10.1016/j.liver.2026.100318
|
| 13 |
Ahmed El Kaffas, Krishna Chaitanya Bhatraju, Jenny M. Vo-Phamhi, Thodsawit Tiyarattanachai, Neha Antil, Lindsey M. Negrete, Aya Kamaya, Luyao Shen. Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound. Ultrasound in Medicine & Biology 2025; 51(2) doi: 10.1016/j.ultrasmedbio.2024.09.020
|
| 14 |
Manshee Agarwal, Sapna Sinha, Vikram Bali. NAFLD Screening from B-Mode Ultrasound: A Sensitivity-Constrained Comparison of Decision Strategies. 2025 5th International Conference on Advancement in Electronics & Communication Engineering (AECE) 2025; doi: 10.1109/AECE67531.2025.11386670
|
| 15 |
Archana D. Dantakale, Sanjeev J. Wagh. Comparative Evaluation of Deep Learning Models for Exhaled Breath-Based Liver Disease Prediction. 2025 6th International Conference for Emerging Technology (INCET) 2025; doi: 10.1109/INCET64471.2025.11140928
|
| 16 |
Xueying Qin, Jingjing Liu. Nanoformulations for the diagnosis and treatment of metabolic dysfunction-associated steatohepatitis. Acta Biomaterialia 2024; 184 doi: 10.1016/j.actbio.2024.06.014
|
| 17 |
Houshyar Maghsoudi, Ahmad Khonche, Reza Gereami, Farshad Gharebakhshi. Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI. Abdominal Radiology 2025; 51(6) doi: 10.1007/s00261-025-05317-9
|
| 18 |
Chenglong Yin, Huafeng Zhang, Jin Du, Yingling Zhu, Hua Zhu, Hongqin Yue. Artificial intelligence in imaging for liver disease diagnosis. Frontiers in Medicine 2025; 12 doi: 10.3389/fmed.2025.1591523
|
| 19 |
Josefine Stansch, Kien Vu Trung, Valentin Blank, Jakob Nikolas Kather, Moritz Herzog, Tobias Seibel, Paul-Henry Koop, Robert Haase, Thomas Berg, Johannes Wiegand, Thomas Karlas. Analysis of B-Scan Ultrasonography Using Neural Networks to Predict Risk of Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). Ultrasound in Medicine & Biology 2026; 52(7) doi: 10.1016/j.ultrasmedbio.2026.03.007
|
| 20 |
Jennifer Tai, Tse-Hwa Hsu, Cheng-Jen Chen, Ming-Ling Chang, Chihung Lin, Shiu-Feng Huang, Le Lu, Adam P. Harrison, Dar-In Tai. Clinical Validation of a Deep Learning-Based 2D Ultrasound Steatosis Algorithm: Cutoff Transferability, Scanner Generalizability, and Comparison with FibroScan. Diagnostics 2026; 16(2) doi: 10.3390/diagnostics16020267
|
| 21 |
Yang Yang, Jing Liu, Changxuan Sun, Yuwei Shi, Julianna C. Hsing, Aya Kamya, Cody Auston Keller, Neha Antil, Daniel Rubin, Hongxia Wang, Haochao Ying, Xueyin Zhao, Yi-Hsuan Wu, Mindie Nguyen, Ying Lu, Fei Yang, Pinton Huang, Ann W. Hsing, Jian Wu, Shankuan Zhu. Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. European Radiology 2023; 33(8) doi: 10.1007/s00330-023-09515-1
|
| 22 |
Hussein Al-ogaili. Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images. Wasit Journal of Computer and Mathematics Science 2022; 1(4) doi: 10.31185/wjcm.65
|
| 23 |
Luyao Shen, Richa Patel, Lindsey Negrete, Andy Shon, Simon Lemieux, Tie Liang, Stephan Altmayer, Priyanka Jha, Aya Kamaya. Qualitative assessment of hepatic steatosis on modern grayscale ultrasound: more accurate than previously thought?. Abdominal Radiology 2025; 50(12) doi: 10.1007/s00261-025-05008-5
|
| 24 |
Nilakash Mukherjee, Manab Debnath, Rajdeep Roy, Subhadeep Santra, Tanmoy Ghosh, Dishani Roy. Computational Intelligence in Communications and Business Analytics. Communications in Computer and Information Science 2026; 2862 doi: 10.1007/978-3-032-17187-0_11
|
| 25 |
Kumar Mohit, Rajeev Gupta, Basant Kumar. Contrastive Learned Self-Supervised Technique for Fatty Liver and Chronic Liver Identification. Biomedical Signal Processing and Control 2025; 100 doi: 10.1016/j.bspc.2024.106950
|
| 26 |
P. R. Wankhede, Devendra Bhuyar, Shrinivas Zanwar, Rohit Pawar, Mahendra R. Jadhav, Nisarg Gandhewar, Madhusudan B. Kulkarni, Manish Bhaiyya, Hossam Haick. Artificial Intelligence for Noninvasive Health Diagnostics. ACS Sensors 2025; 10(11) doi: 10.1021/acssensors.5c03171
|
| 27 |
Jennifer Tai, Adam P Harrison, Hui-Ming Chen, Chiu-Yi Hsu, Tse-Hwa Hsu, Cheng-Jen Chen, Wen-Juei Jeng, Ming-Ling Chang, Le Lu, Dar-In Tai. Acoustic radiation force impulse predicts long-term outcomes in a large-scale cohort: High liver cancer, low comorbidity in hepatitis B virus. World Journal of Gastroenterology 2023; 29(14): 2188-2201 doi: 10.3748/wjg.v29.i14.2188
|
| 28 |
Chia-Chien Kang, Tsang-En Wang, Chia-Yuan Liu, Ming-Jen Chen, Horng-Yuan Wang, Chen-Wang Chang, Ching-Wei Chang. Update on Imaging-based Noninvasive Methods for Assessing Hepatic Steatosis in Nonalcoholic Fatty Liver Disease. Journal of Medical Ultrasound 2024; 32(2) doi: 10.4103/jmu.jmu_88_23
|
| 29 |
Zhan Gao, Guanghua Tan, Chunlian Wang, Jianxin Lin, Bin Pu, Shengli Li, Kenli Li. Graph-enhanced ensembles of multi-scale structure perception deep architecture for fetal ultrasound plane recognition. Engineering Applications of Artificial Intelligence 2024; 136 doi: 10.1016/j.engappai.2024.108885
|
| 30 |
Quantification of Liver Fat and Fibrosis with Ultrasound: The Expanding Role of Elastography . Journal of Gandhara Medical and Dental Science 2026; 13(3) doi: 10.37762/jgmds.13-3.912
|
| 31 |
Guilin Meng, Mingzhu Su, Zhaolan Hu, Ji Hu, Yong Cai, Jia Xiao. Rehabilitation for Alcohol Use Disorder: Interdisciplinary Advances, Challenges, and Opportunities. ENGINEERING Health and Rehabilitation Engineering 2026; 1(1) doi: 10.2738/ENGHRE.2026.0008
|
| 32 |
Simone Kresevic, Mauro Giuffrè, Alberto Bortolotto, Deborah Bonazza, Emiliana Giacomello, Aleksandar Miladinović, Biagio Casagranda, Silvia Palmisano, Lory Saveria Crocè, Miloš Ajčević. A novel approach for liver steatosis assessment in ultrasound images using an adapted Vision-Language Foundation Model. Computers in Biology and Medicine 2026; 214 doi: 10.1016/j.compbiomed.2026.111904
|
| 33 |
Elena Codruta Gheorghe, Carmen Nicolau, Adina Kamal, Anca Udristoiu, Lucian Gruionu, Adrian Saftoiu. Artificial Intelligence (AI)-Enhanced Ultrasound Techniques Used in Non-Alcoholic Fatty Liver Disease: Are They Ready for Prime Time?. Applied Sciences 2023; 13(8) doi: 10.3390/app13085080
|
| 34 |
Rodrigo Marques, Jaime Santos, Alexandra André, José Silva. Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning. Sensors 2024; 24(23) doi: 10.3390/s24237568
|
| 35 |
H. Zamanian, A. Shalbaf, M.R. Zali, A.R. Khalaj, P. Dehghan, M. Tabesh, B. Hatami, R. Alizadehsani, Ru-San Tan, U. Rajendra Acharya. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005–2023). Computer Methods and Programs in Biomedicine 2024; 244 doi: 10.1016/j.cmpb.2023.107932
|
| 36 |
Longfei Ma, Rui Wang, Qiong He, Lijie Huang, Xingyue Wei, Xu Lu, Yanan Du, Jianwen Luo, Hongen Liao. Artificial intelligence-based ultrasound imaging technologies for hepatic diseases. iLIVER 2022; 1(4) doi: 10.1016/j.iliver.2022.11.001
|
| 37 |
Tanzilal Mustaqim, Sri Hidayati, Dyah Putri Rahmawati. Metaheuristic-Optimized ConvNeXt Architecture for Ultrasound-Based Liver Diseases Classification. 2025 8th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) 2025; doi: 10.1109/ISRITI68345.2025.11393136
|
| 38 |
Akshay Jagadeesh, Chanchanok Aramrat, Santosh Rai, Fathima Hana Maqsood, Adarsh Kibballi Madhukeshwar, Santhi Bhogadi, Judith Lieber, Hemant Mahajan, Santosh Kumar Banjara, Alexandra Lewin, Sanjay Kinra, Poppy Mallinson. Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India. The Lancet Regional Health - Southeast Asia 2025; 40 doi: 10.1016/j.lansea.2025.100644
|
| 39 |
Kartik Bose, Priya Mudgil, Pankaj Gupta, Samonee Ralmilay, Niharika Dutta, Ajay Gulati, Naveen Kalra, Madhumita Premkumar, Sunil Taneja, Nipun Verma, Arka De, Ajay Duseja. Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset. Abdominal Radiology 2025; 51(6) doi: 10.1007/s00261-025-05309-9
|
| 40 |
Karthi V, Manikandan K, Hariomprakaas M, Madhu Mitha M, Pradeep S, Saik Muhamed Aslam J. A CNN Framework for Accurate Fatty Liver Disease Prediction Using EfficientNetB3 and DenseNet121. 2026 International Conference on Signal, Systems, and Computing for Next-Gen Automation (ICSSCNA) 2026; doi: 10.1109/ICSSCNA68616.2026.11546710
|
| 41 |
Ruijuan Wang, Chang Liu, Mei Xue, Jun Qian, Yue Hu. Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review. Computers in Biology and Medicine 2026; 208 doi: 10.1016/j.compbiomed.2026.111619
|
| 42 |
Adam P. Harrison, Bowen Li, Tse-Hwa Hsu, Cheng-Jen Chen, Wan-Ting Yu, Jennifer Tai, Le Lu, Dar-In Tai. Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes. Diagnostics 2023; 13(20) doi: 10.3390/diagnostics13203225
|