Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49.

Article  PubMed  Google Scholar 

Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Pineros M, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941-53.

Article  CAS  PubMed  Google Scholar 

McGlynn KA, Petrick JL, London WT. Global epidemiology of hepatocellular carcinoma: an emphasis on demographic and regional variability. Clin Liver Dis. 2015;19(2):223-38.

Article  PubMed Central  PubMed  Google Scholar 

Vogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. Lancet. 2022;400(10360):1345-62.

Article  CAS  PubMed  Google Scholar 

Ding J, Wen Z. Survival improvement and prognosis for hepatocellular carcinoma: analysis of the SEER database. BMC Cancer. 2021;21(1):1157.

Article  CAS  PubMed Central  PubMed  Google Scholar 

Njei B, Rotman Y, Ditah I, Lim JK. Emerging trends in hepatocellular carcinoma incidence and mortality. Hepatology. 2015;61(1):191-9.

Article  PubMed  Google Scholar 

Mittal S, Kanwal F, Ying J, Chung R, Sada YH, Temple S, et al. Effectiveness of surveillance for hepatocellular carcinoma in clinical practice: A United States cohort. J Hepatol. 2016;65(6):1148-54.

Article  PubMed Central  PubMed  Google Scholar 

Collier J, Sherman M. Screening for hepatocellular carcinoma. Hepatology. 1998;27(1):273-8.

Article  CAS  PubMed  Google Scholar 

Liang Y, Xu F, Guo Y, Lai L, Jiang X, Wei X, et al. Diagnostic performance of LI-RADS for MRI and CT detection of HCC: A systematic review and diagnostic meta-analysis. Eur J Radiol. 2021;134:109404.

Article  PubMed  Google Scholar 

Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, et al. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology. 2022;304(1):50-62.

Article  PubMed  Google Scholar 

Han SH, Kim KW, Kim S, Youn YC. Artificial Neural Network: Understanding the Basic Concepts without Mathematics. Dement Neurocogn Disord. 2018;17(3):83-9.

Article  PubMed Central  PubMed  Google Scholar 

IBM. What is deep learning? [Available from: https://tinyurl.com/2p8drtfk.

Richardson ML, Garwood ER, Lee Y, Li MD, Lo HS, Nagaraju A, et al. Noninterpretive Uses of Artificial Intelligence in Radiology. Acad Radiol. 2021;28(9):1225-35.

Article  PubMed  Google Scholar 

Goldberg JE, Rosenkrantz AB. Artificial Intelligence and Radiology: A Social Media Perspective. Curr Probl Diagn Radiol. 2019;48(4):308-11.

Article  PubMed  Google Scholar 

Baltzer PA, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol. 2013;23(8):2051-60.

Article  PubMed  Google Scholar 

Zsoter N, Bandi P, Szabo G, Toth Z, Bundschuh RA, Dinges J, et al. PET-CT based automated lung nodule detection. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4974-7.

PubMed  Google Scholar 

Huang W, Tan ZM, Lin Z, Huang GB, Zhou J, Chui CK, et al. A semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with Extreme Learning Machine. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3752-5.

CAS  PubMed  Google Scholar 

Mewes A, Hensen B, Wacker F, Hansen C. Touchless interaction with software in interventional radiology and surgery: a systematic literature review. Int J Comput Assist Radiol Surg. 2017;12(2):291-305.

Article  PubMed  Google Scholar 

Mitrea D, Badea R, Mitrea P, Brad S, Nedevschi S. Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods. Sensors (Basel). 2021;21(6).

Ding Y, Ruan S, Wang Y, Shao J, Sun R, Tian W, et al. Novel deep learning radiomics model for preoperative evaluation of hepatocellular carcinoma differentiation based on computed tomography data. Clin Transl Med. 2021;11(11):e570.

Article  PubMed Central  PubMed  Google Scholar 

Administration TUFaD. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices fda.gov2022 [Available from: shorturl.at/lmX29.

Sanmarchi F, Fanconi C, Golinelli D, Gori D, Hernandez-Boussard T, Capodici A. Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol. 2023.

Ghozy S, Azzam AY, Kallmes KM, Matsoukas S, Fifi JT, Luijten SPR, et al. The diagnostic performance of artificial intelligence algorithms for identifying M2 segment middle cerebral artery occlusions: A systematic review and meta-analysis. J Neuroradiol. 2023.

Widaatalla Y, Wolswijk T, Adan F, Hillen LM, Woodruff HC, Halilaj I, et al. The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review. J Eur Acad Dermatol Venereol. 2023.

Almasan O, Leucuta DC, Hedesiu M, Muresanu S, Popa SL. Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis. J Clin Med. 2023;12(3).

Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, et al. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel). 2022;12(5).

Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, et al. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019;40(25):2058-73.

Article  PubMed Central  PubMed  Google Scholar 

Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol. 2018;15(3 Pt B):504–8.

Higgins JPT TJ, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane, 2022.

Dwamena B. MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies. 2009.

Harbord RM, Whiting P. Metandi: meta-analysis of diagnostic accuracy using hierarchical logistic regression. The Stata Journal. 2009;9(2):211-29.

Article  Google Scholar 

Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015;13:1.

Article  PubMed Central  PubMed  Google Scholar 

Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med. 2019;170(1):51-8.

Article  PubMed  Google Scholar 

Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58(9):882-93.

Article  PubMed  Google Scholar 

Shiraishi J, Sugimoto K, Moriyasu F, Kamiyama N, Doi K. Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography. Medical Physics. 2008;35(5):1734–46.

Sugimoto K, Shiraishi J, Moriyasu F, Doi K. Computer-aided Diagnosis of Focal Liver Lesions by Use of Physicians' Subjective Classification of Echogenic Patterns in Baseline and Contrast-enhanced Ultrasonography. Academic Radiology. 2009;16(4):401–11.

Mittal D, Kumar V, Saxena SC, Khandelwal N, Kalra N. Neural network based focal liver lesion diagnosis using ultrasound images. Computerized Medical Imaging and Graphics. 2011;35(4):315-23.

Article  PubMed  Google Scholar 

Streba CT, Ionescu M, Gheonea DI, Sandulescu L, Ciurea T, Saftoiu A, et al. Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors. World Journal of Gastroenterology. 2012;18(32):4427-34.

Article  PubMed Central  PubMed  Google Scholar 

Virmani J, Kumar V, Kalra N, Khandelwal N. Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound. Journal of Digital Imaging. 2014;27(4):520-37.

Article  PubMed Central  PubMed  Google Scholar 

Yamakawa M, Shiina T, Nishida N, Kudo M, editors. Computer aided diagnosis system developed for ultrasound diagnosis of liver lesions using deep learning. IEEE International Ultrasonics Symposium, IUS; 2019.

Brehar R, Mitrea DA, Vancea F, Marita T, Nedevschi S, Lupsor-Platon M, et al. Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images. Sensors (Basel). 2020;20(11).

Huang Q, Pan F, Li W, Yuan F, Hu H, Huang J, et al. Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatiooral Diagnostic Semantics. IEEE Journal of Biomedical and Health Informatics. 2020;24(10):2860-9.

Article  PubMed  Google Scholar 

Ren S, Li Q, Liu S, Qi Q, Duan S, Mao B, et al. Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study. Frontiers in Oncology. 2021;11.

Tiyarattanachai T, Apiparakoon T, Marukatat S, Sukcharoen S, Geratikornsupuk N, Anukulkarnkusol N, et al. Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. PLoS ONE. 2021;16(6 June).

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. European Radiology. 2021;31(11):8733-42.

Article  PubMed Central  PubMed  Google Scholar 

Nishida N, Yamakawa M, Shiina T, Mekada Y, Nishida M, Sakamoto N, et al. Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts. Journal of Gastroenterology. 2022;57(4):309-21.

Article  PubMed Central  PubMed  Google Scholar 

Zhang WB, Hou SZ, Chen YL, Mao F, Dong Y, Chen JG, et al. Deep Learning for Approaching Hepatocellular Carcinoma Ultrasound Screening Dilemma: Identification of α-Fetoprotein-Negative Hepatocellular Carcinoma From Focal Liver Lesion Found in High-Risk Patients. Frontiers in Oncology. 2022;12.

Tangruangkiat S, Chaiwongkot N, Pamarapa C, Rawangwong T, Khunnarong A, Chainarong C, et al. Diagnosis of focal liver lesions from ultrasound images using a pretrained residual neural network. J Appl Clin Med Phys. 2024;25(1):e14210.

Article  PubMed  Google Scholar 

Urhut MC, Sandulescu LD, Streba CT, Mamuleanu M, Ciocalteu A, Cazacu SM, et al. Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians. Diagnostics (Basel). 2023;13(21).

Azimi Nanvaee F, Setayeshi S. Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier. Hepat Mon. 2023;23(1):e136213.

Google Scholar 

Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng. 1998;45(6):783-94.

Article  CAS  PubMed  Google Scholar 

Kumar SS, Moni RS. Diagnosis of liver tumour from CT images using contourlet transform. International Journal of Biomedical Engineering and Technology. 2011;7(3):276-90.

Article  Google Scholar 

Das A, Das P, Panda SS, Sabut S. Adaptive fuzzy clustering-based texture analysis for classifying liver cancer in abdominal CT images. International Journal of Computational Biology and Drug Design. 2018;11(3):192-208.

Article  Google Scholar 

Nayak A, Baidya Kayal E, Arya M, Culli J, Krishan S, Agarwal S, et al. Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT. International Journal of Computer Assisted Radiology and Surgery. 2019;14(8):1341-52.

Article  PubMed