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Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD.

Methods

In a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists’ consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants’ characteristics on the correctness of 2S-NNet by logistic regression.

Results

Area under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79–0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54–0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05).

Conclusions

By using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design.

Key Points

• Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility.

• The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84–0.93 vs. 0.54–0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology.

• The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.

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Abbreviations

AUROC:

Area under the receiver operating characteristic curve

BL:

“Bright liver”

DLS:

Deep learning system

DXA:

Dual-energy X-ray absorptiometry

IDB:

Intrahepatic ducts blurring

IVD:

Impaired visualization of more than a half of the diaphragm

LMICs:

Low-middle-income countries

NAFLD:

Nonalcoholic fatty liver disease

NPV:

Negative predictive value

PPV:

Positive predictive value

References

  1. Le MH, Yeo YH, Li X et al (2021) 2019 Global NAFLD prevalence: a systematic review and meta-analysis. Clin Gastroenterol Hepatol S1542–3565(21):01280–01285

    Google Scholar 

  2. Wu Y, Zheng Q, Zou B et al (2020) The epidemiology of NAFLD in Mainland China with analysis by adjusted gross regional domestic product: a meta-analysis. Hepatol Int 14:259–269

    Article  PubMed  Google Scholar 

  3. Zhou F, Zhou J, Wang W et al (2019) Unexpected rapid increase in the burden of NAFLD in China From 2008 to 2018: a systematic review and meta-analysis. Hepatology 70:1119–1133

  4. Wang XJ, Malhi H (2018) Nonalcoholic fatty liver disease. Ann Intern Med 169(9):ITC65–ITC80

    Article  PubMed  Google Scholar 

  5. Fan JG, Wei L, Zhuang H et al (2019) Guidelines of prevention and treatment of nonalcoholic fatty liver disease (2018, China). J Dig Dis 20(4):163–173

    Article  PubMed  Google Scholar 

  6. Hardy T, Oakley F, Anstee QM, Day CP (2016) Nonalcoholic fatty liver disease: pathogenesis and disease spectrum. Annu Rev Pathol 11:451–496

    Article  CAS  PubMed  Google Scholar 

  7. Huang DQ, El-Serag HB, Loomba R (2021) Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol 18:223–238

    Article  PubMed  Google Scholar 

  8. Rinella ME (2015) Nonalcoholic fatty liver disease: a systematic review. JAMA 313:2263–2273

    Article  CAS  PubMed  Google Scholar 

  9. Brunner KT, Henneberg CJ, Wilechansky RM, Long MT (2019) Nonalcoholic fatty liver disease and obesity treatment. Curr Obes Rep 8:220–228

    Article  PubMed  PubMed Central  Google Scholar 

  10. Starekova J, Hernando D, Pickhardt PJ, Reeder SB (2021) Quantification of liver fat content with CT and MRI: state of the art. Radiology 301:250–262

    Article  PubMed  Google Scholar 

  11. Ferraioli G, Soares Monteiro LB (2019) Ultrasound-based techniques for the diagnosis of liver steatosis. World J Gastroenterol 25:6053–6062

    Article  PubMed  PubMed Central  Google Scholar 

  12. Byra MMB, Styczynski G, G, C Szmigielski C, et al (2018) Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 13(12):1895–1903

    Article  PubMed  PubMed Central  Google Scholar 

  13. Biswas M, Kuppili V, Edla DR et al (2018) Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 155:165–177

    Article  PubMed  Google Scholar 

  14. Han A, Byra M, Heba E et al (2020) Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology 295:342–350

    Article  PubMed  Google Scholar 

  15. Kim T, Lee DH, Park E-K, Choi S (2021) Deep learning techniques for fatty liver using multi-view ultrasound images scanned by different scanners: development and validation study. JMIR Med Inform 9:e30066

    Article  PubMed  PubMed Central  Google Scholar 

  16. Che H, Brown LG, Foran DJ, Nosher JL, Hacihaliloglu I (2021) Liver disease classification from ultrasound using multi-scale CNN. Int J Comput Assist Radiol Surg 16:1537–1548

    Article  PubMed  Google Scholar 

  17. Zamanian H, Mostaar A, Azadeh P, Ahmadi M (2021) Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images. J Biomed Phys Eng 11:73–84

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen J-R, Chao Y-P, Tsai Y-W et al (2020) Clinical value of information entropy compared with deep learning for ultrasound grading of hepatic steatosis. Entropy (Basel) 22:E1006

  19. Sanabria SJ, Pirmoazen AM, Dahl J, Kamaya A, El Kaffas A (2022) Comparative study of raw ultrasound data representations in deep learning to classify hepatic steatosis. Ultrasound Med Biol 48:2060–2078

    Article  PubMed  Google Scholar 

  20. Byra M, Han A, Boehringer AS et al (2022) Liver fat assessment in multiview sonography using transfer learning with convolutional neural networks. J Ultrasound Med 41:175–184

  21. Cha DI, Kang TW, Min JH et al (2021) Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography. Ultrasonography 40:565–574

  22. Troelstra MA, Van Dijk A-M, Witjes JJ et al (2022) Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease. Front Physiol 13:942495

    Article  PubMed  PubMed Central  Google Scholar 

  23. Constantinescu EC, Udriștoiu A-L, UdriștoiuȘtefan C et al (2021) Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images. Med Ultrason 23:135–139

    PubMed  Google Scholar 

  24. Li B, Tai D-I, Yan K et al (2022) Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning. World J Gastroenterol 28:2494–2508

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rhyou S-Y, Yoo J-C (2021) Cascaded deep learning neural network for automated liver steatosis diagnosis using ultrasound images. Sensors (Basel) 21:5304

  26. Chou T-H, Yeh H-J, Chang C-C et al (2021) Deep learning for abdominal ultrasound: a computer-aided diagnostic system for the severity of fatty liver. J Chin Med Assoc 84:842–850

  27. Ci C, Tb C, Nh L et al (2019) Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study. Proc Inst Mech Eng H 233(11):1100–1112

    Article  Google Scholar 

  28. Cao W, An X, Cong L, Lyu C, Zhou Q, Guo R (2020) Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease. J Ultrasound Med 39(1):51–59

    Article  PubMed  Google Scholar 

  29. Saba L, Dey N, Ashour AS et al (2016) Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm. Comput Methods Programs Biomed 130:118–134

    Article  PubMed  Google Scholar 

  30. Reddy DS, Bharath R, Rajalakshmi P (2018) A novel computer-aided diagnosis framework using deep learning for classification of fatty liver disease in ultrasound imaging. 2018 IEEE 20th international conference on e-health networking, applications and services (Healthcom). IEEE, 2018:1–5

  31. Zhang P, Ge Z, Wang H et al (2018) Prolactin improves hepatic steatosis via CD36 pathway. J Hepatol 68:1247–1255

    Article  CAS  PubMed  Google Scholar 

  32. Dasarathy S, Dasarathy J, Khiyami A, Joseph R, Lopez R, McCullough AJ (2009) Validity of real time ultrasound in the diagnosis of hepatic steatosis: a prospective study. J Hepatol 51:1061–1067

    Article  PubMed  PubMed Central  Google Scholar 

  33. Min Y, Zhao X, Stafford RS et al (2021) Cohort profile: WELL Living Laboratory in China (WELL-China). Int J Epidemiol 50(5):1432–1443

    Article  PubMed  Google Scholar 

  34. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016:770–778

  35. Han D, Kim J, Kim J (2017) Deep pyramidal residual networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017:5927–5935

  36. Tan M, Le QV (2020) EfficientNet: rethinking model scaling for convolutional neural networks. International conference on machine learning. PMLR, 2019:6105–6114

  37. Sandler M, Howard A, Zhu M, et al (2019) MobileNetV2: inverted residuals and linear bottlenecks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018:4510–4520

  38. Bedogni G, Bellentani S, Miglioli L et al (2006) The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol 6:33

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lee J-H, Kim D, Kim HJ et al (2010) Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis 42:503–508

  40. Li L, You W, Ren W (2017) The ZJU index is a powerful index for identifying NAFLD in the general Chinese population. Acta Diabetol 54:905–911

    Article  PubMed  Google Scholar 

  41. Yip TC-F, Ma AJ, Wong VW-S et al (2017) Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment Pharmacol Ther 46:447–456

    Article  CAS  PubMed  Google Scholar 

  42. Zhou Y-J, Zhou Y-F, Zheng J-N et al (2017) NAFL screening score: a basic score identifying ultrasound-diagnosed non-alcoholic fatty liver. Clin Chim Acta 475:44–50

  43. Kong X, Ai B, Kong Y et al (2019) Artificial intelligence: a key to relieve China’s insufficient and unequally-distributed medical resources. Am J Transl Res 11:2632–2640

    PubMed  PubMed Central  Google Scholar 

  44. Byrne CD, Targher G (2016) EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease: is universal screening appropriate? Diabetologia 59:1141–1144

    Article  CAS  PubMed  Google Scholar 

  45. Subramanya MB, Kumar V, Mukherjee S, Saini M (2015) A CAD system for B-mode fatty liver ultrasound images using texture features. J Med Eng Technol 39(2):123–130

    Article  CAS  PubMed  Google Scholar 

  46. Li G, Luo Y, Deng W, Xu X, Liu A, Song E (2008) Computer aided diagnosis of fatty liver ultrasonic images based on support vector machine. Annu Int Conf IEEE Eng Med Biol Soc 2008:4768–4771

    PubMed  Google Scholar 

  47. Chen J-R, Chao Y-P, Tsai Y-W et al (2020) Clinical value of information entropy compared with deep learning for ultrasound grading of hepatic steatosis. Entropy (Basel) 22(9):1006

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Initial foundational funding for the Stanford Wellness Living laboratory (WELL) was provided by Amway via an unrestricted gift through the Nutrilite Health Institute Wellness Fund to Stanford University. Through Zhejiang University, the Cyrus Tang Foundation, Hsun K Chou Fund, and Zhejiang University Education Foundation also provided important financial support for the study. Ying Lu kindly provided statistical advice for this manuscript.

Funding

Initial foundational funding for the Stanford Wellness Living laboratory (WELL) was provided by Amway via an unrestricted gift through the Nutrilite Health Institute Wellness Fund to Stanford University. Through Zhejiang University, the Cyrus Tang Foundation, Hsun K Chou Fund, and Zhejiang University Education Foundation also provided important financial support for the study.

Author information

Authors and Affiliations

  1. Chronic Disease Research Institute, The Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China

    Yang Yang, Changxuan Sun, Yuwei Shi, Xueyin Zhao, Fei Yang & Shankuan Zhu

  2. Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China

    Yang Yang, Changxuan Sun, Yuwei Shi, Xueyin Zhao, Fei Yang & Shankuan Zhu

  3. College of Computer Science and Technology, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, China

    **g Liu

  4. Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine, Stanford, CA, USA

    Julianna C. Hsing

  5. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA

    Aya Kamya, Cody Auston Keller, Neha Antil & Daniel Rubin

  6. Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Daniel Rubin & Ying Lu

  7. Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Hongxia Wang

  8. Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China

    Haochao Ying

  9. Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 780 Welch Road, CJ Huang Building, Suite 250D, Stanford, CA, 94305, USA

    Yi-Hsuan Wu & Ann W. Hsing

  10. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA

    Mindie Nguyen, Ying Lu & Ann W. Hsing

  11. Division of Gastroenterology and Hepatology, Stanford University Medical Center, Stanford, CA, USA

    Mindie Nguyen

  12. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Ying Lu & Ann W. Hsing

  13. Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Pinton Huang

  14. Second Affiliated Hospital School of Medicine, School of Public Health, and Institute of Wenzhou, Zhejiang University, Hangzhou, 310058, China

    Jian Wu

Authors
  1. Yang Yang
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  2. **g Liu
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Corresponding authors

Correspondence to Ann W. Hsing, Jian Wu or Shankuan Zhu.

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Guarantor

The scientific guarantor of this publication is Shankuan Zhu (one of the corresponding authors).

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Ying Lu kindly provided statistical advice for this manuscript, and he is one of the authors.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.The study has obtained the Institution Review Board approvals from both Zhejiang University (No. ZGL201507-3) and Stanford University (IRB-35020).

Methodology

• retrospective

• cross-sectional study

• performed at one institution

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Publisher's Note

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Yang Yang and **g Liu contributed equally as first authors.

Ann W. Hsing, Jian Wu, and Shankuan Zhu contributed equally as senior authors.

Summary statement: By using two-section design, our newly developed deep learning system (2S-NNet) had high accuracies and AUROCs for hepatic steatosis and NAFLD detection based on radiologists’ diagnosis.

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Yang, Y., Liu, J., Sun, C. et al. Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. Eur Radiol 33, 5894–5906 (2023). https://doi.org/10.1007/s00330-023-09515-1

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