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ISSN: 2392-1099
Clinical and Experimental Hepatology
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Original paper

Deep learning-based classification of gallbladder lesions in patients with non-diagnostic (GB-RADS 0) ultrasound

Pankaj Gupta
1
,
Ruby Siddiqui
1
,
Thakur D. Yadav
1
,
Lileswar Kaman
1
,
Gaurav Prakash
1
,
Parikshaa Gupta
1
,
Uma N. Saikia
1
,
Usha Dutta
1

  1. PGIMER, Chandigarh, India
Clin Exp HEPATOL 2024; 10, 4:
DOI: https://doi.org/10.5114/ceh.2024.145424
Online publish date: 2024/12/11
Article file
- 00677 - Deep.pdf  [0.27 MB]
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1. Sung H, Ferlay J, Siegel RL, 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: 209-249.
2. Gupta P, Kumar M, Sharma V, et al. Evaluation of gallbladder wall thickening: a multimodality imaging approach. Expert Rev Gastroenterol Hepatol 2020; 14: 463-473.
3. Gupta P, Dutta U, Rana P, et al. Gallbladder reporting and data system (GB-RADS) for risk stratification of gallbladder wall thickening on ultrasonography: an international expert consensus. Abdom Radiol (NY) 2022; 47: 554-565.
4. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
5. Zhang YD, Dong Z, Wang SH, et al. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Inf Fusion 2020; 64: 149-187.
6. Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. Int J Multimed Inf Retr 2022; 11: 19-38.
7. Zhang YD, Dong Z, Wang SH, et al. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Inf Fusion 2020; 64: 149-187.
8. Jeong Y, Kim JH, Chae HD, et al. Deep learning-based decision support system for the diagnosis of neoplastic gallbladder polyps on ultrasonography: Preliminary results. Sci Rep 2020; 10: 7700.
9. Jang SI, Kim YJ, Kim EJ, et al. Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions. J Gastroenterol Hepatol 2021; 36: 3548-3555.
10. Basu S, Gupta M, Rana P, et al. Surpassing the human accuracy: Detecting gallbladder cancer from USG images with curriculum learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022; 20886-20896.
11. Basu S, Gupta M, Rana P, et al. RadFormer: Transformers with global-local attention for interpretable and accurate Gallbladder Cancer detection. Med Image Anal 2023; 83: 102676.
12. Gupta P, Basu S, Rana P, et al. Deep-learning enabled ultrasound based detection of gallbladder cancer in northern India: a prospective diagnostic study. Lancet Reg Health Southeast Asia 2023; 24: 100279.
13. Gao Y, Zeng S, Xu X, et al. Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. Lancet Digit Health 2022; 4: e179-e187.
14. Gupta P, Marodia Y, Bansal A, et al. Imaging-based algorithmic approach to gallbladder wall thickening. World J Gastroenterol 2020; 26: 6163-6181.
15. Kalage D, Gupta P, Gulati A, et al. Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology. Eur Radiol 2023; 33: 4981-4993.
Copyright: © Clinical and Experimental Hepatology. This is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/) enables reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.
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