Skip to main content

Advertisement

Springer Nature Link
Log in

Predicting pancreatic diseases from fundus images using deep learning

  • Research
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Pancreatic cancer (PC) is an extremely deadly cancer, with mortality rates closely tied to its frequency of occurrence. By the time of diagnosis, pancreatic cancer often presents at an advanced stage, and has often spread to other parts of the body. Due to the poor survival outcomes, PDAC is the fifth leading cause of global cancer death. The 5-year relative survival rate of pancreatic cancer was about 6% and the lowest level in all cancers. Currently, there are no established guidance for screening individuals at high risk for pancreatic cancer, including those with a family history of the pancreatic disease or chronic pancreatitis (CP). With the development of medicine, fundus maps can now predict many systemic diseases. Subsequently, the association between ocular changes and a few pancreatic diseases was also discovered. Therefore, our objective is to construct a deep learning model aimed at identifying correlations between ocular features and significant pancreatic ailments. The utilization of AI and fundus images has extended beyond the investigation of ocular disorders. Hence, in order to solve the tasks of PC and CP classification, we propose a brand new deep learning model (PANet) that integrates pre-trained CNN network, multi-scale feature modules, attention mechanisms, and an FC classifier. PANet adopts a ResNet34 backbone and selectively integrates attention modules to construct its fundamental architecture. To enhance feature extraction capability, PANet combines multi-scale feature modules before the attention module. Our model is trained and evaluated using a dataset comprising 1300 fundus images. The experimental outcomes illustrate the successful realization of our objectives, with the model achieving an accuracy of 91.50% and an area under the receiver operating characteristic curve (AUC) of 96.00% in PC classification, and an accuracy of 95.60% and an AUC of 99.20% in CP classification. Our study establishes a characterizing link between ocular features and major pancreatic diseases, providing a non-invasive, convenient, and complementary method for screening and detection of pancreatic diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

No datasets were generated or analyzed during the current study.

References

  1. Al-Jebrni, A.H., Ali, S.G., Li, H., et al.: Sthy-net: a feature fusion-enhanced dense-branched modules network for small thyroid nodule classification from ultrasound images. Vis. Comput. 39(8), 3675–3689 (2023)

    MATH  Google Scholar 

  2. Aouaidjia, K., Sheng, B., Li, P., et al.: Efficient body motion quantification and similarity evaluation using 3-D joints skeleton coordinates. IEEE Trans. Syst. Man Cybern. Syst. 51(5), 2774–2788 (2019)

    MATH  Google Scholar 

  3. Bahr, T., Vu, T.A., Tuttle, J.J., et al.: Deep learning and machine learning algorithms for retinal image analysis in neurodegenerative disease: systematic review of datasets and models. Transl. Vision Sci. Technol. 13(2), 16–16 (2024)

    MATH  Google Scholar 

  4. Campo, S.M.A., Gasparri, V., Catarinelli, G., et al.: Acute pancreatitis with Purtscher’s retinopathy: case report and review of the literature. Dig. Liver Dis. 32(8), 729–732 (2000)

    Google Scholar 

  5. Chari, S.T., Mohan, V., Pitchumoni, C.S., et al.: Risk of pancreatic carcinoma in tropical calcifying pancreatitis: an epidemiologic study. Pancreas 9(1), 62–66 (1994)

    Google Scholar 

  6. Cheema, M.N., Nazir, A., Yang, P., et al.: Modified GAN-cAED to minimize risk of unintentional liver major vessels cutting by controlled segmentation using CTA/SPET-CT. IEEE Trans. Industr. Inf. 17(12), 7991–8002 (2021)

    MATH  Google Scholar 

  7. Chen, W., Butler, R.K., Zhou, Y., et al.: Prediction of pancreatic cancer based on imaging features in patients with duct abnormalities. Pancreas 49(3), 413–419 (2020)

    MATH  Google Scholar 

  8. Chikumba, S., Hu, Y., Luo, J.: Deep learning-based fundus image analysis for cardiovascular disease: a review. Ther. Adv. Chronic Dis. 14, 20406223231209896 (2023)

    Google Scholar 

  9. Conroy, T., Pfeiffer, P., Vilgrain, V., et al.: Pancreatic cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann. Oncol. 34(11), 987–1002 (2023)

    Google Scholar 

  10. Dai, L., Sheng, B., Chen, T., et al.: A deep learning system for predicting time to progression of diabetic retinopathy. Nat. Med. 30(2), 584–594 (2024)

    MATH  Google Scholar 

  11. Dai, L., Wu, L., Li, H., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 3242 (2021)

    MATH  Google Scholar 

  12. Gao, S.H., Cheng, M.M., Zhao, K., et al.: Res2net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2019)

    MATH  Google Scholar 

  13. Gao, X., Wang, X.: Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: a preliminary study. Diagn. Interv. Imaging 101(2), 91–100 (2020)

    MathSciNet  MATH  Google Scholar 

  14. Gorris, M., Hoogenboom, S.A., Wallace, M.B., et al.: Artificial intelligence for the management of pancreatic diseases. Dig. Endosc. 33(2), 231–241 (2021)

    MATH  Google Scholar 

  15. Guan, Z., Li, H., Liu, R., et al.: Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Rep. Med. 4, 101213 (2023)

    Google Scholar 

  16. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

  17. Hemelings, R., Elen, B., Schuster, A.K., et al.: A generalizable deep learning regression model for automated glaucoma screening from fundus images. NPJ Digit. Med. 6(1), 112 (2023)

    Google Scholar 

  18. Hu, J.X., Zhao, C.F., Chen, W.B., et al.: Pancreatic cancer: a review of epidemiology, trend, and risk factors. World J. Gastroenterol. 27(27), 4298 (2021)

    MATH  Google Scholar 

  19. Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)

  20. Huang, S., Liu, X., Tan, T., et al.: TransMRSR: transformer-based self-distilled generative prior for brain MRI super-resolution. Vis. Comput. 39(8), 3647–3659 (2023)

    MATH  Google Scholar 

  21. Karambakhsh, A., Sheng, B., Li, P., et al.: SparseVoxNet: 3-D object recognition with sparsely aggregation of 3-D dense blocks. IEEE Trans. Neural Netw. Learn. Syst. 35(1), 532–546 (2022)

    Google Scholar 

  22. Kelly, R.J., Bever, K., Chao, J., et al.: Society for immunotherapy of cancer (SITC) clinical practice guideline on immunotherapy for the treatment of gastrointestinal cancer. J. Immunother. Cancer 11(6), e006658 (2023)

    Google Scholar 

  23. Koonce, B., Koonce, B.E.: Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization. Apress, New York (2021)

    Google Scholar 

  24. Kumar, K.V., Soora, N.R., Santoshkumar, N.C.: Fundus image classification for the early detection of issues in the DR for the effective disease diagnosis. J. Comput. Allied Intell. 1(01), 27–40 (2023)

    Google Scholar 

  25. Lee, H.A., Chen, K.W., Hsu, C.Y.: Prediction model for pancreatic cancer—a population-based study from NHIRD. Cancers 14(4), 882 (2022)

    MATH  Google Scholar 

  26. Li, J., Zhang, P., Wang, T., et al.: DSMT-Net: dual self-supervised multi-operator transformation for multi-source endoscopic ultrasound diagnosis. IEEE Trans. Med. Imaging 43, 64–75 (2023)

    MATH  Google Scholar 

  27. Li, P., Liang, L., Gao, Z., et al.: AMD-Net: automatic subretinal fluid and hemorrhage segmentation for wet age-related macular degeneration in ocular fundus images. Biomed. Signal Process. Control 80, 104262 (2023)

    MATH  Google Scholar 

  28. Li, T., Bo, W., Hu, C., et al.: Applications of deep learning in fundus images: a review. Med. Image Anal. 69, 101971 (2021)

    MATH  Google Scholar 

  29. Li, Y., Zhang, R., Dong, L., et al.: Predicting systemic diseases in fundus images: systematic review of setting, reporting, bias, and models’ clinical availability in deep learning studies. Eye 38, 1246–1251 (2024)

    MATH  Google Scholar 

  30. Lim, G., Lim, Z.W., Xu, D., et al. Feature isolation for hypothesis testing in retinal imaging: an ischemic stroke prediction case study. In: Proceedings of the AAAI conference on artificial intelligence 33(1): 9510–9515 (2019)

  31. Liu, R., Ou, L., Sheng, B., et al.: Mixed-weight neural bagging for detecting $ m^ 6A $ modifications in SARS-CoV-2 RNA sequencing. IEEE Trans. Biomed. Eng. 69(8), 2557–2568 (2022)

    MATH  Google Scholar 

  32. Liu, R., Wang, X., Wu, Q., et al.: DeepDRiD: diabetic retinopathy-grading and image quality estimation challenge. Patterns 3(6), 100512 (2022)

    Google Scholar 

  33. Manikandan, J., Krishna, B.V., Sasivarma, C., et al.: Cataract fundus image detection using hybrid deep learning model. In: International conference on computational intelligence in data science. Cham: Springer Nature Switzerland pp. 300–313 (2023)

  34. Mayer, C., Khoramnia, R.: Purtscher-like retinopathy caused by acute pancreatitis. The Lancet 378(9803), 1653 (2011)

    MATH  Google Scholar 

  35. McGuigan, A., Kelly, P., Turkington, R.C., et al.: Pancreatic cancer: a review of clinical diagnosis, epidemiology, treatment and outcomes. World J. Gastroenterol. 24(43), 4846 (2018)

    Google Scholar 

  36. Midha, S., Chawla, S., Garg, P.K.: Modifiable and non-modifiable risk factors for pancreatic cancer: a review. Cancer Lett. 381(1), 269–277 (2016)

    MATH  Google Scholar 

  37. Mitani, A., Huang, A., Venugopalan, S., et al.: Detection of anaemia from retinal fundus images via deep learning. Nat. Biomed. Eng. 4(1), 18–27 (2020)

    Google Scholar 

  38. Nazir, A., Cheema, M.N., Sheng, B., et al.: Living donor-recipient pair matching for liver transplant via ternary tree representation with cascade incremental learning. IEEE Trans. Biomed. Eng. 68(8), 2540–2551 (2021)

    MATH  Google Scholar 

  39. Pandey, R., Rana, S.S., Gupta, V., et al.: Retino-choroidal changes in patients with acute pancreatitis: a prospective analysis of a novel biomarker. Pancreatology 20(8), 1604–1610 (2020)

    MATH  Google Scholar 

  40. Paul, W., Burlina, P., Mocharla, R., et al.: Accuracy of artificial intelligence in estimating best-corrected visual acuity from fundus photographs in eyes with diabetic macular edema. JAMA Ophthalmol. 141(7), 677–685 (2023)

    Google Scholar 

  41. Placido, D., Yuan, B., Hjaltelin, J.X., et al.: A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat. Med. 29(5), 1113–1122 (2023)

    MATH  Google Scholar 

  42. Qian, B., Chen, H., Wang, X., et al.: DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. Patterns (2024). https://doi.org/10.1016/j.patter.2024.100929

    Article  MATH  Google Scholar 

  43. Rahim, S., Sabri, K., Ells, A., et al.: Novel fundus image preprocessing for Retcam images to improve deep learning classification of retinopathy of prematurity. (2023) https://doi.org/10.48550/arXiv.2302.02524

  44. Rim, T.H., Lee, G., Kim, Y., et al.: Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. Lancet Digit. Health 2(10), e526–e536 (2020)

    MATH  Google Scholar 

  45. Sabanayagam, C., Xu, D., Ting, D.S.W., et al.: A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit. Health 2(6), e295–e302 (2020)

    Google Scholar 

  46. Shen, Z., Savvides, M.: Meal v2: boosting vanilla resnet-50 to 80%+ top-1 accuracy on imagenet without tricks (2020) https://doi.org/10.48550/arXiv.2009.08453

  47. Sheng, B., Guan, Z., Lim, L.L., et al.: Large language models for diabetes care: potentials and prospects. Sci. Bull. S2095–9273(24), 00004 (2024)

    MATH  Google Scholar 

  48. Simonyan K, Zisserman, A.: Very deep convolutional networks for large-scale image recognition. (2014) https://doi.org/10.48550/arXiv.1409.1556

  49. Steiner, M., del Mar, E.-O., Muñoz-Fernández, S.: Choroidal and retinal thickness in systemic autoimmune and inflammatory diseases: a review[J]. Surv. Ophthalmol. 64(6), 757–769 (2019)

    Google Scholar 

  50. Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence (2017)

  51. Tan, Y., Ma, Y., Rao, S., et al.: Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: a systematic review and meta-analysis. Eur. J. Ophthalmol. 34(2), 502–509 (2024)

    MATH  Google Scholar 

  52. Ting, D.S.W., Peng, L., Varadarajan, A.V., et al.: Deep learning in ophthalmology: the technical and clinical considerations. Prog. Retin. Eye Res. 72, 100759 (2019)

    MATH  Google Scholar 

  53. Viedma, I.A., Alonso-Caneiro, D., Read, S.A., et al.: Deep learning in retinal optical coherence tomography (OCT): a comprehensive survey. Neurocomputing 507, 247–264 (2022)

    Google Scholar 

  54. Visioli, G., Zeppieri, M., Iannucci, V., et al.: From bedside to diagnosis: the role of ocular fundus in systemic infections. J. Clin. Med. 12(23), 7216 (2023)

    MATH  Google Scholar 

  55. Wang, Q., Wu, B., Zhu, P., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11534–11542 (2020)

  56. Wong, T.Y., Klein, R., Couper, D.J., et al.: Retinal microvascular abnormalities and incident stroke: the atherosclerosis risk in communities study. Lancet 358(9288), 1134–1140 (2001)

    MATH  Google Scholar 

  57. Xiao, W., Huang, X., Wang, J.H., et al.: Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study. Lancet Digit. Health 3(2), e88–e97 (2021)

    Google Scholar 

  58. Xu, P.P., Liu, T.Y., Zhou, F., et al.: Artificial intelligence in coronary computed tomography angiography. Med. Plus 10, 100001 (2023)

    MATH  Google Scholar 

  59. Zhu, X., Xiong, Y., Dai, J., et al.: Deep feature flow for video recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2349–2358 (2017)

  60. Zou, K.H., O’Malley, A.J., Mauri, L.: Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115(5), 654–657 (2007)

    MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the participants of the study as well as Shanghai Sixth’s People’s Hospital and Shanghai Jiao Tong University for providing funding and organizational support. Many thanks to Tingting Li and Xuan Cai, two attending ophthalmologist, who have been practicing ophthalmology for more than 10 years, for reading fundus images in this study.

Funding

This work was supported by the College-level Project Fund of Shanghai Sixth People’s Hospital (Grant No. ynlc201909) and the Interdisciplinary Program of Shanghai Jiao Tong University (Project No. YG2022QN089).

Author information

Author notes

  1. Yiting Wu and Pinqi Fang have contributed equally to this work.

Authors and Affiliations

  1. College of Information Technology, Shanghai Ocean University, 999 Hucheng Ring Road, Shanghai, China

    Yiting Wu & Pinqi Fang

  2. Department of Ophthalmology, Shanghai Sixth People’s Hospital, 600 Yishan Road, Shanghai, China

    Xiangning Wang

  3. Medical Records and Statistic Office, Shanghai Sixth People’s Hospital, 600 Yishan Road, Shanghai, China

    Jie Shen

Authors
  1. Yiting Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Pinqi Fang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiangning Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jie Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Y. W. and P. F. contributed equally to this work. Corresponding author: J. S. (Email: slyysj2009@163.com). All authors reviewed the manuscript.

Corresponding author

Correspondence to Jie Shen.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Fang, P., Wang, X. et al. Predicting pancreatic diseases from fundus images using deep learning. Vis Comput 41, 3553–3564 (2025). https://doi.org/10.1007/s00371-024-03619-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-024-03619-5

Keywords