Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computational medicine: A cybernetic eye for rare disease

Nature Biomedical Engineering volume 1, Article number: 0032 (2017) Cite this article

Subjects

Deep learning implemented in a collaborative cloud-based platform empowers ophthalmologists in the diagnosis of congenital cataracts.

Rare diseases are often neglected, yet they are a public health concern1. Congenital cataracts (CCs) — a typical rare disease characterized by the clouding of the lens of the eye — can lead to blindness in children, especially in developing countries2. The management of CCs requires accurate diagnosis, timely surgery and rigorous follow-ups. However, it is challenging to integrate the typically scattered and limited medical resources for tackling rare disease that exist in many developing countries. Also, the quality of the healthcare services provided to CC patients, including diagnoses and treatment decisions, often varies across individual centres. Writing in Nature Biomedical Engineering, Yizhi Liu and colleagues report that artificial-intelligence (AI) software can provide diagnostic and treatment suggestions for CC patients, with accuracy comparable to that of individual expert ophthalmologists. They also describe a cloud-based web platform for the collaborative management of CC patients across care centres3.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: A multihospital platform for the collaborative management of congenital cataracts and other rare diseases through artificial intelligence.

References

  1. Schieppati, A., Henter, J. I., Daina, E. & Aperia, A. Lancet 371, 2039–2041 (2008).

    Article  Google Scholar 

  2. Steinkuller, P. G. et al. J. Am. Assoc. Pediatr. Ophthalmol. Strab. 3, 26–32 (1999).

    Article  CAS  Google Scholar 

  3. Long, E. et al. Nat. Biomed. Eng. 1, 0024 (2017).

    Article  Google Scholar 

  4. LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).

    Article  CAS  Google Scholar 

  5. Andreu-Perez, J., Poon, C. C., Merrifield, R. D., Wong, S. T. & Yang, G. Z. IEEE J. Biomed. Health Inform. 19, 1193–1208 (2015).

    Article  Google Scholar 

  6. Krizhevsky, A., Sutskever, I. & Hinton, G. Proc. Adv. Neural Inf. Process. Syst. 25, 1090–1098 (2012).

    Google Scholar 

  7. Gulshan, V. et al. JAMA 316, 2402–2410 (2016).

    Article  Google Scholar 

  8. Zhou, S. K., Greenspan, H. & Shen, D. (eds) Deep Learning for Medical Image Analysis (Academic, 2017).

    Google Scholar 

  9. Shen, D., Wu, G., & Suk, H. I. Annu. Rev. Biomed. Eng. 19, http://www.annualreviews.org/doi/abs/10.1146/annurev-bioeng-071516-044442 (2017).

  10. Bahrami, K., Shi, F., Rekik, I. & Shen, D. in Deep Learning and Data Labeling for Medical Applications (eds Carneiro, G. et al.) 39–47 (Lecture Notes in Computer Science Vol. 10008, Springer, 2016).

    Google Scholar 

  11. Wu, G., Kim, M., Wang, Q., Munsell, B. C. & Shen, D. IEEE Trans. Biomed. Eng. 63, 1505–1516 (2016).

    Article  Google Scholar 

  12. Zhang, W. et al. NeuroImage 108, 214–224 (2015).

    Article  Google Scholar 

  13. Guo, Y., Gao, Y. & Shen, D. IEEE Trans. Med. Imag. 35, 1077–1089 (2016).

    Article  Google Scholar 

  14. Suk, H. I., Lee, S. W. & Shen, D. NeuroImage 101, 569–582 (2014).

    Article  Google Scholar 

  15. Cheng, J. et al. Sci. Rep. 6, 1–13 (2016).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Qian Wang is at the Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.,

    Qian Wang

  2. Dinggang Shen is in the Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.,

    Dinggang Shen

Authors
  1. Qian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dinggang Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dinggang Shen.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Shen, D. Computational medicine: A cybernetic eye for rare disease. Nat Biomed Eng 1, 0032 (2017). https://doi.org/10.1038/s41551-017-0032

Download citation

  • Published:

  • DOI: https://doi.org/10.1038/s41551-017-0032

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing