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Raumviboonsuk P, Krause J, Chotcomwongse P, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019;2:25doi: 10.1038/s41746-019-0099-8.
Raumviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., Silpa-Archa, S., Limwattanayingyong, J., Rao, C., Kuruvilla, O., Jung, J., Tan, J., Orprayoon, S., Kangwanwongpaisan, C., Sukumalpaiboon, R., Luengchaichawang, C., Fuangkaew, J., Kongsap, P., Chualinpha, L., Saree, S., Kawinpanitan, S., Mitvongsa, K., Lawanasakol, S., Thepchatri, C., Wongpichedchai, L., Corrado, G. S., Peng, L., & Webster, D. R. (2019). Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ digital medicine, 225. https://doi.org/10.1038/s41746-019-0099-8
Raumviboonsuk, Paisan, et al. "Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program." NPJ digital medicine vol. 2 (2019): 25. doi: https://doi.org/10.1038/s41746-019-0099-8
Raumviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, Campana BJL, Phene S, Hemarat K, Tadarati M, Silpa-Archa S, Limwattanayingyong J, Rao C, Kuruvilla O, Jung J, Tan J, Orprayoon S, Kangwanwongpaisan C, Sukumalpaiboon R, Luengchaichawang C, Fuangkaew J, Kongsap P, Chualinpha L, Saree S, Kawinpanitan S, Mitvongsa K, Lawanasakol S, Thepchatri C, Wongpichedchai L, Corrado GS, Peng L, Webster DR. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019 Apr 10;2:25. doi: 10.1038/s41746-019-0099-8. eCollection 2019. PMID: 31304372; PMCID: PMC6550283.
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NPJ Digit Med. 2019 Apr 10;2:25. doi: 10.1038/s41746-019-0099-8. eCollection 2019.

Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program.

NPJ digital medicine

Paisan Raumviboonsuk, Jonathan Krause, Peranut Chotcomwongse, Rory Sayres, Rajiv Raman, Kasumi Widner, Bilson J L Campana, Sonia Phene, Kornwipa Hemarat, Mongkol Tadarati, Sukhum Silpa-Archa, Jirawut Limwattanayingyong, Chetan Rao, Oscar Kuruvilla, Jesse Jung, Jeffrey Tan, Surapong Orprayoon, Chawawat Kangwanwongpaisan, Ramase Sukumalpaiboon, Chainarong Luengchaichawang, Jitumporn Fuangkaew, Pipat Kongsap, Lamyong Chualinpha, Sarawuth Saree, Srirut Kawinpanitan, Korntip Mitvongsa, Siriporn Lawanasakol, Chaiyasit Thepchatri, Lalita Wongpichedchai, Greg S Corrado, Lily Peng, Dale R Webster

Affiliations

  1. 1Department of Ophthalmology, Rajavithi Hospital, Bangkok, Thailand.
  2. 2Google AI, Google, Mountain View, CA USA.
  3. 3Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu India.
  4. 4Department of Ophthalmology, Vajira Hospital, Bangkok, Thailand.
  5. Eye and Laser Center, Charlotte, NC USA.
  6. 6East Bay Retina Consultants, Oakland, CA; Department of Ophthalmology, University of California San Francisco, San Francisco, CA USA.
  7. 7Retina-Vitreous Associates Medical Group, Department of Ophthalmology, University of Southern California, Los Angeles, CA USA.
  8. Department of Ophthalmology, Lamphun Hospital, Lamphun, Thailand.
  9. Department of Ophthalmology, Somdejphrajaotaksin Maharaj Hospital, Tak, Thailand.
  10. Department of Ophthalmology, Sawanpracharak Hospital, Nakhon Sawan, Thailand.
  11. Department of Ophthalmology, Nakhon Nayok Hospital, Nakhon Nayok, Thailand.
  12. Department of Ophthalmology, Photharam Hospital, Ratchaburi, Thailand.
  13. 13Department of Ophthalmology, Prapokklao Hospital, Chanthaburi, Thailand.
  14. Department of Ophthalmology, Mahasarakham Hospital, Mahasarakham, Thailand.
  15. Department of Ophthalmology, Nongbualamphu Hospital, Nongbualamphu, Thailand.
  16. Department of Ophthalmology, Pakchongnana Hospital, Nakhon Ratchasima, Thailand.
  17. Department of Ophthalmology, Mukdahan Hospital, Mukdahan, Thailand.
  18. Department of Ophthalmology, Suratthani Hospital, Suratthani, Thailand.
  19. Department of Ophthalmology, Sungaikolok Hospital, Narathiwat, Thailand.
  20. 20Bangkok Metropolitan Administration Public Health Center 7, Bangkok, Thailand.

PMID: 31304372 PMCID: PMC6550283 DOI: 10.1038/s41746-019-0099-8

Abstract

Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with that of human graders. A total of 25,326 gradable retinal images of patients with diabetes from the community-based, nationwide screening program of DR in Thailand were analyzed for DR severity and referable diabetic macular edema (DME). Grades adjudicated by a panel of international retinal specialists served as the reference standard. Relative to human graders, for detecting referable DR (moderate NPDR or worse), the deep learning algorithm had significantly higher sensitivity (0.97 vs. 0.74,

Keywords: Developing world; Diabetes complications

Conflict of interest statement

Competing interestsJ.K., R.S., K.W., B.J.L.C., G.S.C., L.P., S.P. and D.R.W. are Google employees and receive salary and stock as a part of the standard compensation package. O.K., J.J. and J.T. are c

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