Chris Haarburger

Christoph Haarburger
Feb 23, 2022

Beyond the Eye - Decoding the Ocular Fundus

Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., de Oliveira Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A. V., Hammel, N., Liu, Y., & Bavishi, P. (2021). Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet. Digital Health, 3(1), e10–e19.

Chang, J., Ko, A., Park, S. M., Choi, S., Kim, K., Kim, S. M., Yun, J. M., Kang, U., Shin, I. H., Shin, J. Y., Ko, T., Lee, J., Oh, B.-L., & Park, K. H. (2020). Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images. American Journal of Ophthalmology, 217, 121–130.

Cheung, C. Y., Xu, D., Cheng, C.-Y., Sabanayagam, C., Tham, Y.-C., Yu, M., Rim, T. H., Chai, C. Y., Gopinath, B., Mitchell, P., Poulton, R., Moffitt, T. E., Caspi, A., Yam, J. C., Tham, C. C., Jonas, J. B., Wang, Y. X., Song, S. J., Burrell, L. M., … Wong, T. Y. (2021). A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nature Biomedical Engineering, 5(6), 498–508.

Dong, L., Ju, L., Hui, S., Luo, L., Nie, Z., Zhang, R., Jiang, X., Zhou, W., Li, H. Y., Ding, J., Zhang, J., Hou, Z., Li, Y., Jonas, J. B., Wang, X., Zhao, X., He, C., Chen, Y., Wang, Z., … Li, D.-M. (2021). Retinal Photograph-Based Deep Learning System for Detection of Hyperthyroidism: A Multicenter, Diagnostic Study.

Li, T., Bo, W., Hu, C., Kang, H., Liu, H., Wang, K., & Fu, H. (2021). Applications of Deep Learning in Fundus Images: A Review. In arXiv [eess.IV]. arXiv.

Lim, G., Lim, Z. W., Xu, D., Ting, D. S. W., Wong, T. Y., Lee, M. L., & Hsu, W. (2019). Feature Isolation for Hypothesis Testing in Retinal Imaging: An Ischemic Stroke Prediction Case Study. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9510–9515.

Liu, C., Wang, W., Li, Z., Jiang, Y., Han, X., Ha, J., Meng, W., & He, M. (2019). Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 138–146.

Mitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y., & Varadarajan, A. V. (2020). Detection of anaemia from retinal fundus images via deep learning. Nature Biomedical Engineering, 4(1), 18–27.

Mueller, S., Wintergerst, M. W. M., Falahat, P., Holz, F. G., Schaefer, C., Schahab, N., Finger, R. P., & Schultz, T. (2022). Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography. Scientific Reports, 12(1), 1389.

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158–164.

Rim, T. H., Lee, C. J., Tham, Y.-C., Cheung, N., Yu, M., Lee, G., Kim, Y., Ting, D. S. W., Chong, C. C. Y., Choi, Y. S., Yoo, T. K., Ryu, I. H., Baik, S. J., Kim, Y. A., Kim, S. K., Lee, S.-H., Lee, B. K., Kang, S.-M., Wong, E. Y. M., … Wong, T. Y. (2021). Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. The Lancet. Digital Health, 3(5), e306–e316.

Rim, T. H., Lee, G., Kim, Y., Tham, Y.-C., Lee, C. J., Baik, S. J., Kim, Y. A., Yu, M., Deshmukh, M., Lee, B. K., Park, S., Kim, H. C., Sabayanagam, C., Ting, D. S. W., Wang, Y. X., Jonas, J. B., Kim, S. S., Wong, T. Y., & Cheng, C.-Y. (2020). Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. The Lancet. Digital Health, 2(10), e526–e536.

Schmidt-Erfurth, U., Bogunovic, H., Sadeghipour, A., Schlegl, T., Langs, G., Gerendas, B. S., Osborne, A., & Waldstein, S. M. (2018). Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration. Ophthalmology. Retina, 2(1), 24–30.

Tham, Y.-C., Anees, A., Zhang, L., Goh, J. H. L., Rim, T. H., Nusinovici, S., Hamzah, H., Chee, M.-L., Tjio, G., Li, S., Xu, X., Goh, R., Tang, F., Cheung, C. Y.-L., Wang, Y. X., Nangia, V., Jonas, J. B., Gopinath, B., Mitchell, P., … Cheng, C.-Y. (2021). Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study. The Lancet. Digital Health, 3(1), e29–e40.

Tham, Y.-C., Cheng, C. Y., & Wong, T. Y. (2020). Detection of anaemia from retinal images [Review of Detection of anaemia from retinal images]. Nature Biomedical Engineering, 4(1), 2–3.

Tian, J., Smith, G., Guo, H., Liu, B., Pan, Z., Wang, Z., Xiong, S., & Fang, R. (2021). Modular machine learning for Alzheimer’s disease classification from retinal vasculature. Scientific Reports, 11(1), 238.

Tseng, R. M. W. W., Rim, T. H., Cheung, C. Y., & Wong, T. Y. (2021). Artificial Intelligence Using the Eye as a Biomarker of Systemic Risk. In A. Grzybowski (Ed.), Artificial Intelligence in Ophthalmology (pp. 243–255). Springer International Publishing.

Wang, Z., Keane, P. A., Chiang, M., Cheung, C. Y., Wong, T. Y., & Ting, D. S. W. (2020). Artificial Intelligence and Deep Learning in Ophthalmology. In N. Lidströmer & H. Ashrafian (Eds.), Artificial Intelligence in Medicine (pp. 1–34). Springer International Publishing.

Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X., Qin, X., Su, Y., Xu, W., Olvera, A., Xue, K., Li, Z., Zhang, M., Zeng, X., Zhang, C. L., … Wang, G. (2021). Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nature Biomedical Engineering, 5(6), 533–545.

Back to main post