The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy
Open accessAbstract
The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity. We developed the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitivity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sensitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80 ± 5.60 ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists. A deep-learning algorithm can detect polyps in the colon in real time and with high sensitivity and specificity, according to validation studies with prospectively collected images and videos from colonoscopies performed in 1,138 patients.
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Cites
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Adenoma detection rate and risk of colorectal cancer and death
Corley, D. A., Jensen, C. D., Marks, A. R., Zhao, W. K., Lee, J. K., Doubeni, C. A., Zauber, A. G., de Boer, J., Fireman, B. H., Schottinger, J. E., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1309086 · Cited by 2,270
Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations
Chen, J. H., & Asch, S. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1702071 · Cited by 1,171
Mori, Y., Kudo, S.-E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Ohtsuka, K., Urushibara, F., Kataoka, S., Ogawa, Y., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0249 · Cited by 509
Fernández-Esparrach, G., Bernal, J., López-Cerón, M., Córdova, H., Sánchez-Montes, C., Rodríguez de Miguel, C., & Sánchez, F. (2016) · Endoscopy · doi:10.1055/s-0042-108434 · Cited by 146
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High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136
Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913
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Self-citationWang, P., Liu, X., Berzin, T. M., Glissen Brown, J. R., Liu, P., Zhou, C., Lei, L., Li, L., Guo, Z., Lei, S., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30411-x · Cited by 415
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Metadata
- Authors
- Wang, P., Xiao, X., Glissen Brown, J. R., Berzin, T. M., Tu, M., Xiong, F., Hu, X., Liu, P., Song, Y., Zhang, D., Yang, X., Li, L., He, J., Yi, X., Liu, J., & Liu, X.
- Venue
- Nature Biomedical Engineering
- Published
- 2018-10-10
- Volume
- 2
- Issue
- 10
- Pages
- 741-748
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W2894010682
- Cited by
- 483
- References
- 6
- FWCI
- 18.79
- Type
- journal article
- Language
- en
- Open access
- green
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- Wang, P., Xiao, X., Glissen Brown, J. R., Berzin, T. M., Tu, M., Xiong, F., Hu, X., Liu, P., Song, Y., Zhang, D., Yang, X., Li, L., He, J., Yi, X., Liu, J., & Liu, X. (2018). Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-018-0301-3
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