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Scientific Reports logoLink to Scientific Reports
. 2021 Aug 2;11:15996. doi: 10.1038/s41598-021-95469-7

Author Correction: Automated rotator cuff tear classification using 3D convolutional neural network

Eungjune Shim 1, Joon Yub Kim 2, Jong Pil Yoon 3, Se‑Young Ki 4, Taewoo Lho 4, Youngjun Kim 1,✉,#, Seok Won Chung 4,✉,#
PMCID: PMC8329254  PMID: 34341475

Correction to: Scientific Reports https://doi.org/10.1038/s41598-020-72357-0, published online 24 September 2020

The original version of this Article contained errors in the Abstract.

“The VRN-based 3D CNN outperformed orthopedists specialized in shoulder and general orthopedists in binary accuracy (92.5% vs. 76.4% and 68.2%), top-1 accuracy (69.0% vs. 45.8% and 30.5%), top-1±1 accuracy (87.5% vs. 79.8% and 71.0%), sensitivity (0.94 vs. 0.86 and 0.90), and specificity (0.90 vs. 0.58 and 0.29).”

now reads:

“The VRN-based 3D CNN outperformed orthopedists specialized in shoulder and general orthopedists in binary accuracy (92.5% vs. 76.4% and 68.2%), top-1 accuracy (69.0% vs. 45.8% and 30.5%), top-1±1 accuracy (87.5% vs. 79.8% and 71.0%), sensitivity (0.92 vs. 0.89 and 0.93), and specificity (0.86 vs. 0.61 and 0.26).”

The original Article has been corrected.

Contributor Information

Youngjun Kim, Email: junekim@kist.re.kr.

Seok Won Chung, Email: smilecsw@gmail.com.


Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

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