Author Correction: Automated rotator cuff tear classification using 3D convolutional neural network (Scientific Reports, (2020), 10, 1, (15632), 10.1038/s41598-020-72357-0)

Eungjune Shim, Joon Yub Kim, Jong Pil Yoon, Se‑Young ‑Y Ki, Taewoo Lho, Youngjun Kim, Seok Won Chung

Research output: Contribution to journalComment/debate

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number15996
JournalScientific Reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

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