Name
Title | ||
|---|---|---|
Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning. |
Abstract | ||
|---|---|---|
•A deep learning-based algorithm for detecting the region of interest in medical images.•Objective accuracy of developed algorithm was evaluated through 10-fold cross validation of 240 patient data.•Developed algorithm pixel accuracy was 99.86% and dice coefficient was 0.9590 on average.•This algorithm can be used in statistical analysis of clinical surgery and diagnosis area to improve efficiency and patient satisfaction in orthopedic and various medical field. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cmpb.2019.105063 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Medicine,Deep learning,Segmentation,Orthopedics,Rotator cuff tear | Sørensen–Dice coefficient,Convolutional neural network,Computer science,Rotator cuff,Algorithm,Muscle atrophy,Artificial intelligence,Orthopedic surgery,Rotator cuff muscle,Fossa,Deep learning | Journal |
Volume | ISSN | Citations |
182 | 0169-2607 | 1 |
PageRank | References | Authors |
0.35 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joo Young Kim | 1 | 3 | 0.72 |
| Kyunghan Ro | 2 | 1 | 0.35 |
| Sungmin You | 3 | 3 | 0.72 |
| Bo Rum Nam | 4 | 1 | 0.35 |
| Sunhyun Yook | 5 | 2 | 1.72 |
| Hee Seol Park | 6 | 1 | 0.35 |
| Jae Chul Yoo | 7 | 1 | 0.35 |
| Eunkyoung Park | 8 | 1 | 0.35 |
| kyeongwon cho | 9 | 1 | 1.36 |
| Baek Hwan Cho | 10 | 84 | 8.71 |
| In Young Kim | 11 | 250 | 32.24 |