Summary of Toward Non-invasive Diagnosis Of Bankart Lesions with Deep Learning, by Sahil Sethi et al.
Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning
by Sahil Sethi, Sai Reddy, Mansi Sakarvadia, Jordan Serotte, Darlington Nwaudo, Nicholas Maassen, Lewis Shi
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRI arthrograms (MRAs). The goal is to improve diagnostic accuracy and reduce the need for invasive imaging. A dataset of 586 shoulder MRIs from 558 patients was curated, with ground truth labels derived from intraoperative findings. Separate DL models were trained using the Swin Transformer architecture, pre-trained on a public knee MRI dataset. The models were evaluated on a hold-out test set and achieved high accuracy, sensitivity, and specificity rates. The results match or surpass radiologist performance on both standard MRIs and MRAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computers to help doctors diagnose shoulder problems by looking at special pictures of the shoulder called MRIs. They wanted to find ways to do this without needing more complicated and invasive tests. To do this, they looked at a big group of MRI pictures and used that data to train computer models to spot certain types of problems. The models were really good at spotting these problems on both simple and more complicated pictures. This could help doctors make better diagnoses and give patients better care. |
Keywords
» Artificial intelligence » Deep learning » Transformer
