Enhancing prediction of supraspinatus/infraspinatus tendon complex injuries through integration of deep visual features and clinical information: a multicenter two-round assessment study

Enhancing prediction of supraspinatus/infraspinatus tendon complex injuries through integration of deep visual features and clinical information: a multicenter two-round assessment study

Abstract Objective Develop and evaluate an ensemble clinical machine learning–deep learning (CML-DL) model integrating deep visual features and clinical data to improve the prediction of supraspinatus/infraspinatus tendon complex (SITC) injuries. Methods Patients with suspected SITC injuries were re...

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Bibliographic Details
Main Authors: Yamuhanmode Alike, Cheng Li, Jingyi Hou, Yi Long, Jinming Zhang, Chuanhai Zhou, Zongda Zhang, Qi Zhu, Tao Li, Shinan Cao, Yuanhao Zhang, Dan Wang, Shuangqin Cheng, Rui Yang
Format: Article
Language:English
Published: SpringerOpen 2023-11-01
Series:Insights into Imaging
Subjects:
Deep learning
Machine learning
Rotator cuff injury
Two-round assessment
Online Access:https://doi.org/10.1186/s13244-023-01551-1

Internet

https://doi.org/10.1186/s13244-023-01551-1

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