Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint.

Journal: SLAS technology

PMID: 38796035

Abstract

OBJECTIVE: This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral density (BMD) measurements and clinical information based on machine learning (ML) models.

Authors

Yu Zhao

College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
Jingjing Qiu

Neonatology Department, Shijiazhuang Fourth Hospital, Shijiazhuang, 050000 Hebei, China.
Yang Li

Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
Muhammad Attique Khan

Department of Computer Science, HITEC University, Taxila, Pakistan.
Lei Wan

Institute of Forensic Science, Ministry of Justice, China, Shanghai Key Laboratory of Forensic Medicine, NO1347, West Guangfu Road, Putuo District, Shanghai, China.
Lihua Chen

Department of Radiology, Southwest Hospital, Chongqing, China.

Keywords

Aged Aged, 80 and over Bone Density Female Humans Machine Learning Male Middle Aged Osteoporosis Radiography Retrospective Studies Rotator Cuff Injuries Shoulder Joint

External Resources

View on PubMed Access via DOI PubMed (38796035)

Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint.

Abstract

Authors

Keywords

External Resources

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