Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Journal: Journal of magnetic resonance imaging : JMRI

PMID: 39501646

Abstract

BACKGROUND: Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explored.

Authors

Kaifeng Wang

Fujian Medical University, Fuzhou, Fujian, China.
Fabin Lin

Fujian Medical University, Fuzhou, Fujian, China.
Zulin Liao

Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Yongjiang Wang

Ordos Central Hospital, Ordos, Inner Mongolia, China.
Tingxin Zhang

Ordos Central Hospital, Ordos, Inner Mongolia, China.
Rui Wang

Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.

Keywords

Adult Aged Deep Learning Diskectomy Female Humans Intervertebral Disc Displacement Lumbar Vertebrae Magnetic Resonance Imaging Male Microsurgery Middle Aged Postoperative Period Reproducibility of Results Retrospective Studies Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (39501646)

Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Abstract

Authors

Keywords

External Resources

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