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深度学习在肩袖撕裂MRI诊断中的研究进展

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MRI是评估肩袖撕裂的主要检查方法,它不仅可以直观显示肩袖撕裂区域(如撕裂的大小和位置、脂肪浸润程度等),还可以定性及定量分析每一个肌腱和肌肉.深度学习(DL)应用于肩袖撕裂的MRI诊断是人工智能研究的一个新兴领域,在骨肌放射学方面具备广阔的应用前景,包括提高工作效率及诊断准确率等.笔者总结了近十年DL在肩袖撕裂MRI诊断中的研究进展,旨在为肩袖撕裂的诊断、治疗、预后评估及随访提供新的思路.
Research progress of MRI in diagnosing rotator cuff tears based on deep learning
MRI is the primary examination tool for assessing rotator cuff tears,which could not only intuitively display the rotator cuff tear area(such as the size,location,and degree of fat infiltration of the tear),but it can also qualitatively and quantitatively analyze each tendon and muscle.The application of deep learning(DL)in MRI diagnosis of rotator cuff tears is a new field of artificial intelligence research,which provides a wide application prospects for musculoskeletal radiology,such as improving work efficiency and diagnostic accuracy.The authors summarized the research progress of DL in MRI diagnosis of rotator cuff tears in the last ten years to provide new ideas for the diagnosis,treatment,prognosis evaluation,and follow-up of rotator cuff tears.

Rotator cuff tearMagnetic resonance imagingDeep learningArtificial intelligence

焦琰洁、粘琦玉、崔立春

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青岛市胶州中心医院医学影像科,青岛 266300

肩袖撕裂 磁共振成像 深度学习 人工智能

2024

国际放射医学核医学杂志
中华医学会 中国医学科学院放射医学研究所

国际放射医学核医学杂志

CSTPCD
影响因子:0.227
ISSN:1673-4114
年,卷(期):2024.48(10)