A systematic review for using deep learning in bone scan classification

被引:4
|
作者
Kao, Yung-Shuo [1 ,5 ]
Huang, Chun-Pang [2 ]
Tsai, Wen-Wen [3 ]
Yang, Jen [4 ]
机构
[1] China Med Univ Hosp, Dept Radiat Oncol, Taichung, Taiwan
[2] Chang Gung Univ, Kaohsiung Chang Gung Mem Hosp, Dept Nucl Med, Coll Med, Kaohsiung, Taiwan
[3] Chi Mei Med Ctr, Dept Educ, Tainan, Taiwan
[4] Natl Cheng Kung Univ, Natl Cheng Kung Univ Hosp, Coll Med, Dept Med Imaging, Tainan, Taiwan
[5] Dr Kao Clin, Taichung, Taiwan
关键词
Deep learning; Classification; Bone scan; Systematic review; Bone metastasis; ASSISTED DIAGNOSIS SYSTEM; CANCER-PATIENTS; SCINTIGRAPHY; METASTASIS; PERFORMANCE; VERSION;
D O I
10.1007/s40336-023-00539-7
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
IntroductionBone scintigraphy, a nuclear medicine technique, is widely used for the detection of bone metastasis. Deep learning has also been used in bone scan classification. Thus, we performed this systematic review to draw the most up-to-date conclusions on this topic.Materials/methodsDatabases (PubMed, Cochrane, Embase, and IEEE Xplore) were searched for articles on neural-network-based bone scan classification (determination of whether a patient has bone metastases) from inception to April 4, 2022. The study quality was evaluated using QUADAS-2.ResultsWe collected a total of 616 articles. After article review, 24 articles were selected for inclusion in the final systematic review. 14 studies adopted convolutional neural networks (CNN) methods to extract initial image features. Other ten studies used either bone scan index (BSI) or region-specific thresholding methods. Most of the included studies exhibited high quality.ConclusionDeep-learning-based models have shown strong potential for incorporation into the clinical scenario of diagnosis assistance.
引用
收藏
页码:271 / 283
页数:13
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