Feasibility study of hallux valgus measurement with a deep convolutional neural network based on landmark detection.

Li, Tong; Wang, Yuzhao; Qu, Yang; Dong, Rongpeng; Kang, Mingyang; Zhao, Jianwu

Li, Tong; Wang, Yuzhao; Qu, Yang; Dong, Rongpeng; Kang, Mingyang; Zhao, Jianwu.

Affiliation

Li T; The Second Hospital of Jilin University, Jilin University, Changchun, 130000, China.
Wang Y; College of Computer Science and Technology, Jilin University, Changchun, 130000, China.
Qu Y; The Second Hospital of Jilin University, Jilin University, Changchun, 130000, China.
Dong R; The Second Hospital of Jilin University, Jilin University, Changchun, 130000, China.
Kang M; The Second Hospital of Jilin University, Jilin University, Changchun, 130000, China.
Zhao J; The Second Hospital of Jilin University, Jilin University, Changchun, 130000, China. jianwu@jlu.edu.cn.

Skeletal Radiol ; 51(6): 1235-1247, 2022 Jun.

Article in En | MEDLINE | ID: mdl-34748073

Subject(s)

Hallux Valgus; Metatarsal Bones; Metatarsophalangeal Joint; Feasibility Studies; Hallux Valgus/diagnostic imaging; Humans; Metatarsophalangeal Joint/diagnostic imaging; Neural Networks, Computer; Radiography

Key words

Anteroposterior (AP) radiography; Automatic measurement; Deep learning; Hallux valgus

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Metatarsal Bones / Hallux Valgus / Metatarsophalangeal Joint Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Skeletal Radiol Year: 2022 Document type: Article Affiliation country: China Country of publication: Germany

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