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Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot by Seung Min Ryu; Keewon Shin; Soo Wung Shin; Sun Ho Lee; Su Min Seo; Seung-uk Cheon; Seung-Ah Ryu; Jun-Sik Kim; Sunghwan Ji; Namkug Kim is a Medicine article available to read on EtoBox.
What is Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot about?
Landmark detection in flatfoot radiographs is crucial in analyzing foot deformity. Here, we evaluated the accuracy and efficiency of the automated identification of flatfoot landmarks using a newly developed cascade convolutional neural network (CNN) algorithm, Flatfoot Landmarks AnnoTating Network (FlatNet). A total of 1200 consecutive weight-bearing lateral radiographs of the foot were acquired. The first 1050 radiographs were used as the training and tuning, and the following 150 radiographs were used as the test sets, respectively. An expert orthopedic surgeon (A) manually labeled ground truths for twenty-five anatomical landmarks. Two orthopedic surgeons (A and B, each with eight years of clinical experience) and a general physician (GP) independently identified the landmarks of the test sets using the same method. After two weeks, observers B and GP independently identified the landmarks once again using the developed deep learning CNN model (DLm). The Xand Y-coordinates and the mean absolute distance were evaluated. The average differences (mm) from the ground truth were 0.60 ± 0.57, 1.37 ± 1.28, and 1.05 ± 1.23 for the X-coordinate, and 0.46 ± 0.59, 0.97 ± 0.98, and 0.73 ±
Who reads Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Seung Min Ryu; Keewon Shin; Soo Wung Shin; Sun Ho Lee; Su Min Seo; Seung-uk Cheon; Seung-Ah Ryu; Jun-Sik Kim; Sunghwan Ji; Namkug Kim
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Medicine (Physical Sciences)