Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs

Objectives Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)–based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection. Methods We us...

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Bibliographic Details
Published in:European radiology 2023-07, Vol.33 (7), p.4822-4832
Main Authors: Ryu, Seung Min, Shin, Keewon, Shin, Soo Wung, Lee, Sun Ho, Seo, Su Min, Cheon, Seung-Uk, Ryu, Seung-Ah, Kim, Min-Ju, Kim, Hyunjung, Doh, Chang Hyun, Choi, Young Rak, Kim, Namkug
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Datasets
Deep learning
Diagnosis
Diagnostic Radiology
Errors
Imaging
Internal Medicine
Interventional Radiology
Machine learning
Medical diagnosis
Medicine
Medicine & Public Health
Musculoskeletal
Neural networks
Neuroradiology
Observers
Orthopedics
Radiographs
Radiography
Radiology
Reliability
Surgeons
Time measurement
Ultrasound
Weight
Young adults
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Summary:Objectives Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)–based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection. Methods We used 1200 weight-bearing lateral foot radiographs from young adult Korean males for the model development. An experienced orthopedic surgeon identified 22 radiographic landmarks and measured three angles for flatfoot diagnosis that served as the ground truth (GT). Another orthopedic surgeon (OS) and a general physician (GP) independently identified the landmarks of the test dataset and measured the angles using the same method. External validation was performed using 100 and 17 radiographs acquired from a tertiary referral center and a public database, respectively. Results The DLM showed smaller absolute average errors from the GT for the three angle measurements for flatfoot diagnosis compared with both human observers. Under the guidance of the DLM, the average errors of observers OS and GP decreased from 2.35° ± 3.01° to 1.55° ± 2.09° and from 1.99° ± 2.76° to 1.56° ± 2.19°, respectively (both p  
ISSN:1432-1084
0938-7994
1432-1084
DOI:10.1007/s00330-023-09442-1