Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm

Back Kim; Do Weon Lee; Sanggyu Lee; Sunho Ko; Changwung Jo; Jaeseok Park; Byung Sun Choi; Aaron John Krych; Ayoosh Pareek; Hyuk-Soo Han; Du Hyun Ro

doi:10.3390/medicina58111677

journal article Open Access Nov 19, 2022

Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm

Back Kim

Do Weon Lee Sanggyu Lee Sunho Ko Changwung Jo

Jaeseok Park Byung Sun Choi

Medicina Vol. 58 No. 11 pp. 1677 · MDPI AG

View at Publisher Save 10.3390/medicina58111677

Abstract

Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

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Details

Published: Nov 19, 2022
Vol/Issue: 58(11)
Pages: 1677
License: View

Authors

B

Back Kim

College of Medicine, Seoul National University, Seoul 03080, Republic of Korea

D

Do Weon Lee

Department of Orthopedic Surgery, Korean Armed Forces Yangju Hospital, Yangju-si 11429, Republic of Korea

S

Sanggyu Lee

Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea

S

Sunho Ko

Department of Orthopedic Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea

C

Changwung Jo

Seoul National University Hospital, Seoul 03080, Republic of Korea

J

Jaeseok Park

College of Medicine, Seoul National University, Seoul 03080, Republic of Korea

B

Byung Sun Choi

Department of Orthopedic Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea

A

Aaron John Krych

Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55902, USA

A

Ayoosh Pareek

Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55902, USA

H

Hyuk-Soo Han

College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Department of Orthopedic Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea

D

Du Hyun Ro

College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Department of Orthopedic Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea; CONNECTEVE Co., Ltd., Seoul 03080, Republic of Korea

Cite This Article

Back Kim, Do Weon Lee, Sanggyu Lee, et al. (2022). Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm. Medicina, 58(11), 1677. https://doi.org/10.3390/medicina58111677

Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm

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