Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs

Author:

Noh Sung Hyun12,Lee Gaeun3ORCID,Bae Hyun-Jin3ORCID,Han Ju Yeon1,Son Su Jeong1,Kim Deok1,Park Jeong Yeon1,Choi Seung Kyeong1,Cho Pyung Goo1ORCID,Kim Sang Hyun1,Yuh Woon Tak4ORCID,Lee Su Hun5,Park Bumsoo6,Kim Kwang-Ryeol7,Kim Kyoung-Tae8ORCID,Ha Yoon9

Affiliation:

1. Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea

2. Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea

3. Promedius Inc., Seoul 05609, Republic of Korea

4. Department of Neurosurgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Republic of Korea

5. Department of Neurosurgery, Pusan National University Yangsan Hospital, Busan 50612, Republic of Korea

6. Department of Neurosurgery, Bon Hospital, Daejeon 34188, Republic of Korea

7. Department of Neurosurgery, Daegu Catholic University College of Medicine, Daegu 42400, Republic of Korea

8. Department. of Neurosurgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu 41944, Republic of Korea

9. Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea

Abstract

This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program’s performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20–85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5–2.4 mm), followed by lumbosacral landmarks (median error 2.1–3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4–4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements.

Publisher

MDPI AG

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