Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images

Abstract

Automatic type identification of sub-axial spine fractures is of prime importance for orthopaedicians to reduce image interpretation time and increase patient care time. But identifying fracture types is challenging due to imbalanced datasets. In this work, CT scan images of fractured spine has been collected from a Tertiary Care hospital and extended Deep Convolutional Generative Adversarial Network (DCGAN) architecture is developed for generating spine fracture images that overcomes the imbalanced dataset problem. These enhanced dataset are clinically evaluated with Two Visual Turing Tests (VTTs): the first test to “identify real and generated images” and second test to determine “type of fractures in the generated images.” The first VTT demonstrates that generated images of fractures are realistic and that even spine surgeons have difficulty in distinguishing them from real. The second VTT demonstrates that fracture lines are clearly visible in the generated images. The VTT results are analyzed using Fleiss Kappa statistical techniques to determine the inter-observer reliability of spine surgeons’ clinical evaluation. The results showed high interobserver agreement for “type identification” in the generated images. The clinically evaluated generated images are provided to the proposed ensemble based type identification model, which outperformed other models in type identification.

Keywords:

• Classification
• clinical evaluation
• computed tomography (CT) images
• Deep Convolutional Generative Adversarial Network (DCGAN)
• deep learning
• spine fracture
• Visual Turing Tests (VTT)

Reviewing Editor:

• Jenhui Chen, Chang Gung University, Kweishan, Taiwan

Subjects:

• Artificial Intelligence
• Computer Engineering
• Computer Science (General)

Acknowledgments

Our sincere thanks to the Hospital for providing the data. Also, thanks to Spine surgeons for participating in the Visual Turing Test.

Ethical approval

This study was approved by the Institutional Ethics Committee. The authors declare that all procedures performed in this study abide by the ethical standards of the institutional research committee.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not for- profit sectors.

Notes on contributors

Sindhura D. N.

Sindhura D N received the B.E. degree from the Jawaharlal Nehru National College of Engineering, VTU, Belgaum, and the Master’s degree in Computer Science and Engineering from Jawaharlal Nehru National College of Engineering, VTU, Belgaum, India. She is currently pursuing the Ph.D. degree in computer vision and deep learning at Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. Her research interests include image processing and computer vision in healthcare.

Radhika M. Pai

Dr. Radhika M. Pai (Senior Member, IEEE) received the Ph.D. degree from the National Institute of Technology Karnataka, Surathkal, India. She is currently a Professor and the Head of the Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. She has a teaching and research experience of over 31 years. She has published 95 papers in national/international journals/conferences and has guided three Ph.D.’s and several master’s thesis. She has 2 granted patents to her credit. Her research interests include data mining, big data analytics, character recognition, sensor networks, and e-learning. She was the Principal Investigator for a research grant project and has executed other projects as a co-investigator. She was a recipient of National Doctoral Fellowship from AICTE, Government of India.

Shyamasunder N. Bhat

Dr. Shyamasunder Bhat N is Professor and Head of Orthopaedics at Manipal Academy of Higher Education, Manipal, INDIA. Since 1999, he has been a faculty in the Department of Orthopaedics at Kasturba Medical College, Manipal affiliated to Manipal Academy of Higher Education. After his basic medical degree from University of Mysore (1995), he continued his postgraduate training and obtained masters in Orthopaedics at Kasturba Medical College, Manipal (1999). He received Diplomate of National Board in 1999. He is the recipient of AOTrauma Fellowship (Singapore), AOSpine Fellowship (Hong Kong), Fellowship in Degenerative Spine Surgery (Japan), Visiting Fellowship in Spinal Deformity (Canada), AOTrauma Visit the Expert Fellowship (Germany) and Visiting Fellowship in Endoscopic Spine Surgery (Japan). He has published several articles (90) in National and International Indexed journals. He authored and co-authored chapters in 3 books. He also has many National and International conference presentations to his credit. His interests are Spine surgery Trauma, Imaging in Spine surgery and Interprofessional Education and Practice.

Manohara Pai M. M.

Dr. Manohara Pai M.M (Senior Member, IEEE) received the Ph.D. degree in Computer Science and Engineering. He is currently a Senior Professor with the Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. He has a rich experience of 31 years in Teaching/Research. He holds nine patents to his credit and has published 145 papers in national and international journals/conference proceedings. He has published two books, guided six Ph.D.’s, and 85 master’s thesis. His research interests include data analytics, cloud computing, the IoT, computer networks, mobile computing, scalable video coding, and robot motion planning. He is also a Life Member of ISTE and a Life Member of Systems Society of India. He is also a Principal Investigator for multiple industry/government research projects. He has been an Executive Committee Member of the IEEE Bangalore Section, Mangalore Subsection, and the past Chair of the IEEE Mangalore Subsection. He has received the National Technical Teachers’ Award (NTTA 2022) from Ministry of Education, Government of India.