Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study

Aakriti Mishra; A. Ramanathan; Vineet Batta; C. Malathy; Soumya Snigdha Kundu; M. Gayathri; D. Vathana; Srinath Kamineni

doi:10.1007/978-3-031-48593-0_9

Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study

Aakriti Mishra
, A. Ramanathan
, Vineet Batta
, C. Malathy
, Soumya Snigdha Kundu
, M. Gayathri
, D. Vathana
, Srinath Kamineni

College of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Orthopedic implant identification is an important and necessary step prior to performing revision surgery of different joints. The inability to identify an implant can lead to significant surgical difficulties with consequent unfavorable outcomes. This paper proposes a novel framework to identify the make and model of seven (7) different total shoulder arthroplasty implants utilizing plain X-ray images and Artificial intelligence. The proposed work classified implants with an accuracy of 91.48% and with an AUC (Area under curve) of 0.9932 showing higher effectiveness in orthopedic implant identification. Further work is required to enhance and progress this work, with a goal of greater accuracy and fewer errors.

Original language	English
Title of host publication	Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings
Editors	Gordon Waiter, Georgios Leontidis, Teresa Morris, Tryphon Lambrou, Nir Oren, Sharon Gordon
Pages	119-132
Number of pages	14
DOIs	https://doi.org/10.1007/978-3-031-48593-0_9
State	Published - 2024
Event	27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023 - Aberdeen, United Kingdom Duration: Jul 19 2023 → Jul 21 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14122 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023
Country/Territory	United Kingdom
City	Aberdeen
Period	7/19/23 → 7/21/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Biomedical
Deep Learning
Medical Images
Orthopedics
Shoulder Implant

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-48593-0_9

Cite this

Mishra, A., Ramanathan, A., Batta, V., Malathy, C., Kundu, S. S., Gayathri, M., Vathana, D., & Kamineni, S. (2024). Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study. In G. Waiter, G. Leontidis, T. Morris, T. Lambrou, N. Oren, & S. Gordon (Eds.), Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings (pp. 119-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14122 LNCS). https://doi.org/10.1007/978-3-031-48593-0_9

Mishra, Aakriti ; Ramanathan, A. ; Batta, Vineet et al. / Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification : A Comparative Study. Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings. editor / Gordon Waiter ; Georgios Leontidis ; Teresa Morris ; Tryphon Lambrou ; Nir Oren ; Sharon Gordon. 2024. pp. 119-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study",

abstract = "Orthopedic implant identification is an important and necessary step prior to performing revision surgery of different joints. The inability to identify an implant can lead to significant surgical difficulties with consequent unfavorable outcomes. This paper proposes a novel framework to identify the make and model of seven (7) different total shoulder arthroplasty implants utilizing plain X-ray images and Artificial intelligence. The proposed work classified implants with an accuracy of 91.48\% and with an AUC (Area under curve) of 0.9932 showing higher effectiveness in orthopedic implant identification. Further work is required to enhance and progress this work, with a goal of greater accuracy and fewer errors.",

keywords = "Biomedical, Deep Learning, Medical Images, Orthopedics, Shoulder Implant",

author = "Aakriti Mishra and A. Ramanathan and Vineet Batta and C. Malathy and Kundu, \{Soumya Snigdha\} and M. Gayathri and D. Vathana and Srinath Kamineni",

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Mishra, A, Ramanathan, A, Batta, V, Malathy, C, Kundu, SS, Gayathri, M, Vathana, D & Kamineni, S 2024, Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study. in G Waiter, G Leontidis, T Morris, T Lambrou, N Oren & S Gordon (eds), Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14122 LNCS, pp. 119-132, 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023, Aberdeen, United Kingdom, 7/19/23. https://doi.org/10.1007/978-3-031-48593-0_9

Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study. / Mishra, Aakriti; Ramanathan, A.; Batta, Vineet et al.
Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings. ed. / Gordon Waiter; Georgios Leontidis; Teresa Morris; Tryphon Lambrou; Nir Oren; Sharon Gordon. 2024. p. 119-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14122 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mishra, Aakriti

AU - Ramanathan, A.

AU - Batta, Vineet

AU - Malathy, C.

AU - Kundu, Soumya Snigdha

AU - Gayathri, M.

AU - Vathana, D.

AU - Kamineni, Srinath

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Orthopedic implant identification is an important and necessary step prior to performing revision surgery of different joints. The inability to identify an implant can lead to significant surgical difficulties with consequent unfavorable outcomes. This paper proposes a novel framework to identify the make and model of seven (7) different total shoulder arthroplasty implants utilizing plain X-ray images and Artificial intelligence. The proposed work classified implants with an accuracy of 91.48% and with an AUC (Area under curve) of 0.9932 showing higher effectiveness in orthopedic implant identification. Further work is required to enhance and progress this work, with a goal of greater accuracy and fewer errors.

AB - Orthopedic implant identification is an important and necessary step prior to performing revision surgery of different joints. The inability to identify an implant can lead to significant surgical difficulties with consequent unfavorable outcomes. This paper proposes a novel framework to identify the make and model of seven (7) different total shoulder arthroplasty implants utilizing plain X-ray images and Artificial intelligence. The proposed work classified implants with an accuracy of 91.48% and with an AUC (Area under curve) of 0.9932 showing higher effectiveness in orthopedic implant identification. Further work is required to enhance and progress this work, with a goal of greater accuracy and fewer errors.

KW - Biomedical

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KW - Shoulder Implant

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Mishra A, Ramanathan A, Batta V, Malathy C, Kundu SS, Gayathri M et al. Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study. In Waiter G, Leontidis G, Morris T, Lambrou T, Oren N, Gordon S, editors, Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings. 2024. p. 119-132. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-48593-0_9

Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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