Staff View: Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving medical-expert-like performance. This opacity makes them challenging to trust in clinical practice. Recently, explainable a...

Full description

Saved in:

Bibliographic Details
Main Authors:	Teoh, Yun Xin, Othmani, Alice, Li Goh, Siew, Usman, Juliana, Lai, Khin Wee
Format:	Article
Published:	Institute of Electrical and Electronics Engineers 2024
Subjects:	QA75 Electronic computers. Computer science R Medicine (General)
Online Access:	http://eprints.um.edu.my/47121/ https://doi.org/10.1109/ACCESS.2024.3439096
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.um.eprints.47121
record_format	eprints
spelling	my.um.eprints.471212024-11-28T04:17:29Z http://eprints.um.edu.my/47121/ Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review Teoh, Yun Xin Othmani, Alice Li Goh, Siew Usman, Juliana Lai, Khin Wee QA75 Electronic computers. Computer science R Medicine (General) Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving medical-expert-like performance. This opacity makes them challenging to trust in clinical practice. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction by revealing how the prediction is derived, thus promoting the use of AI systems in healthcare. This paper presents the first survey of XAI techniques used for knee OA diagnosis. This survey identified 78 AI-based primary knee OA diagnostic test accuracy studies, of which 70 (89.7%) employed XAI. In 34 out of 70 (48.6%) of studies, XAI was utilized for the goal of visualization of predictions. Gradient-weighted class activation mapping (GradCAM) is the most common technique, being used in 24 out of 70 studies (34.3%), followed by SHapley Additive exPlanations (SHAP), being used in 9 out of 70 (12.9%) studies. All included studies analyzed the outcomes generated by XAI methods through qualitative analysis. However, only three studies utilized quantitative measures to evaluate the reliability of the XAI outcomes. We also observed that 64.3% of the studies utilized widely-circulated dataset, namely Osteoarthritis Initiative (OAI) extensively.The XAI techniques are discussed from two perspectives: data interpretability and model interpretability. Our paper provides an overview of XAI's potential towards a more reliable knee OA diagnosis approach and helps to encourage its adoption in clinical practice. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed Teoh, Yun Xin and Othmani, Alice and Li Goh, Siew and Usman, Juliana and Lai, Khin Wee (2024) Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review. IEEE Access, 12. pp. 109080-109108. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3439096 <https://doi.org/10.1109/ACCESS.2024.3439096>. https://doi.org/10.1109/ACCESS.2024.3439096 10.1109/ACCESS.2024.3439096
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science R Medicine (General)
spellingShingle	QA75 Electronic computers. Computer science R Medicine (General) Teoh, Yun Xin Othmani, Alice Li Goh, Siew Usman, Juliana Lai, Khin Wee Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review
description	Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving medical-expert-like performance. This opacity makes them challenging to trust in clinical practice. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction by revealing how the prediction is derived, thus promoting the use of AI systems in healthcare. This paper presents the first survey of XAI techniques used for knee OA diagnosis. This survey identified 78 AI-based primary knee OA diagnostic test accuracy studies, of which 70 (89.7%) employed XAI. In 34 out of 70 (48.6%) of studies, XAI was utilized for the goal of visualization of predictions. Gradient-weighted class activation mapping (GradCAM) is the most common technique, being used in 24 out of 70 studies (34.3%), followed by SHapley Additive exPlanations (SHAP), being used in 9 out of 70 (12.9%) studies. All included studies analyzed the outcomes generated by XAI methods through qualitative analysis. However, only three studies utilized quantitative measures to evaluate the reliability of the XAI outcomes. We also observed that 64.3% of the studies utilized widely-circulated dataset, namely Osteoarthritis Initiative (OAI) extensively.The XAI techniques are discussed from two perspectives: data interpretability and model interpretability. Our paper provides an overview of XAI's potential towards a more reliable knee OA diagnosis approach and helps to encourage its adoption in clinical practice.
format	Article
author	Teoh, Yun Xin Othmani, Alice Li Goh, Siew Usman, Juliana Lai, Khin Wee
author_facet	Teoh, Yun Xin Othmani, Alice Li Goh, Siew Usman, Juliana Lai, Khin Wee
author_sort	Teoh, Yun Xin
title	Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review
title_short	Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review
title_full	Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review
title_fullStr	Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review
title_full_unstemmed	Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review
title_sort	deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: a systematic review
publisher	Institute of Electrical and Electronics Engineers
publishDate	2024
url	http://eprints.um.edu.my/47121/ https://doi.org/10.1109/ACCESS.2024.3439096
_version_	1817841983735988224
score	13.223943

Deciphering Knee Osteoarthritis Diagnostic Features With Explainable Artificial Intelligence: A Systematic Review

Similar Items