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CC BY 4.0 · Journal of Gastrointestinal and Abdominal Radiology
DOI: 10.1055/s-0044-1795111
Original Article

Automated Assessment of Sarcopenia with Hounsfield Unit Average Calculation in Computed Tomography Scans Using Deep Learning Techniques

Vinayak Rengan 
1   Department of Pediatric Surgery, SMS Medical College, Jaipur, Rajasthan, India
,
Pravin Meenashi Sundaram 
2   Department of Renal Transplant Surgery, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
,
Eham Arora 
3   Department of General Surgery, Grant Medical College & Sir JJ Group of Hospitals, Mumbai, Maharashtra, India
,
Rengan Ravanasamudram Sitaraman
4   Department of General Surgery, Chennai Hernia Center, Chennai, Tamil Nadu, India
,
Praveen Sekaran 
5   Department of General Surgery, Basildon and Thurrock University Hospital, Mid and South Essex NHS Foundation Trust, Basildon, Nether Mayne, United Kingdom
,
Rohit Kalla 
6   Department of Research Science, Curium Life Tech, Chennai, Tamil Nadu, India
,
Ashvind Bawa 
7   Department of Surgery, Dayanand Medical College and Hospital, Ludhiana, Punjab, India
,
Naveen Alexander 
8   Department of General Surgery, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
,
Rochita V. Ramanan 
9   Department of Radiology, Advantage Imaging and Research Institute, Chennai, Tamil Nadu, India
› Author Affiliations Funding None.
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Abstract

Introduction Skeletal muscle is increasingly plastic with an ability to gain or lose tissue. Depletion of muscle mass and quality occurs due to various factors such as aging, disease, and disuse. Sarcopenia can be loosely defined as a significant loss of muscle mass and function. Sarcopenia is now recognized as an independent risk factor for various patient-related negative outcomes after various surgeries. Various computed tomography (CT) based imaging indices for assessment of sarcopenia exist in practice. The psoas muscle Hounsfield unit average calculation (HUAC) has been proven to be an effective one as it is independent of patient anthropometric data, and it can be calculated in the images provided.

Aim The aim of this study is to develop automated tools for estimation of the HUAC using deep learning algorithms.

Materials and Methods A total of 41 abdominal CTs were used. Ground truth was established and validated by two radiologists with more than 5 and 10 years of experience each. Models were trained to identify the psoas muscle among the slices and calculate the HUAC.

Results At inference, an average intersection over union (IoU) value of 90% was obtained between the deep learning model outputs and the original annotated test images for the CT slices. The Dice coefficient was 0.90 between the ground truth labels and the output from the model.

Conclusion We have demonstrated the accuracy of our deep learning–based algorithm for quantifying the psoas muscle HUAC, which is a marker for sarcopenia. There is a potential for a fully automated measure to calculate the HUAC for any patient undergoing CT scan.

Keywords

deep learning - HUAC - muscle quality - sarcopenia


Publication History

Article published online:
06 December 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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