1
|
Strohm H, Rothluebbers S, Perotti L, Stamm O, Fournelle M, Jenne J, Guenther M. Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications. Int J Comput Assist Radiol Surg 2024; 19:1607-1614. [PMID: 38865060 DOI: 10.1007/s11548-024-03204-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Wearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles. METHODS The first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise. RESULTS Using the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too. CONCLUSION Both retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived.
Collapse
Affiliation(s)
- Hannah Strohm
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
- mediri GmbH, Heidelberg, Germany.
| | | | - Luis Perotti
- Geriatrics and Medical Gerontology, Charité-Universitaetsmedizin Berlin, Berlin, Germany
| | - Oskar Stamm
- Geriatrics and Medical Gerontology, Charité-Universitaetsmedizin Berlin, Berlin, Germany
| | - Marc Fournelle
- Fraunhofer Institute for Biomedical Engineering IBMT, Sulzbach, Germany
| | - Juergen Jenne
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- mediri GmbH, Heidelberg, Germany
| | - Matthias Guenther
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- mediri GmbH, Heidelberg, Germany
- University of Bremen, Bremen, Germany
| |
Collapse
|
2
|
Nhat PTH, Van Hao N, Yen LM, Anh NH, Khiem DP, Kerdegari H, Phuong LT, Hoang VT, Ngoc NT, Thu LNM, Trung TN, Pisani L, Razavi R, Yacoub S, Van Vinh Chau N, King AP, Thwaites L, Denehy L, Gomez A. Clinical evaluation of AI-assisted muscle ultrasound for monitoring muscle wasting in ICU patients. Sci Rep 2024; 14:14798. [PMID: 38926427 PMCID: PMC11208490 DOI: 10.1038/s41598-024-64564-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998-0.999 vs. 0.982 95% CI 0.962-0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9-21.7) to 9.4 min (IQR 7.2-11.7) compared to when using the AI tool (p < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes.
Collapse
Affiliation(s)
- Phung Tran Huy Nhat
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK.
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.
| | - Nguyen Van Hao
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Lam Minh Yen
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | | | | | - Hamideh Kerdegari
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Le Thanh Phuong
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Vo Tan Hoang
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | | | | | | | | | - Reza Razavi
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Sophie Yacoub
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Andrew P King
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Alberto Gomez
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| |
Collapse
|
3
|
Calulo Rivera Z, González-Seguel F, Horikawa-Strakovsky A, Granger C, Sarwal A, Dhar S, Ntoumenopoulos G, Chen J, Bumgardner VKC, Parry SM, Mayer KP, Wen Y. MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.26.24306153. [PMID: 38746458 PMCID: PMC11092729 DOI: 10.1101/2024.04.26.24306153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Introduction/Aims Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects. Methods Quadriceps complex (QC [rectus femoris and vastus intermedius]) and tibialis anterior (TA) muscle ultrasound images of healthy, intensive care unit, and/or lung cancer subjects were captured with portable devices. Automated analyses of muscle morphometry were performed using a custom-built deep-learning model (MyoVision-US), while manual analyses were performed by experts. Consistency between manual and automated analyses was determined using intraclass correlation coefficients (ICC), while predictability of MyoVision -US was calculated using adjusted linear regression (adj.R 2 ). Results Manual analysis took approximately 24 hours to analyze all 180 images, while MyoVision - US took 247 seconds, saving roughly 99.8%. Consistency between the manual and automated analyses by ICC was good to excellent for all QC (ICC:0.85-0.99) and TA (ICC:0.93-0.99) measurements, even for critically ill (ICC:0.91-0.98) and lung cancer (ICC:0.85-0.99) images. The predictability of MyoVision-US was moderate to strong for QC (adj.R 2 :0.56-0.94) and TA parameters (adj.R 2 :0.81-0.97). Discussion The application of AI automating lower limb muscle ultrasound analyses showed excellent consistency and strong predictability compared with human analysis. Future work needs to explore AI-powered models for the evaluation of other skeletal muscle groups.
Collapse
|
4
|
Vafaeezadeh M, Behnam H, Gifani P. Ultrasound Image Analysis with Vision Transformers-Review. Diagnostics (Basel) 2024; 14:542. [PMID: 38473014 DOI: 10.3390/diagnostics14050542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/22/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.
Collapse
Affiliation(s)
- Majid Vafaeezadeh
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran 1311416846, Iran
| | - Hamid Behnam
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran 1311416846, Iran
| | - Parisa Gifani
- Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
| |
Collapse
|
5
|
Taha MA, Morren JA. The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions. Muscle Nerve 2024; 69:260-272. [PMID: 38151482 DOI: 10.1002/mus.28023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/29/2023]
Abstract
The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.
Collapse
Affiliation(s)
- Mohamed A Taha
- Neuromuscular Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - John A Morren
- Neuromuscular Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| |
Collapse
|
6
|
Guo C, Li M, Xu J, Bai L. Ultrasonic characterization of small defects based on Res-ViT and unsupervised domain adaptation. ULTRASONICS 2024; 137:107194. [PMID: 37925964 DOI: 10.1016/j.ultras.2023.107194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/07/2023]
Abstract
This paper investigated the application of deep neural networks and domain adaptation for ultrasonic characterization of elliptical defects that are small and inclined. Based on performance evaluation of deep residual network (ResNet) and vision transformer (ViT), we proposed a novel Res-ViT architecture which fuses deep representative features of both models. Furthermore, we developed an unsupervised domain adaptation method to minimize the distance between the source and target domains, which is measured by maximum mean discrepancy. This approach serves to improve the generalizability of the proposed Res-ViT model in noisy environments. Simulation studies were performed at various noise levels to evaluate robustness of different deep neural networks. The proposed Res-ViT model was shown to reduce the characterization uncertainty of various defect parameters, including size, angle, and aspect ratio. Experiments were performed on three elliptical defects which have large orientation angles of 60∘ relative to the array direction. The proposed method achieved a 61% reduction in the root-mean-square error (RMSE) of defect size compared to a benchmark approach, which is based on principal component analysis and the nearest neighbor method.
Collapse
Affiliation(s)
- Changrong Guo
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Mingxuan Li
- College of Life Sciences and Technology, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Jianfeng Xu
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Long Bai
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
| |
Collapse
|
7
|
Klawitter F, Walter U, Axer H, Patejdl R, Ehler J. Neuromuscular Ultrasound in Intensive Care Unit-Acquired Weakness: Current State and Future Directions. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050844. [PMID: 37241077 DOI: 10.3390/medicina59050844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023]
Abstract
Intensive care unit-acquired weakness (ICUAW) is one of the most common causes of muscle atrophy and functional disability in critically ill intensive care patients. Clinical examination, manual muscle strength testing and monitoring are frequently hampered by sedation, delirium and cognitive impairment. Many different attempts have been made to evaluate alternative compliance-independent methods, such as muscle biopsies, nerve conduction studies, electromyography and serum biomarkers. However, they are invasive, time-consuming and often require special expertise to perform, making them vastly impractical for daily intensive care medicine. Ultrasound is a broadly accepted, non-invasive, bedside-accessible diagnostic tool and well established in various clinical applications. Hereby, neuromuscular ultrasound (NMUS), in particular, has been proven to be of significant diagnostic value in many different neuromuscular diseases. In ICUAW, NMUS has been shown to detect and monitor alterations of muscles and nerves, and might help to predict patient outcome. This narrative review is focused on the recent scientific literature investigating NMUS in ICUAW and highlights the current state and future opportunities of this promising diagnostic tool.
Collapse
Affiliation(s)
- Felix Klawitter
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Rostock University Medical Center, Schillingallee 35, 18057 Rostock, Germany
| | - Uwe Walter
- Department of Neurology, Rostock University Medical Center, Gehlsheimer Straße 20, 18147 Rostock, Germany
| | - Hubertus Axer
- Department of Neurology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Robert Patejdl
- Department of Medicine, Health and Medical University Erfurt, 99089 Erfurt, Germany
| | - Johannes Ehler
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| |
Collapse
|