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DA Silva DG, DA Silva DG, Angleri V, Scarpelli MC, Bergamasco JGA, Nóbrega SR, Damas F, Chaves TS, Camargo HDEA, Ugrinowitsch C, Libardi CA. Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound. Med Sci Sports Exerc 2024; 56:1840-1848. [PMID: 38637954 DOI: 10.1249/mss.0000000000003456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
PURPOSE Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross-sectional area (CSA) from sequential ultrasound (US) images is accessible, is reproducible, and has concurrent validity with magnetic resonance imaging. However, this technique requires numerous controls and procedures during image acquisition and reconstruction, making it laborious and time-consuming. The aim of this study was to determine the concurrent validity of VL CSA assessments between MR and computer vision-based automated reconstruction (AR) of CSA from sequential images of the VL obtained by US. METHODS The images from each sequence were manually rotated to align the fascia between images and thus visualize the VL CSA. For the AR, an artificial neural network model was utilized to segment areas of interest in the image, such as skin, fascia, deep aponeurosis, and femur. This segmentation was crucial to impose necessary constraints for the main assembly phase. At this stage, an image registration application, combined with differential evolution, was employed to achieve appropriate adjustments between the images. Next, the VL CSA obtained from the MR ( n = 488) and AR ( n = 488) techniques was used to determine their concurrent validity. RESULTS Our findings demonstrated a low coefficient of variation (CV) (1.51%) for AR compared with MR. The Bland-Altman plot showed low bias and close limits of agreement (+1.18 cm 2 , -1.19 cm 2 ), containing more than 95% of the data points. CONCLUSIONS The AR technique is valid compared with MR when measuring VL CSA in a heterogeneous sample.
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Affiliation(s)
- Deivid Gomes DA Silva
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Diego Gomes DA Silva
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Vitor Angleri
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Maíra Camargo Scarpelli
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - João Guilherme Almeida Bergamasco
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Sanmy Rocha Nóbrega
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Felipe Damas
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Talisson Santos Chaves
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | | | | | - Cleiton Augusto Libardi
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
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D’Agostino V, Sorriento A, Cafarelli A, Donati D, Papalexis N, Russo A, Lisignoli G, Ricotti L, Spinnato P. Ultrasound Imaging in Knee Osteoarthritis: Current Role, Recent Advancements, and Future Perspectives. J Clin Med 2024; 13:4930. [PMID: 39201072 PMCID: PMC11355885 DOI: 10.3390/jcm13164930] [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: 06/26/2024] [Revised: 08/04/2024] [Accepted: 08/19/2024] [Indexed: 09/02/2024] Open
Abstract
While conventional radiography and MRI have a well-established role in the assessment of patients with knee osteoarthritis, ultrasound is considered a complementary and additional tool. Moreover, the actual usefulness of ultrasound is still a matter of debate in knee osteoarthritis assessment. Despite that, ultrasound offers several advantages and interesting aspects for both current clinical practice and future perspectives. Ultrasound is potentially a helpful tool in the detection of anomalies such as cartilage degradation, osteophytes, and synovitis in cases of knee osteoarthritis. Furthermore, local diagnostic and minimally invasive therapeutic operations pertaining to knee osteoarthritis can be safely guided by real-time ultrasound imaging. We are constantly observing a growing knowledge and awareness among radiologists and other physicians, concerning ultrasound imaging. Ultrasound studies can be extremely useful to track the response to various therapies. For this specific aim, tele-ultrasonography may constitute an easy tool aiding precise and repeated follow-up controls. Moreover, raw radio-frequency data from US backscattering signals contain more information than B-mode imaging. This paves the way for quantitative in-depth analyses of cartilage, bone, and other articular structures. Overall, ultrasound technologies and their rapid evolution have the potential to make a difference at both the research and clinical levels. This narrative review article describes the potential of such technologies and their possible future implications.
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Affiliation(s)
- Valerio D’Agostino
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Via GC Pupilli 1, 40136 Bologna, Italy
- Radiology Unit, Policlinico Ospedaliero “Umberto I”, Nocera Inferiore, 84014 Salerno, Italy
| | - Angela Sorriento
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Andrea Cafarelli
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Danilo Donati
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, 41121 Modena, Italy
| | - Nicolas Papalexis
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Via GC Pupilli 1, 40136 Bologna, Italy
| | - Alessandro Russo
- Clinica 2, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
| | - Gina Lisignoli
- Laboratorio di Immunoreumatologia e Rigenerazione Tissutale, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
| | - Leonardo Ricotti
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Via GC Pupilli 1, 40136 Bologna, Italy
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Hare MM, Wohlgemuth KJ, Blue MNM, Mota JA. Reliability and Validity of Muscle Size and Quality Analysis Techniques. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1188-1193. [PMID: 38697896 DOI: 10.1016/j.ultrasmedbio.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVE This study investigated reliability and validity of muscle cross-sectional area and echo intensity using an automatic image analysis program. METHODS Twenty-two participants completed two data collection trials consisting of ultrasound imaging of the vastus lateralis (VL) at 10 and 12 MHz. Images were analyzed manually and with Deep Anatomical Cross-Sectional Area (DeepACSA). Reliability statistics (i.e., intraclass correlation coefficient [ICC] model 2,1, standard error of measure expressed as a percentage of the mean [SEM%], minimal differences [MD] values needed to be considered real) and validity statistics (i.e., constant error [CE], total error [TE], standard error of the estimate [SEE]) were calculated. RESULTS Automatic analyses of ACSA and EI demonstrated good reliability (10 MHz: ICC2,1 = 0.83 - 0.90; 12 MHz: ICC2,1 = 0.87-0.88), while manual analyses demonstrated moderate to excellent reliability (10 MHz: ICC2,1 = 0.82-0.99; 12 MHz: ICC2,1 = 0.73-0.99). Automatic analyses of ACSA presented greater error at 10 (CE = -0.76 cm2, TE = 4.94 cm2, SEE = 3.65 cm2) than 12 MHz (CE = 0.17 cm2, TE = 3.44 cm2, SEE = 3.11 cm2). Analyses of EI presented greater error at 10 (CE = 3.35 a.u., TE = 2.70 a.u., SEE = 2.58 a.u.) than at 12 MHz (CE = 3.21 a.u., TE = 2.61 a.u., SEE = 2.34 a.u.). CONCLUSION The results suggest the DeepACSA program may be less reliable compared to manual analysis for VL ACSA but displayed similar reliability for EI. In addition, the results demonstrated the automatic program had low error for 10 and 12 MHz.
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Affiliation(s)
- McKenzie M Hare
- Neuromuscular and Occupational Performance Laboratory, Texas Tech University, Lubbock, TX, USA
| | - Kealey J Wohlgemuth
- Neuromuscular and Occupational Performance Laboratory, Texas Tech University, Lubbock, TX, USA
| | - Malia N M Blue
- Health Exercise and Lifestyle Laboratory, University of North Carolina, Chapel Hill, NC, USA
| | - Jacob A Mota
- Neuromuscular and Occupational Performance Laboratory, Texas Tech University, Lubbock, TX, USA.
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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.
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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
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Kamatham AT, Alzamani M, Dockum A, Sikdar S, Mukherjee B. SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force from Highly Sparse Ultrasound Images. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2024; 54:317-324. [PMID: 38974222 PMCID: PMC11225932 DOI: 10.1109/thms.2024.3389690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared to surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches for tracking muscle anatomical structures or extracting features from brightness-mode (B-mode) images and amplitude-mode (A-mode) signals. This paper uses an offline regression convolutional neural network (CNN) called SonoMyoNet to estimate continuous isometric force from sparse ultrasound scanlines. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to estimate continuous isometric force accurately. The performance of SonoMyoNet was evaluated by varying the number of scanlines to simulate the placement of multiple single-element ultrasound transducers in a wearable system. Results showed that SonoMyoNet could accurately predict isometric force with just four scanlines and is immune to speckle noise and shifts in the scanline location. Thus, the proposed network reduces the computational load involved in feature tracking algorithms and estimates muscle force from the global features of sparse ultrasound images.
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Affiliation(s)
- Anne Tryphosa Kamatham
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi 110016 India
| | - Meena Alzamani
- Department of Bioengineering, George Mason University, Fairfax, VA 22030 USA
| | - Allison Dockum
- Department of Bioengineering, George Mason University, Fairfax, VA 22030 USA
| | - Siddhartha Sikdar
- Department of Bioengineering, George Mason University, Fairfax, VA 22030 USA
| | - Biswarup Mukherjee
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi 110016 India
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Peng J, Zeng J, Lai M, Huang R, Ni D, Li Z. One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:304-314. [PMID: 38044200 DOI: 10.1016/j.ultrasmedbio.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/23/2023] [Accepted: 10/22/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVE Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating this problem, we developed a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluated its effectiveness as a computer-aided diagnostic tool. METHODS We combined real-time MN delineation, accurate biometric measurements and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We then collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol. RESULTS The proposed model exhibited better segmentation and measurement performance than competing methods, with a Hausdorff distance (95th percentile) score of 7.21 px, average symmetric surface distance score of 2.64 px, Dice score of 85.78% and intersection over union score of 76.00%. In the reader study, it exhibited performance comparable to the average performance of experienced radiologists in classifying CTS and outperformed inexperienced radiologists in terms of classification metrics (e.g., accuracy score 3.59% higher and F1 score 5.85% higher). CONCLUSION Diagnostic performance of the OSA-CTSD was promising, with the advantages of real-time delineation, automation and clinical interpretability. The application of such a tool not only reduces reliance on the expertise of examiners but also can help to promote future standardization of the CTS diagnostic process, benefiting both patients and radiologists.
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Affiliation(s)
- Jiayu Peng
- Department of Ultrasound, Second People's Hospital of Shenzhen, First Affiliated Hospital of Shenzhen University, Shenzhen, China; Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
| | - Jiajun Zeng
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Manlin Lai
- Ultrasound Division, Department of Medical Imaging, University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Ruobing Huang
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Dong Ni
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Zhenzhou Li
- Department of Ultrasound, Second People's Hospital of Shenzhen, First Affiliated Hospital of Shenzhen University, Shenzhen, China; Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
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Frącz W, Matuska J, Szyszka J, Dobrakowski P, Szopka W, Skorupska E. The Cross-Sectional Area Assessment of Pelvic Muscles Using the MRI Manual Segmentation among Patients with Low Back Pain and Healthy Subjects. J Imaging 2023; 9:155. [PMID: 37623687 PMCID: PMC10455268 DOI: 10.3390/jimaging9080155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 07/25/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
The pain pathomechanism of chronic low back pain (LBP) is complex and the available diagnostic methods are insufficient. Patients present morphological changes in volume and cross-sectional area (CSA) of lumbosacral region. The main objective of this study was to assess if CSA measurements of pelvic muscle will indicate muscle atrophy between asymptomatic and symptomatic sides in chronic LBP patients, as well as between right and left sides in healthy volunteers. In addition, inter-rater reliability for CSA measurements was examined. The study involved 71 chronic LBP patients and 29 healthy volunteers. The CSA of gluteus maximus, medius, minimus and piriformis were measured using the MRI manual segmentation method. Muscle atrophy was confirmed in gluteus maximus, gluteus minimus and piriformis muscle for over 50% of chronic LBP patients (p < 0.05). Gluteus medius showed atrophy in patients with left side pain occurrence (p < 0.001). Muscle atrophy occurred on the symptomatic side for all inspected muscles, except gluteus maximus in rater one assessment. The reliability of CSA measurements between raters calculated using CCC and ICC presented great inter-rater reproducibility for each muscle both in patients and healthy volunteers (p < 0.95). Therefore, there is the possibility of using CSA assessment in the diagnosis of patients with symptoms of chronic LBP.
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Affiliation(s)
- Wiktoria Frącz
- Faculty of Biomedical Sciences, Medical University of Lodz, Al. Kosciuszki 4, 90-419 Lodz, Poland;
| | - Jakub Matuska
- Department of Physiotherapy, Poznan University of Medical Sciences, ul. 28 czerwca 1956r. nr 135/147, 61-545 Poznan, Poland;
- Doctoral School, Poznan University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland
- Doctoral School, Rovira I Virgili University, Carrer St. Llorenç No. 21, 43201 Reus, Spain
| | - Jarosław Szyszka
- Opole Rehabilitation Centre in Korfantów, Wyzwolenia 11, 48-317 Korfantów, Poland
| | - Paweł Dobrakowski
- Psychology Institute, Humanitas University in Sosnowiec, 41-200 Sosnowiec, Poland
| | - Wiktoria Szopka
- Faculty of Veterinary Medicine and Animal Science, Poznan University of Life Sciences, 60-637 Poznań, Poland
| | - Elżbieta Skorupska
- Department of Physiotherapy, Poznan University of Medical Sciences, ul. 28 czerwca 1956r. nr 135/147, 61-545 Poznan, Poland;
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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.
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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
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Ritsche P, Wirth P, Cronin NJ, Sarto F, Narici MV, Faude O, Franchi MV. DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning. Med Sci Sports Exerc 2022; 54:2188-2195. [PMID: 35941517 DOI: 10.1249/mss.0000000000003010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE Muscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the human rectus femoris (RF), vastus lateralis (VL), gastrocnemius medialis (GM) and lateralis (GL) muscles. METHODS We trained three muscle-specific convolutional neural networks (CNN) using 1772 ultrasound images from 153 participants (age = 38.2 yr, range = 13-78). Images were acquired in 10% increments from 30% to 70% of femur length for RF and VL and at 30% and 50% of muscle length for GM and GL. During training, CNN performance was evaluated using intersection-over-union scores. We compared the performance of DeepACSA to manual analysis and a semiautomated algorithm using an unseen test set. RESULTS Comparing DeepACSA analysis of the RF to manual analysis with erroneous predictions removed (3.3%) resulted in intraclass correlation (ICC) of 0.989 (95% confidence interval = 0.983-0.992), mean difference of 0.20 cm 2 (0.10-0.30), and SEM of 0.33 cm 2 (0.26-0.41). For the VL, ICC was 0.97 (0.96-0.968), mean difference was 0.85 cm 2 (-0.4 to 1.31), and SEM was 0.92 cm 2 (0.73-1.09) after removal of erroneous predictions (7.7%). After removal of erroneous predictions (12.3%), GM/GL muscles demonstrated an ICC of 0.98 (0.96-0.99), a mean difference of 0.43 cm 2 (0.21-0.65), and an SEM of 0.41 cm 2 (0.29-0.51). Analysis duration was 4.0 ± 0.43 s (mean ± SD) for analysis of one image in our test set using DeepACSA. CONCLUSIONS DeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable with manual segmentation. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high-quality image for accurate prediction.
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Affiliation(s)
- Paul Ritsche
- Department of Sport, Exercise and Health, University of Basel, Basel, SWITZERLAND
| | | | - Neil J Cronin
- Neuromuscular Research Centre, Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, FINLAND
| | - Fabio Sarto
- Department of Biomedical Sciences, University of Padova, Padova, ITALY
| | | | - Oliver Faude
- Department of Sport, Exercise and Health, University of Basel, Basel, SWITZERLAND
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Naruse M, Trappe SW, Trappe TA. Human skeletal muscle size with ultrasound imaging: a comprehensive review. J Appl Physiol (1985) 2022; 132:1267-1279. [PMID: 35358402 PMCID: PMC9126220 DOI: 10.1152/japplphysiol.00041.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Skeletal muscle size is an important factor in assessing adaptation to exercise training and detraining, athletic performance, age-associated atrophy and mobility decline, clinical conditions associated with cachexia, and overall skeletal muscle health. Magnetic resonance (MR) imaging and computed tomography (CT) are widely accepted as the gold standard methods for skeletal muscle size quantification. However, it is not always feasible to use these methods (e.g., field studies, bedside studies, large cohort studies). Ultrasound has been available for skeletal muscle examination for more than 50 years and the development, utility, and validity of ultrasound imaging are underappreciated. It is now possible to use ultrasound in situations where MR and CT imaging are not suitable. This review provides a comprehensive summary of ultrasound imaging and human skeletal muscle size assessment. Since the first study in 1968, more than 600 articles have used ultrasound to examine the cross-sectional area and/or volume of 107 different skeletal muscles in more than 27,500 subjects of various ages, health status, and fitness conditions. Data from these studies, supported by decades of technological developments, collectively show that ultrasonography is a valid tool for skeletal muscle size quantification. Considering the wide-ranging connections between human health and function and skeletal muscle mass, the utility of ultrasound imaging will allow it to be employed in research investigations and clinical practice in ways not previously appreciated or considered.
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Affiliation(s)
- Masatoshi Naruse
- Human Performance Laboratory, Ball State University, Muncie, IN, United States
| | - Scott W Trappe
- Human Performance Laboratory, Ball State University, Muncie, IN, United States
| | - Todd A Trappe
- Human Performance Laboratory, Ball State University, Muncie, IN, United States
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11
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Automated Segmentation of Median Nerve in Dynamic Sonography Using Deep Learning: Evaluation of Model Performance. Diagnostics (Basel) 2021; 11:diagnostics11101893. [PMID: 34679591 PMCID: PMC8534332 DOI: 10.3390/diagnostics11101893] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/01/2021] [Accepted: 10/10/2021] [Indexed: 11/21/2022] Open
Abstract
There is an emerging trend to employ dynamic sonography in the diagnosis of entrapment neuropathy, which exhibits aberrant spatiotemporal characteristics of the entrapped nerve when adjacent tissues move. However, the manual tracking of the entrapped nerve in consecutive images demands tons of human labors and impedes its popularity clinically. Here we evaluated the performance of automated median nerve segmentation in dynamic sonography using a variety of deep learning models pretrained with ImageNet, including DeepLabV3+, U-Net, FPN, and Mask-R-CNN. Dynamic ultrasound images of the median nerve at across wrist level were acquired from 52 subjects diagnosed as carpal tunnel syndrome when they moved their fingers. The videos of 16 subjects exhibiting diverse appearance and that of the remaining 36 subjects were used for model test and training, respectively. The centroid, circularity, perimeter, and cross section area of the median nerve in individual frame were automatically determined from the inferred nerve. The model performance was evaluated by the score of intersection over union (IoU) between the annotated and model-predicted data. We found that both DeepLabV3+ and Mask R-CNN predicted median nerve the best with averaged IOU scores close to 0.83, which indicates the feasibility of automated median nerve segmentation in dynamic sonography using deep learning.
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Ritsche P, Wirth P, Franchi MV, Faude O. ACSAuto-semi-automatic assessment of human vastus lateralis and rectus femoris cross-sectional area in ultrasound images. Sci Rep 2021; 11:13042. [PMID: 34158572 PMCID: PMC8219722 DOI: 10.1038/s41598-021-92387-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 06/07/2021] [Indexed: 01/19/2023] Open
Abstract
Open-access scripts to perform muscle anatomical cross-sectional area (ACSA) evaluation in ultrasound images are currently unavailable. This study presents a novel semi-automatic ImageJ script (named "ACSAuto") for quantifying the ACSA of lower limb muscles. We compared manual ACSA measurements from 180 ultrasound scans of vastus lateralis (VL) and rectus femoris (RF) muscles to measurements assessed by the ACSAuto script. We investigated inter- and intra-investigator reliability of the script. Consecutive-pairwise intra-class correlations (ICC) and standard error of measurement (SEM) with 95% compatibility interval were calculated. Bland-Altman analyses were employed to test the agreement between measurements. Comparing manual and ACSAuto measurements, ICCs and SEMs ranged from 0.96 to 0.999 and 0.12 to 0.96 cm2 (1.2-5.9%) and mean bias was smaller than 0.5 cm2 (4.3%). Inter-investigator comparison revealed ICCs, SEMs and mean bias ranging from 0.85 to 0.999, 0.07 to 1.16 cm2 (0.9-7.6%) and - 0.16 to 0.66 cm2 (- 0.6 to 3.2%). Intra-investigator comparison revealed ICCs, SEMs and mean bias between 0.883-0.998, 0.07-0.93 cm2 (1.1-7.6%) and - 0.80 to 0.15 cm2 (- 3.4 to 1.8%). Image quality needs to be high for efficient and accurate ACSAuto analyses. Taken together, the ACSAuto script represents a reliable tool to measure RF and VL ACSA, is comparable to manual analysis and can reduce time needed to evaluate ultrasound images.
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Affiliation(s)
- Paul Ritsche
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland.
| | - Philipp Wirth
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Martino V Franchi
- Institute of Physiology, Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Oliver Faude
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
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13
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Shen YT, Chen L, Yue WW, Xu HX. Artificial intelligence in ultrasound. Eur J Radiol 2021; 139:109717. [PMID: 33962110 DOI: 10.1016/j.ejrad.2021.109717] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/28/2021] [Accepted: 04/11/2021] [Indexed: 12/13/2022]
Abstract
Ultrasound (US), a flexible green imaging modality, is expanding globally as a first-line imaging technique in various clinical fields following with the continual emergence of advanced ultrasonic technologies and the well-established US-based digital health system. Actually, in US practice, qualified physicians should manually collect and visually evaluate images for the detection, identification and monitoring of diseases. The diagnostic performance is inevitably reduced due to the intrinsic property of high operator-dependence from US. In contrast, artificial intelligence (AI) excels at automatically recognizing complex patterns and providing quantitative assessment for imaging data, showing high potential to assist physicians in acquiring more accurate and reproducible results. In this article, we will provide a general understanding of AI, machine learning (ML) and deep learning (DL) technologies; We then review the rapidly growing applications of AI-especially DL technology in the field of US-based on the following anatomical regions: thyroid, breast, abdomen and pelvis, obstetrics heart and blood vessels, musculoskeletal system and other organs by covering image quality control, anatomy localization, object detection, lesion segmentation, and computer-aided diagnosis and prognosis evaluation; Finally, we offer our perspective on the challenges and opportunities for the clinical practice of biomedical AI systems in US.
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Affiliation(s)
- Yu-Ting Shen
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China
| | - Liang Chen
- Department of Gastroenterology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, PR China
| | - Wen-Wen Yue
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China.
| | - Hui-Xiong Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China.
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14
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Marzola F, Alfen NV, Salvi M, Santi BD, Doorduin J, Meiburger KM. Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2113-2116. [PMID: 33018423 DOI: 10.1109/embc44109.2020.9176343] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound images of gastrocnemius medialis using a convolutional neural network(CNN). In the provided dataset images with both normal and increased echogenicity are present. The manually annotated dataset consisted of 591 images, from 200 subjects, 400 relative to subjects with normal echogenicity and 191 to subjects with augmented echogenicity. From the DICOM files, the image has been extracted and processed using the CNN, then the output has been post-processed to obtain a finer segmentation. Final results have been compared to the manual segmentations. Precision and Recall scores as mean ± standard deviation for training, validation, and test sets are 0.96 ± 0.05, 0.90 ± 0.18, 0.89 ± 0.15 and 0.97 ±0.03, 0.89± 0.17, 0.90 ± 0.14 respectively. The CNN approach has also been compared to another automatic algorithm, showing better performances. The proposed automatic method provides an accurate estimation of muscle cross-sectional area in muscles with different echogenicity levels.
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