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Hohlmann B, Broessner P, Radermacher K. Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery - a review and future challenges. Comput Assist Surg (Abingdon) 2024; 29:2276055. [PMID: 38261543 DOI: 10.1080/24699322.2023.2276055] [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] [Indexed: 01/25/2024] Open
Abstract
Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.
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Affiliation(s)
- Benjamin Hohlmann
- Chair of Medical Engineering, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany
| | - Peter Broessner
- Chair of Medical Engineering, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany
| | - Klaus Radermacher
- Chair of Medical Engineering, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany
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2
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Getzmann JM, Zantonelli G, Messina C, Albano D, Serpi F, Gitto S, Sconfienza LM. The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01856-1. [PMID: 39001961 DOI: 10.1007/s11547-024-01856-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 07/04/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE To systematically review the use of artificial intelligence (AI) in musculoskeletal (MSK) ultrasound (US) with an emphasis on AI algorithm categories and validation strategies. MATERIAL AND METHODS An electronic literature search was conducted for articles published up to January 2024. Inclusion criteria were the use of AI in MSK US, involvement of humans, English language, and ethics committee approval. RESULTS Out of 269 identified papers, 16 studies published between 2020 and 2023 were included. The research was aimed at predicting diagnosis and/or segmentation in a total of 11 (69%) out of 16 studies. A total of 11 (69%) studies used deep learning (DL)-based algorithms, three (19%) studies employed conventional machine learning (ML)-based algorithms, and two (12%) studies employed both conventional ML- and DL-based algorithms. Six (38%) studies used cross-validation techniques with K-fold cross-validation being the most frequently employed (n = 4, 25%). Clinical validation with separate internal test datasets was reported in nine (56%) papers. No external clinical validation was reported. CONCLUSION AI is a topic of increasing interest in MSK US research. In future studies, attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets.
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Affiliation(s)
| | - Giulia Zantonelli
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
| | - Carmelo Messina
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
- UOC Radiodiagnostica, ASST Centro Specialistico Ortopedico Traumatologico Gaetano Pini-CTO, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Scienze Biomediche, Chirurgiche Ed Odontoiatriche, Università Degli Studi Di Milano, Milan, Italy
| | | | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy.
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
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Jacobs E, Wainman B, Bowness J. Applying artificial intelligence to the use of ultrasound as an educational tool: A focus on ultrasound-guided regional anesthesia. ANATOMICAL SCIENCES EDUCATION 2024; 17:919-925. [PMID: 36880869 DOI: 10.1002/ase.2266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/10/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Emma Jacobs
- Department of Anaesthesia, Royal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK
| | - Bruce Wainman
- Education Program in Anatomy, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Science, McMaster University, Hamilton, Ontario, Canada
| | - James Bowness
- Department of Anaesthesia, Royal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK
- OxSTaR Center, Nuffield Division of Anaesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Pan G. Current status of dynamic musculoskeletal ultrasound for application to treatment of orthopedic diseases. Am J Transl Res 2024; 16:2180-2189. [PMID: 39006303 PMCID: PMC11236655 DOI: 10.62347/wher3512] [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: 03/20/2024] [Accepted: 05/08/2024] [Indexed: 07/16/2024]
Abstract
In recent years, dynamic musculoskeletal ultrasound (DMUS) technology has demonstrated great potential in the diagnosis and treatment of orthopedic diseases. As a non-invasive, real-time imaging technique, it provides a dynamic view of joints and soft tissues, offering crucial insight for evaluating muscle injuries and bone damage caused by motion. This article comprehensively reviews the latest research in the field of DMUS, discussing its significant roles in managing orthopedic diseases and enhancing clinical practice. The application of DMUS is wide-ranging, including but not limited to the diagnosis of tendon injuries, ligament tears, arthritis, and soft tissue diseases. Additionally, DMUS has significant value in monitoring treatment progress and evaluating post-operative recovery. Furthermore, we discuss the use of DMUS for improving the accuracy and effectiveness of orthopedic surgeries. DMUS can provide high-quality diagnostic and therapeutic services for patients without a need for expensive equipment or complex procedures. Despite its promising outlook in orthopedics, broader clinical adoption remains limited by factors such as the steep learning curve associated with its use, the demand for specialized skills in interpreting high-quality images, and the need for extensive clinical validation. Future research should focus on standardizing operational procedures, improving the automation of image analysis, and validating its application in different orthopedic diseases through clinical trials.
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Affiliation(s)
- Guobin Pan
- Medical Equipment Department, Yantai Yantaishan Hospital Yantai 264003, Shandong, China
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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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Wu L, Xia D, Wang J, Chen S, Cui X, Shen L, Huang Y. Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation. Diagnostics (Basel) 2024; 14:755. [PMID: 38611668 PMCID: PMC11011346 DOI: 10.3390/diagnostics14070755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/14/2024] Open
Abstract
The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated.
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Affiliation(s)
| | | | | | | | - Xulei Cui
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100006, China; (L.W.); (D.X.); (J.W.); (S.C.); (L.S.); (Y.H.)
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Alrashdi N, Alotaibi M, Alharthi M, Kashoo F, Alanazi S, Alanazi A, Alzhrani M, Alhussainan T, Alanazi R, Almutairi R, Ithurburn M. Incidence, Prevalence, Risk Factors, and Clinical Treatment for Children with Developmental Dysplasia of the Hip in Saudi Arabia. A Systematic Review. J Epidemiol Glob Health 2024:10.1007/s44197-024-00217-5. [PMID: 38483754 DOI: 10.1007/s44197-024-00217-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/09/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND Developmental dysplasia of the hip (DDH) leads to pain, joint instability, and early degenerative joint disease. Incidence, prevalence, and management strategies of DDH have been well-documented in several countries, but not in Saudi Arabia. OBJECTIVE We synthesized the current evidence regarding incidence, prevalence, risk factors, and clinical treatment for children with DDH in Saudi Arabia. METHODS We searched 3 databases to locate studies. Studies that included children with DDH in Saudi Arabia; reported either incidence rate, prevalence, risk factors, and/or clinical practice; and were available in English or Arabic were included. We excluded reviews, case studies, or animal studies. Two independent authors reviewed potential studies and assessed study's quality. RESULTS Our search yielded 67 potential studies, of which 16 studies were included (total DDH sample = 3,127; age range = 2.5 to 86.4 months). Three studies reported incidence rates ranging from 3.1 to 4.9 per 1000 births, and 3 studies reported prevalence ranging from 6 to 78%. Nine studies reported that female sex, breech position, family history, and age less than 3 years were risk factors associated with DDH. Four studies reported that brace applications and closed reduction were conservative treatments, and 9 studies reported that open hip reduction, adductor tenotomy, and/or pelvic osteotomy were surgical approaches to treat DDH. CONCLUSIONS In Saudi Arabia, the Incidence and prevalence rates of DDH are 3.1 to 4.9 per 1,000 births, and 6-78%, respectively (differ from what has been reported in other countries), but the risk factors of DDH in Saudi Arabia appear to be similar in comparison to other countries (female, breech presentation, family history of DDH).
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Affiliation(s)
- Naif Alrashdi
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia.
| | - Mansour Alotaibi
- Department of Physical Therapy, College of Applied Medical Sciences, Northern Border University, Arar, Saudi Arabia
| | - Moqfa Alharthi
- Rehabilitation Services Department, King Abdullah Specialized Children's Hospital, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Faizan Kashoo
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
| | - Sultan Alanazi
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
| | - Ahmad Alanazi
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
| | - Msaad Alzhrani
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
| | - Thamer Alhussainan
- Department of Orthopedics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Rami Alanazi
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
- Department of Physical Therapy and Rehabilitation, King Khaled Hospital, Almajmaah, Saudi Arabia
| | - Rakan Almutairi
- Department of Physical Therapy and Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952, Saudi Arabia
- Physiotherapy Department, Al Iman General Hospital, Riyadh First Health Cluster, Riyadh, Saudi Arabia
| | - Matthew Ithurburn
- American Sports Medicine Institute, Birmingham, AL, USA
- Department of Physical Therapy, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, USA
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Traboco LS. Class-Rheum for AI: Reflections from a Filipino Rheumatologist and Health Informatics graduate student. Int J Rheum Dis 2024; 27:e15094. [PMID: 38450964 DOI: 10.1111/1756-185x.15094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/01/2024] [Accepted: 02/11/2024] [Indexed: 03/08/2024]
Affiliation(s)
- Lisa S Traboco
- St Luke's Medical Center, Global City, Philippines
- University of the Philippines, Manila - Medical Informatics Unit, Manila, Philippines
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Yi PH, Garner HW, Hirschmann A, Jacobson JA, Omoumi P, Oh K, Zech JR, Lee YH. Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2024; 222:e2329530. [PMID: 37436032 DOI: 10.2214/ajr.23.29530] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped. In comparison with other modalities, ultrasound has unique advantages and disadvantages that must be considered in AI algorithm development and clinical translation. Challenges in developing AI for musculoskeletal ultrasound involve both clinical aspects of image acquisition and practical limitations in image processing and annotation. Solutions from other radiology subspecialties (e.g., crowdsourced annotations coordinated by professional societies), along with use cases (most commonly rotator cuff tendon tears and palpable soft-tissue masses), can be applied to musculoskeletal ultrasound to help develop AI. To facilitate creation of high-quality imaging datasets for AI model development, technologists and radiologists should focus on increasing uniformity in musculoskeletal ultrasound performance and increasing annotations of images for specific anatomic regions. This Expert Panel Narrative Review summarizes available evidence regarding AI's potential utility in musculoskeletal ultrasound and challenges facing its development. Recommendations for future AI advancement and clinical translation in musculoskeletal ultrasound are discussed.
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Affiliation(s)
- Paul H Yi
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD
| | | | - Anna Hirschmann
- Imamed Radiology Nordwest, Basel, Switzerland
- Department of Radiology, University of Basel, Basel, Switzerland
| | - Jon A Jacobson
- Lenox Hill Radiology, New York, NY
- Department of Radiology, University of California, San Diego Medical Center, San Diego, CA
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Kangrok Oh
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea
| | - John R Zech
- Department of Radiology, Columbia University Irving Medical Center, New York-Presbyterian Hospital, New York, NY
| | - Young Han Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea
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10
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Virto N, Río X, Angulo-Garay G, García Molina R, Avendaño Céspedes A, Cortés Zamora EB, Gómez Jiménez E, Alcantud Córcoles R, Rodriguez Mañas L, Costa-Grille A, Matheu A, Marcos-Pérez D, Lazcano U, Vergara I, Arjona L, Saeteros M, Lopez-de-Ipiña D, Coca A, Abizanda Soler P, Sanabria SJ. Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study. JMIR Res Protoc 2024; 13:e50325. [PMID: 38393761 PMCID: PMC10924264 DOI: 10.2196/50325] [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: 09/12/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Frailty resulting from the loss of muscle quality can potentially be delayed through early detection and physical exercise interventions. There is a demand for cost-effective tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessments. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional, and microstructural muscle properties, while biological assays derived from blood samples are associated with functional information. OBJECTIVE This study aims to assess multiparametric combinations of ultrasound and blood-based biomarkers to offer a cross-sectional evaluation of the patient frailty phenotype and to track changes in muscle quality associated with supervised exercise programs. METHODS This prospective observational multicenter study will include patients aged 70 years and older who are capable of providing informed consent. We aim to recruit 100 patients from hospital environments and 100 from primary care facilities. Each patient will undergo at least two examinations (baseline and follow-up), totaling a minimum of 400 examinations. In hospital environments, 50 patients will be measured before/after a 16-week individualized and supervised exercise program, while another 50 patients will be followed up after the same period without intervention. Primary care patients will undergo a 1-year follow-up evaluation. The primary objective is to compare cross-sectional evaluations of physical performance, functional capacity, body composition, and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based assays. We will analyze ultrasound raw data obtained with a point-of-care device, along with a set of biomarkers previously associated with frailty, using quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Additionally, we will examine the sensitivity of these biomarkers to detect short-term muscle quality changes and functional improvement after a supervised exercise intervention compared with usual care. RESULTS At the time of manuscript submission, the enrollment of volunteers is ongoing. Recruitment started on March 1, 2022, and ends on June 30, 2024. CONCLUSIONS The outlined study protocol will integrate portable technologies, using quantitative muscle ultrasound and blood biomarkers, to facilitate an objective cross-sectional assessment of muscle quality in both hospital and primary care settings. The primary objective is to generate data that can be used to explore associations between biomarker combinations and the cross-sectional clinical assessment of frailty and sarcopenia. Additionally, the study aims to investigate musculoskeletal changes following multicomponent physical exercise programs. TRIAL REGISTRATION ClinicalTrials.gov NCT05294757; https://clinicaltrials.gov/ct2/show/NCT05294757. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/50325.
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Affiliation(s)
- Naiara Virto
- Department of Physical Activity and Sport Science, Faculty of Education and Sport, University of Deusto, Bilbao, Spain
| | - Xabier Río
- Department of Physical Activity and Sport Science, Faculty of Education and Sport, University of Deusto, Bilbao, Spain
| | - Garazi Angulo-Garay
- Department of Physical Activity and Sport Science, Faculty of Education and Sport, University of Deusto, Bilbao, Spain
| | - Rafael García Molina
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
| | - Almudena Avendaño Céspedes
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
- Facultad de Enfermería de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Elisa Belen Cortés Zamora
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
| | - Elena Gómez Jiménez
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
| | - Ruben Alcantud Córcoles
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
| | - Leocadio Rodriguez Mañas
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
- Geriatrics Department, University Hospital of Getafe, Getafe, Spain
| | | | - Ander Matheu
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
- Biodonostia, Health Research Institute, Donostia, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Diego Marcos-Pérez
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
| | - Uxue Lazcano
- Biodonostia, Health Research Institute, Donostia, Spain
| | - Itziar Vergara
- Biodonostia, Health Research Institute, Donostia, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
- Osakidetza, Health Care Department, Research Unit APOSIs, Gipuzkoa, Spain
- Research Network in Chronicity, Primary Care and Health Promotion (RICAPPS), Barakaldo, Spain
| | - Laura Arjona
- Deusto Institute of Technology, University of Deusto, Bilbao, Spain
| | - Morelva Saeteros
- Deusto Institute of Technology, University of Deusto, Bilbao, Spain
| | | | - Aitor Coca
- Department of Physical Activity and Sports Sciences, Faculty of Health Sciences, Euneiz University, Vitoria-Gasteiz, Spain
| | - Pedro Abizanda Soler
- Department of Geriatrics, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
- Center for Biomedical Research Network on Fragility and Healthy Aging (CIBERfes), Instituto de Salud Carlos III, Madrid, Spain
- Facultad de Medicina de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Sergio J Sanabria
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
- Deusto Institute of Technology, University of Deusto, Bilbao, Spain
- Department of Radiology, Stanford University, Palo Alto, CA, United States
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Gitto S, Serpi F, Albano D, Risoleo G, Fusco S, Messina C, Sconfienza LM. AI applications in musculoskeletal imaging: a narrative review. Eur Radiol Exp 2024; 8:22. [PMID: 38355767 PMCID: PMC10866817 DOI: 10.1186/s41747-024-00422-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 02/16/2024] Open
Abstract
This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.
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Affiliation(s)
- Salvatore Gitto
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Francesca Serpi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Giovanni Risoleo
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
| | - Stefano Fusco
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Carmelo Messina
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Luca Maria Sconfienza
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
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Walker FO, Kremkau F. Latency and distance. Muscle Nerve 2024; 69:131-133. [PMID: 38126477 DOI: 10.1002/mus.28024] [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: 11/07/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023]
Abstract
See article on pages 148–156 in this issue.
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Affiliation(s)
- Francis O Walker
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Frederick Kremkau
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Ritsche P, Franchi MV, Faude O, Finni T, Seynnes O, Cronin NJ. Fully Automated Analysis of Muscle Architecture from B-Mode Ultrasound Images with DL_Track_US. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:258-267. [PMID: 38007322 DOI: 10.1016/j.ultrasmedbio.2023.10.011] [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: 06/13/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/27/2023]
Abstract
OBJECTIVE B-mode ultrasound can be used to image musculoskeletal tissues, but one major bottleneck is analyses of muscle architectural parameters (i.e., muscle thickness, pennation angle and fascicle length), which are most often performed manually. METHODS In this study we trained two different neural networks (classic U-Net and U-Net with VGG16 pre-trained encoder) to detect muscle fascicles and aponeuroses using a set of labeled musculoskeletal ultrasound images. We determined the best-performing model based on intersection over union and loss metrics. We then compared neural network predictions on an unseen test set with those obtained via manual analysis and two existing semi/automated analysis approaches (simple muscle architecture analysis [SMA] and UltraTrack). DL_Track_US detects the locations of the superficial and deep aponeuroses, as well as multiple fascicle fragments per image. RESULTS For single images, DL_Track_US yielded results similar to those produced by a non-trainable automated method (SMA; mean difference in fascicle length: 5.1 mm) and human manual analysis (mean difference: -2.4 mm). Between-method differences in pennation angle were within 1.5°, and mean differences in muscle thickness were less than 1 mm. Similarly, for videos, there was overlap between the results produced with UltraTrack and DL_Track_US, with intraclass correlations ranging between 0.19 and 0.88. CONCLUSION DL_Track_US is fully automated and open source and can estimate fascicle length, pennation angle and muscle thickness from single images or videos, as well as from multiple superficial muscles. We also provide a user interface and all necessary code and training data for custom model development.
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Affiliation(s)
- Paul Ritsche
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland.
| | - Martino V Franchi
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Oliver Faude
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Taija Finni
- Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland
| | - Olivier Seynnes
- Department for Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Neil J Cronin
- Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland; School of Sport & Exercise, University of Gloucestershire, Gloucester, UK
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Hammam N, Tharwat S, M Elsaman A, Bakhiet A, Mahmoud MB, Ismail F, El Saadany H, R ElShereef R, F Mohamed E, I Abd Elazeem M, Eid A, Ali F, Hamdy M, El Mallah R, Ha Mohammed R, M Gamal R, Fawzy S, Senara S, Hammam O, M Fathi H, Aboul Fotouh A, A Gheita T. Unsupervised cluster analysis of clinical and ultrasound features reveals unique gout subtypes: Results from the Egyptian College of Rheumatology (ECR). Diabetes Metab Syndr 2023; 17:102897. [PMID: 37979221 DOI: 10.1016/j.dsx.2023.102897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/25/2023] [Accepted: 10/20/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND Gout comprises a heterogeneous group of disorders; however, comorbidities have been the focus of most efforts to classify disease subgroups. OBJECTIVES We applied cluster analysis using musculoskeletal ultrasound (MSUS) combined with clinical and laboratory findings in patients with gout to identify disease phenotypes, and differences across clusters were investigated. PATIENTS AND METHODS Patients with gout who complied with the ACR/EULAR classification criteria were enrolled in the Egyptian College of Rheumatology (ECR)-MSUS Study Group, a multicenter study. Selected variables included demographic, clinical, and laboratory findings. MSUS scans assessed the bilateral knee and first metatarsophalangeal joints. We performed a K-mean cluster analysis and compared the features of each cluster. RESULTS 425 patients, 267 (62.8 %) males, mean age 54.2 ± 10.3 years were included. Three distinct clusters were identified. Cluster 1 (n = 138, 32.5 %) has the lowest burden of the disease and a lower frequency of MSUS characteristics than the other clusters. Cluster 2 (n = 140, 32.9 %) was mostly women, with a low rate of urate-lowering treatment (ULT). Cluster 3 (n = 147, 34.6 %) has the highest disease burden and the greatest proportion of comorbidities. Significant MSUS variations were found between clusters 2 and 3: joint effusion (p < 0.0001; highest: cluster 3), power Doppler signal (p < 0.0001; highest: clusters 2), and aggregates of crystal deposition (p < 0.0001; highest: cluster 3). CONCLUSION Cluster analysis using MSUS findings identified three gout subgroups. People with more MSUS features were more likely to receive ULT. Treatment should be tailored according to the cluster and MSUS features.
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Affiliation(s)
- Nevin Hammam
- Rheumatology Department, Faculty of Medicine, Assiut University, Egypt.
| | - Samar Tharwat
- Internal Medicine, Rheumatology Unit, Mansoura University, Egypt
| | - Ahmed M Elsaman
- Rheumatology Department, Faculty of Medicine, Sohag University, Egypt
| | - Ali Bakhiet
- Computer Science Department, Higher Institute of Computer Science and Information Systems, Culture & Science City, Giza, Egypt
| | - Mohamed Bakrey Mahmoud
- Computer Science Department, Higher Institute of Computer Science and Information Systems, Culture & Science City, Giza, Egypt
| | - Faten Ismail
- Rheumatology Department, Faculty of Medicine, Minia University, Egypt
| | | | | | - Eman F Mohamed
- Internal Medicine Department, Rheumatology Unit, Faculty of Medicine (Girls), Al-Azhar University, Egypt
| | | | - Ayman Eid
- Rheumatology Department, Faculty of Medicine, Beni-Suef University, Egypt
| | - Fatma Ali
- Rheumatology Department, Faculty of Medicine, Minia University, Egypt
| | - Mona Hamdy
- Rheumatology Department, Faculty of Medicine, Minia University, Egypt
| | - Reem El Mallah
- Rheumatology Department, Faculty of Medicine, Ain Shams University, Egypt
| | - Reem Ha Mohammed
- Rheumatology Department, Faculty of Medicine, Cairo University, Egypt
| | - Rania M Gamal
- Rheumatology Department, Faculty of Medicine, Assiut University, Egypt
| | - Samar Fawzy
- Rheumatology Department, Faculty of Medicine, Cairo University, Egypt
| | - Soha Senara
- Rheumatology Department, Faculty of Medicine, Fayoum University, Egypt
| | - Osman Hammam
- Department of Rheumatology and Rehabilitation, Faculty of Medicine, New Valley University, New Valley, Egypt
| | - Hanan M Fathi
- Rheumatology Department, Faculty of Medicine, Fayoum University, Egypt
| | - Adham Aboul Fotouh
- Egyptian School for Musculoskeletal Ultrasonography (EgySMUS), Egyptian Society of Musculoskeletal and Neuromuscular Sonography (ESMNS), Egypt
| | - Tamer A Gheita
- Rheumatology Department, Faculty of Medicine, Cairo University, Egypt
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15
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Lin TM, Lee HY, Chang CK, Lin KH, Chang CC, Wu BF, Peng SJ. Identification of tophi in ultrasound imaging based on transfer learning and clinical practice. Sci Rep 2023; 13:12507. [PMID: 37532752 PMCID: PMC10397312 DOI: 10.1038/s41598-023-39508-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023] Open
Abstract
Gout is a common metabolic disorder characterized by deposits of monosodium urate monohydrate crystals (tophi) in soft tissue, triggering intense and acute arthritis with intolerable pain as well as articular and periarticular inflammation. Tophi can also promote chronic inflammatory and erosive arthritis. 2015 ACR/EULAR Gout Classification criteria include clinical, laboratory, and imaging findings, where cases of gout are indicated by a threshold score of ≥ 8. Some imaging-related findings, such as a double contour sign in ultrasound, urate in dual-energy computed tomography, or radiographic gout-related erosion, generate a score of up to 4. Clearly, the diagnosis of gout is largely assisted by imaging findings; however, dual-energy computed tomography is expensive and exposes the patient to high levels of radiation. Although musculoskeletal ultrasound is non-invasive and inexpensive, the reliability of the results depends on expert experience. In the current study, we applied transfer learning to train a convolutional neural network for the identification of tophi in ultrasound images. The accuracy of predictions varied with the convolutional neural network model, as follows: InceptionV3 (0.871 ± 0.020), ResNet101 (0.913 ± 0.015), and VGG19 (0.918 ± 0.020). The sensitivity was as follows: InceptionV3 (0.507 ± 0.060), ResNet101 (0.680 ± 0.056), and VGG19 (0.747 ± 0.056). The precision was as follows: InceptionV3 (0.767 ± 0.091), ResNet101 (0.863 ± 0.098), and VGG19 (0.825 ± 0.062). Our results demonstrate that it is possible to retrain deep convolutional neural networks to identify the patterns of tophi in ultrasound images with a high degree of accuracy.
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Affiliation(s)
- Tzu-Min Lin
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Hsiang-Yen Lee
- Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Ching-Kuei Chang
- Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Ke-Hung Lin
- Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chi-Ching Chang
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Bing-Fei Wu
- Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing St., Xinyi Dist., Taipei City, 110, Taiwan.
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
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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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Shao J, Zhou K, Cai YH, Geng DY. Application of an Improved U2-Net Model in Ultrasound Median Neural Image Segmentation. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2512-2520. [PMID: 36167742 DOI: 10.1016/j.ultrasmedbio.2022.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
To investigate whether an improved U2-Net model could be used to segment the median nerve and improve segmentation performance, we performed a retrospective study with 402 nerve images from patients who visited Huashan Hospital from October 2018 to July 2020; 249 images were from patients with carpal tunnel syndrome, and 153 were from healthy volunteers. From these, 320 cases were selected as training sets, and 82 cases were selected as test sets. The improved U2-Net model was used to segment each image. Dice coefficients (Dice), pixel accuracy (PA), mean intersection over union (MIoU) and average Hausdorff distance (AVD) were used to evaluate segmentation performance. Results revealed that the Dice, MIoU, PA and AVD values of our improved U2-Net were 72.85%, 79.66%, 95.92% and 51.37 mm, respectively, which were comparable to the actual ground truth; the ground truth came from the labeling of clinicians. However, the Dice, MIoU, PA and AVD values of U-Net were 43.19%, 65.57%, 86.22% and 74.82 mm, and those of Res-U-Net were 58.65%, 72.53%, 88.98% and 57.30 mm. Overall, our data suggest our improved U2-Net model might be used for segmentation of ultrasound median neural images.
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Affiliation(s)
- Jie Shao
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Ye-Hua Cai
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Dao-Ying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China; Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China.
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18
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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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19
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Ghasseminia S, Lim AKS, Concepcion NDP, Kirschner D, Teo YM, Dulai S, Mabee M, Kernick S, Brockley C, Muljadi S, Singh P, Rakkunedeth Hareendranathan A, Kapur J, Zonoobi D, Punithakumar K, Jaremko JL. Interobserver Variability of Hip Dysplasia Indices on Sweep Ultrasound for Novices, Experts, and Artificial Intelligence. J Pediatr Orthop 2022; 42:e315-e323. [PMID: 35125417 DOI: 10.1097/bpo.0000000000002065] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Ultrasound for developmental dysplasia of the hip (DDH) is challenging for nonexperts to perform and interpret. Recording "sweep" images allows more complete hip assessment, suitable for automation by artificial intelligence (AI), but reliability has not been established. We assessed agreement between readers of varying experience and a commercial AI algorithm, in DDH detection from infant hip ultrasound sweeps. METHODS We selected a full spectrum of poor-to-excellent quality images and normal to severe dysplasia, in 240 hips (120 single 2-dimensional images, 120 sweeps). For 12 readers (radiologists, sonographers, clinicians and researchers; 3 were DDH subspecialists), and a ultrasound-FDA-cleared AI software package (Medo Hip), we calculated interobserver reliability for alpha angle measurements by intraclass correlation coefficient (ICC2,1) and for DDH classification by Randolph Kappa. RESULTS Alpha angle reliability was high for AI versus subspecialists (ICC=0.87 for sweeps, 0.90 for single images). For DDH diagnosis from sweeps, agreement was high between subspecialists (kappa=0.72), and moderate for nonsubspecialists (0.54) and AI (0.47). Agreement was higher for single images (kappa=0.80, 0.66, 0.49). AI reliability deteriorated more than human readers for the poorest-quality images. The agreement of radiologists and clinicians with the accepted standard, while still high, was significantly poorer for sweeps than 2D images (P<0.05). CONCLUSIONS In a challenging exercise representing the wide spectrum of image quality and reader experience seen in real-world hip ultrasound, agreement on DDH diagnosis from easily obtained sweeps was only slightly lower than from single images, likely because of the additional step of selecting the best image. AI performed similarly to a nonsubspecialist human reader but was more affected by low-quality images.
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Affiliation(s)
| | - Andrew Kean Seng Lim
- Department of Orthopaedic Surgery, University Orthopaedics, Hand and Reconstructive Microsurgery Cluster, National University Health System
| | | | | | - Yi Ming Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Sukhdeep Dulai
- Surgery, Faculty of Medicine and Dentistry, University of Alberta
| | - Myles Mabee
- University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Sara Kernick
- Department of Medical Imaging, Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Cain Brockley
- Department of Medical Imaging, Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Siska Muljadi
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Pavel Singh
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | | | - Jeevesh Kapur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
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20
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Martín-Noguerol T, Barousse R, Luna A, Socolovsky M, Górriz JM, Gómez-Río M. New insights into the evaluation of peripheral nerves lesions: a survival guide for beginners. Neuroradiology 2022; 64:875-886. [PMID: 35212785 DOI: 10.1007/s00234-022-02916-x] [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: 11/11/2021] [Accepted: 02/09/2022] [Indexed: 12/09/2022]
Abstract
PURPOSE To perform a review of the physical basis of DTI and DCE-MRI applied to Peripheral Nerves (PNs) evaluation with the aim of providing readers the main concepts and tools to acquire these types of sequences for PNs assessment. The potential added value of these advanced techniques for pre-and post-surgical PN assessment is also reviewed in diverse clinical scenarios. Finally, a brief introduction to the promising applications of Artificial Intelligence (AI) for PNs evaluation is presented. METHODS We review the existing literature and analyze the latest evidence regarding DTI, DCE-MRI and AI for PNs assessment. This review is focused on a practical approach to these advanced sequences providing tips and tricks for implementing them into real clinical practice focused on imaging postprocessing and their current clinical applicability. A summary of the potential applications of AI algorithms for PNs assessment is also included. RESULTS DTI, successfully used in central nervous system, can also be applied for PNs assessment. DCE-MRI can help evaluate PN's vascularization and integrity of Blood Nerve Barrier beyond the conventional gadolinium-enhanced MRI sequences approach. Both approaches have been tested for PN assessment including pre- and post-surgical evaluation of PNs and tumoral conditions. AI algorithms may help radiologists for PN detection, segmentation and characterization with promising initial results. CONCLUSION DTI, DCE-MRI are feasible tools for the assessment of PN lesions. This manuscript emphasizes the technical adjustments necessary to acquire and post-process these images. AI algorithms can also be considered as an alternative and promising choice for PN evaluation with promising results.
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Affiliation(s)
| | - Rafael Barousse
- Peripheral Nerve and Plexus Department, Centro Rossi, Sánchez de Loria 117, C1173 AAC, Buenos Aires, Argentina
| | - Antonio Luna
- MRI unit, Radiology Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain
| | - Mariano Socolovsky
- Nerve & Plexus Surgery Program, Division of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, Paraguay 2155, C1121 ABG, Buenos Aires, Argentina
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Avenida de Fuente Nueva, s/n, 18071, Granada, Spain.,Department of Psychiatry, University of Cambridge, Cambridge, CB21TN, UK
| | - Manuel Gómez-Río
- Department of Nuclear Medicine, Virgen de las Nieves University Hospital, Av. de las Fuerzas Armadas, 2, 18014, Granada, Spain.,IBS Granada Bio-Health Research Institute, Av. de Madrid, 15, 18012, Granada, Spain
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21
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Wu CH, Chiu PH, Boudier-Revret M, Chang SW, Chen WS, zakar L. Deep learning for detecting supraspinatus calcific tendinopathy on ultrasound images. J Med Ultrasound 2022; 30:196-202. [DOI: 10.4103/jmu.jmu_182_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 11/04/2022] Open
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22
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Erdem G, Ermiş Y, Özkan D. Peripheral nerve blocks and the use of artificial intelligence-assisted ultrasonography. J Clin Anesth 2021; 78:110597. [PMID: 34903443 DOI: 10.1016/j.jclinane.2021.110597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/18/2022]
Affiliation(s)
- Gökhan Erdem
- University of Health Sciences, Dışkapı Yıldırım Beyazıt Training and Research Hospital, Anesthesiology and Reanimation Clinic, Ankara, Turkey.
| | - Yasemin Ermiş
- University of Health Sciences, Dışkapı Yıldırım Beyazıt Training and Research Hospital, Anesthesiology and Reanimation Clinic, Ankara, Turkey
| | - Derya Özkan
- University of Health Sciences, Dışkapı Yıldırım Beyazıt Training and Research Hospital, Anesthesiology and Reanimation Clinic, Ankara, Turkey
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Ajmera P, Kharat A, Botchu R, Gupta H, Kulkarni V. Real-world analysis of artificial intelligence in musculoskeletal trauma. J Clin Orthop Trauma 2021; 22:101573. [PMID: 34527511 PMCID: PMC8427222 DOI: 10.1016/j.jcot.2021.101573] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 11/30/2022] Open
Abstract
Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room. Musculoskeletal trauma results in expenditure of billions of dollars and protracted losses of quality-adjusted life years. New and innovative methods are needed to minimise the impact by ensuring quick and accurate assessment. However, each of the currently utilised radiological procedures, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging, has resulted in implosion of medical imaging data. Deep learning, a recent advancement in artificial intelligence, has demonstrated the potential to analyse medical images with sensitivity and specificity at par with experts. In this review article, we intend to summarise and showcase the various developments which have occurred in the dynamic field of artificial intelligence and machine learning and how their applicability to different aspects of imaging in trauma can be explored to improvise our existing reporting systems and improvise on patient outcomes.
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Affiliation(s)
- Pranav Ajmera
- Department of Radiology, Dr D.Y. Patil Medical College, Hospital and Research Center, DPU, Pune, India
| | - Amit Kharat
- Department of Radiology, Dr D.Y. Patil Medical College, Hospital and Research Center, DPU, Pune, India
| | - Rajesh Botchu
- Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, UK
| | - Harun Gupta
- Department of Musculoskeletal Radiology, Leeds Teaching Hospitals, Leeds, UK
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Marzola F, van Alfen N, Doorduin J, Meiburger KM. Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment. Comput Biol Med 2021; 135:104623. [PMID: 34252683 DOI: 10.1016/j.compbiomed.2021.104623] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/14/2021] [Accepted: 06/28/2021] [Indexed: 12/18/2022]
Abstract
Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing the study of large datasets requiring less human interaction. The purpose of this study is to present a deep learning algorithm for the cross-sectional area (CSA) segmentation in transverse musculoskeletal ultrasound images, providing a quantitative grayscale analysis which is useful for studying muscles, and to validate the results in a large dataset. The dataset included 3917 images of biceps brachii, tibialis anterior and gastrocnemius medialis acquired on 1283 subjects (mean age 50 ± 21 years, 729 male). The algorithm was based on multiple deep-learning architectures, and its performance was compared to a manual expert segmentation. We compared the mean grayscale value inside the automatic and manual CSA using Bland-Altman plots and a correlation analysis. Classification in healthy and abnormal muscles between automatic and manual segmentation were compared using the grayscale value z-scores. In the test set, a Precision of 0.88 ± 0.12 and a Recall of 0.92 ± 0.09 was achieved. The network segmentation performance was slightly less in abnormal muscles, without a loss of discrimination between healthy and abnormal muscle images. Bland-Altman plots showed no clear trend in the error distribution and the two readings have a 0.99 Pearson's correlation coefficient (p < 0.001, test set). The ICC(A, 1) calculated between the z-score readings was 0.99. The algorithm achieves robust CSA segmentation performance and gives mean grayscale level information comparable to a manual operator. This could provide a helpful tool for clinicians in neuromuscular disease diagnosis and follow-up. The entire dataset and code are made available for the research community.
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Affiliation(s)
- Francesco Marzola
- Biolab, Polito(BIO)MedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Nens van Alfen
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jonne Doorduin
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Kristen M Meiburger
- Biolab, Polito(BIO)MedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
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Kubicek J, Strycek M, Cerny M, Penhaker M, Prokop O, Vilimek D. Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System. SENSORS 2021; 21:s21124161. [PMID: 34204477 PMCID: PMC8233799 DOI: 10.3390/s21124161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/06/2021] [Accepted: 06/11/2021] [Indexed: 12/27/2022]
Abstract
In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.
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Affiliation(s)
- Jan Kubicek
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
- Correspondence:
| | - Michal Strycek
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
| | - Martin Cerny
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
| | - Marek Penhaker
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
| | - Ondrej Prokop
- MEDIN, a.s., Vlachovicka 619, 59231 Nove Mesto na Morave, Czech Republic;
| | - Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava Poruba, Czech Republic; (M.S.); (M.C.); (M.P.); (D.V.)
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26
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Liao AH, Chen JR, Liu SH, Lu CH, Lin CW, Shieh JY, Weng WC, Tsui PH. Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy. Diagnostics (Basel) 2021; 11:diagnostics11060963. [PMID: 34071811 PMCID: PMC8228495 DOI: 10.3390/diagnostics11060963] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16TL, VGG-19, and VGG-19TL models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.
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Affiliation(s)
- Ai-Ho Liao
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan; (A.-H.L.); (S.-H.L.)
- Department of Biomedical Engineering, National Defense Medical Center, Taipei 114201, Taiwan
| | - Jheng-Ru Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (C.-H.L.)
| | - Shi-Hong Liu
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan; (A.-H.L.); (S.-H.L.)
| | - Chun-Hao Lu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (C.-H.L.)
| | - Chia-Wei Lin
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu 300195, Taiwan;
| | - Jeng-Yi Shieh
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei 100225, Taiwan;
| | - Wen-Chin Weng
- Department of Pediatrics, National Taiwan University Hospital, Taipei 100225, Taiwan
- Department of Pediatric Neurology, National Taiwan University Children’s Hospital, Taipei 100226, Taiwan
- Department of Pediatrics, College of Medicine, National Taiwan University, Taipei 100233, Taiwan
- Correspondence: (W.-C.W.); (P.-H.T.)
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (C.-H.L.)
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Linkou, Taoyuan 333323, Taiwan
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Correspondence: (W.-C.W.); (P.-H.T.)
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27
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Yaoting WMD, Huihui CMD, Ruizhong YMD, Jingzhi LMDP, Ji-Bin LMD, Chen L, Chengzhong PMD. Point-of-Care Ultrasound: New Concepts and Future Trends. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2021. [DOI: 10.37015/audt.2021.210023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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28
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Park SH. Artificial intelligence for ultrasonography: unique opportunities and challenges. Ultrasonography 2021; 40:3-6. [PMID: 33227844 PMCID: PMC7758099 DOI: 10.14366/usg.20078] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/31/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
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