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Hussein A, Youssef S, Ahmed MA, Ghatwary N. MGB-Unet: An Improved Multiscale Unet with Bottleneck Transformer for Myositis Segmentation from Ultrasound Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01168-w. [PMID: 39037670 DOI: 10.1007/s10278-024-01168-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/15/2024] [Accepted: 05/28/2024] [Indexed: 07/23/2024]
Abstract
Myositis is the inflammation of the muscles that can arise from various sources with diverse symptoms and require different treatments. For treatment to achieve optimal results, it is essential to obtain an accurate diagnosis promptly. This paper presents a new supervised segmentation architecture that can efficiently perform precise segmentation and classification of myositis from ultrasound images with few computational resources. The architecture of our model includes a unique encoder-decoder structure that integrates the Bottleneck Transformer (BOT) with a newly developed Residual block named Multi-Conv Ghost switchable bottleneck Residual Block (MCG_RB). This block effectively captures and analyzes ultrasound image input inside the encoder segment at several resolutions. Moreover, the BOT module is a transformer-style attention module designed to bridge the feature gap between the encoding and decoding stages. Furthermore, multi-level features are retrieved using the MCG-RB module, which combines multi-convolution with ghost switchable residual connections of convolutions for both the encoding and decoding stages. The suggested method attains state-of-the-art performance on a benchmark set of myositis ultrasound images across all parameters, including accuracy, precision, recall, dice coefficient, and Jaccard index. Despite its limited training data, the suggested approach demonstrates remarkable generalizability by yielding exceptional results. The proposed model showed a substantial enhancement in accuracy when compared to segmentation state-of-the-art methods such as Unet++, DeepLabV3, and the Duck-Net. The dice coefficient and Jaccard index obtained improvements of up to 3%, 6%, and 7%, respectively, surpassing the other methods.
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Affiliation(s)
- Allaa Hussein
- Computer Engineering, Pharos University, Pharos, Egypt.
| | - Sherin Youssef
- Computer Engineering, Arab Academy for Science and Technology, Alexandria, Egypt
| | - Magdy A Ahmed
- Computer Engineering, Faculty of Engineering, Alexandria, Egypt
| | - Noha Ghatwary
- Computer Engineering, Arab Academy for Science and Technology, Alexandria, Egypt
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2
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Ravipati A, Elman SA. The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more. Clin Dermatol 2024:S0738-081X(24)00103-2. [PMID: 38909858 DOI: 10.1016/j.clindermatol.2024.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. Although not as widely known, AI programs have also been utilized as diagnostic and prognostic tools for dermatologic conditions with systemic or extracutaneous involvement, especially for diseases with autoimmune etiologies. We have provided a primer on commonly used AI platforms and the practical applicability of these algorithms in dealing with psoriasis, systemic sclerosis, and dermatomyositis as a microcosm for future directions in the field. With a rapidly changing landscape in dermatology and medicine as a whole, AI could be a versatile tool to support clinicians and enhance access to care.
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Affiliation(s)
- Advaitaa Ravipati
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Scott A Elman
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA.
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Vincenten SCC, Teeselink S, Voermans NC, van Engelen BGM, Mul K, van Alfen N. Establishing the role of muscle ultrasound as an imaging biomarker in facioscapulohumeral muscular dystrophy. Neuromuscul Disord 2023; 33:936-944. [PMID: 37968164 DOI: 10.1016/j.nmd.2023.10.015] [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] [Received: 05/31/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/17/2023]
Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is a hereditary muscle disease, that causes weakness and wasting of skeletal muscles. In this cross-sectional cohort-study on FSHD patients, we assessed muscle ultrasound findings and their relation to clinical outcome measures, evaluating the role of ultrasound as biomarker in FSHD. We included 115 genetically confirmed FSHD patients (52% males, age-range 22-80 years). They were subjected to a standardized muscle ultrasound protocol of seven truncal and upper- and lower extremity muscles bilaterally. Muscle images were scored using the Heckmatt scale. Muscle echogenicity was quantified using z-scores. Compound echogenicity and Heckmatt scores were calculated. Nearly all patients (94%) had one or multiple muscles with an increased echogenicity z-score. The trapezius muscle was most severely affected, followed by the rectus femoris muscle. Both compound ultrasound scores strongly with multiple clinical outcome measures (ρ 0.68-0.79, p < 0.001). While most muscles showed a high level of agreement between the echogenicity z-score and Heckmatt score (>95%), the tibialis anterior and gastrocnemius muscle showed lower levels of agreement (82 and 92%). In conclusion, our study confirms the use of muscle ultrasound as clinical severity biomarker and provides a solid base for future longitudinal studies to establish ultrasound as a monitoring biomarker in FSHD.
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Affiliation(s)
- S C C Vincenten
- Radboud University Medical Center, Clinical Neuromuscular Imaging Group, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - S Teeselink
- Radboud University Medical Center, Clinical Neuromuscular Imaging Group, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - N C Voermans
- Radboud University Medical Center, Clinical Neuromuscular Imaging Group, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - B G M van Engelen
- Radboud University Medical Center, Clinical Neuromuscular Imaging Group, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - K Mul
- Radboud University Medical Center, Clinical Neuromuscular Imaging Group, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - N van Alfen
- Radboud University Medical Center, Clinical Neuromuscular Imaging Group, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
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Piñeros-Fernández MC. Artificial Intelligence Applications in the Diagnosis of Neuromuscular Diseases: A Narrative Review. Cureus 2023; 15:e48458. [PMID: 37942130 PMCID: PMC10629626 DOI: 10.7759/cureus.48458] [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] [Accepted: 11/07/2023] [Indexed: 11/10/2023] Open
Abstract
The accurate diagnosis of neuromuscular diseases (NMD) is in many cases difficult; the starting point is the clinical approach based on the course of the disease and a careful physical examination of the patient. Electrodiagnostic tests, imaging, muscle biopsy, and genetics are fundamental complementary studies for the diagnosis of NMD. The large volume of data obtained from such studies makes it necessary to look for efficient solutions, such as artificial intelligence (AI) applications, which can help classify, synthesize, and organize the information of patients with NMD to facilitate their accurate and timely diagnosis. The objective of this study was to describe the usefulness of AI applications in the diagnosis of patients with neuromuscular diseases. A narrative review was done, including publications on artificial intelligence applied to the diagnostic methods of NMD currently existing. Twelve studies were included. Two of the studies focused on muscle ultrasound, five of the studies on muscle MRI, two studies on electromyography, two studies on amyotrophic lateral sclerosis (ALS) biomarkers, and one study on genes related to myopathies. The accuracy of classification using different classification algorithms used in each of the studies included in this narrative review was already 90% in most studies. In conclusion, the future design of more accurate algorithms applied to NMD with greater precision will have an impact on the earlier diagnosis of this group of diseases.
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Affiliation(s)
- Martha C Piñeros-Fernández
- Pediatric Neurology, Fundación Cardio Infantil - La Cardio, Bogotá, COL
- Pediatric Neurology, Cobos Medical Center, Bogotá, COL
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Tan AL, Di Matteo A, Wakefield RJ, Biglands J. Update on muscle imaging in myositis. Curr Opin Rheumatol 2023; 35:395-403. [PMID: 37656661 PMCID: PMC10552815 DOI: 10.1097/bor.0000000000000975] [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: 09/03/2023]
Abstract
PURPOSE OF REVIEW Imaging techniques such as MRI, ultrasound and PET/computed tomography (CT) have roles in the detection, diagnosis and management of myositis or idiopathic inflammatory myopathy (IIM). Imaging research has also provided valuable knowledge in the understanding of the pathology of IIM. This review explores the latest advancements of these imaging modalities in IIM. RECENT FINDINGS Recent advancements in imaging of IIM have seen a shift away from manual and qualitative analysis of the images. Quantitative MRI provides more objective, and potentially more sensitive characterization of fat infiltration and inflammation in muscles. In addition to B-mode ultrasound changes, shearwave elastography offers a new dimension to investigating IIM. PET/CT has the added advantage of including IIM-associated findings such as malignancies. SUMMARY It is evident that MRI, ultrasound and PET/CT have important roles in myositis. Continued technological advancement and a quest for more sophisticated applications help drive innovation; this has especially been so of machine learning/deep learning using artificial intelligence and the developing promise of texture analysis.
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Affiliation(s)
- Ai Lyn Tan
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds
- NIHR Leeds Biomedical Research Centre, Chapel Allerton Hospital
| | - Andrea Di Matteo
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds
- NIHR Leeds Biomedical Research Centre, Chapel Allerton Hospital
| | - Richard J. Wakefield
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds
- NIHR Leeds Biomedical Research Centre, Chapel Allerton Hospital
| | - John Biglands
- NIHR Leeds Biomedical Research Centre, Chapel Allerton Hospital
- Department of Medical Physics & Engineering, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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Mirón-Mombiela R, Ruiz-España S, Moratal D, Borrás C. Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques. Mech Ageing Dev 2023; 215:111860. [PMID: 37666473 DOI: 10.1016/j.mad.2023.111860] [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: 04/25/2023] [Revised: 08/08/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 ≤ AUC ≤ 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 ≤ p ≤ 0.18) and were predictive of death for pre-frail and frail participants (0.001 ≤ p ≤ 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.
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Affiliation(s)
- Rebeca Mirón-Mombiela
- Department of Physiology, Universitat de València/INCLIVA, Avda. Blasco Ibáñez, 15, 46010 Valencia, Spain; Hospital General Universitario de Valencia (HGUV), Valencia, Spain; Herlev og Gentofte Hospital, Herlev, Denmark.
| | - Silvia Ruiz-España
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
| | - Consuelo Borrás
- Department of Physiology, Universitat de València/INCLIVA, Avda. Blasco Ibáñez, 15, 46010 Valencia, Spain; INCLIVA Health Research Institute, Av/ de Menéndez y Pelayo, 4, 46010 Valencia, Spain; Center for Biomedical Network Research on Frailty and Healthy Aging (CIBERFES), CIBER-ISCIII, Valencia, Spain.
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Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023; 11:887. [PMID: 36979866 PMCID: PMC10045927 DOI: 10.3390/biomedicines11030887] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023] Open
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
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Affiliation(s)
- Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Bianca Roncaglia
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
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Lupi A, Spolaor S, Favero A, Bello L, Stramare R, Pegoraro E, Nobile MS. Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning. PLoS One 2023; 18:e0285422. [PMID: 37155641 PMCID: PMC10166478 DOI: 10.1371/journal.pone.0285422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/21/2023] [Indexed: 05/10/2023] Open
Abstract
PURPOSE Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). METHODS MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. RESULTS Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. CONCLUSION Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.
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Affiliation(s)
- Amalia Lupi
- Institute of Radiology, Department of Medicine-DIMED, University of Padua, Padua, Italy
| | - Simone Spolaor
- Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Alessandro Favero
- Institute of Radiology, Department of Medicine-DIMED, University of Padua, Padua, Italy
| | - Luca Bello
- Department of Neurosciences, University of Padua, Padua, Italy
| | - Roberto Stramare
- Clinical and Translational Advanced Imaging Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy
| | - Elena Pegoraro
- Department of Neurosciences, University of Padua, Padua, Italy
| | - Marco Salvatore Nobile
- Department of Environmental Sciences, Informatics and Statistics (DAIS), Ca' Foscari University of Venice, Venice, Italy
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Danieli MG, Tonacci A, Paladini A, Longhi E, Moroncini G, Allegra A, Sansone F, Gangemi S. A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases. Autoimmun Rev 2022; 21:103105. [PMID: 35452850 DOI: 10.1016/j.autrev.2022.103105] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To evaluate the response to treatment with intravenous (IVIg) and subcutaneous (20%SCIg) immunoglobulin in our series of patients with Inflammatory idiopathic myopathies (IIM) by the means of artificial intelligence. BACKGROUND IIM are rare diseases mainly involving the skeletal muscle with particular clinical, laboratory and radiological characteristics. Artificial intelligence (AI) represents computer processes which allows to perform complex calculations and data analyses, with the least human intervention. Recently, the use an AI in medicine significantly expanded, especially through machine learning (ML) which analyses huge amounts of information and accordingly makes decisions, and deep learning (DL) which uses artificial neural networks to analyse data and automatically learn. METHODS In this study, we employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in our series of patients with IIM. The diagnoses were determined on the established EULAR/ACR criteria. The treatment response was evaluated employing the following: serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score) and disability (HAQ-DI score). We evaluated all the above parameters, applying, with R, different supervised ML algorithms, including Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net, Classification and Regression Trees and Random Forest to estimate the most important predictors for a good response to IVIg and 20%SCIg treatment. RESULTS AND CONCLUSION By the means of AI we have been able to identify the scores that best predict a good response to IVIg and 20%SCIg treatment. The muscle strength as evaluated by MMT8 score at the follow-up is predicted by the presence of dysphagia and of skin disorders, and the myositis activity index (MITAX) at the beginning of the treatment. The relationship between muscle strength and MITAX indicates a better action of IVIg therapy in patients with more active systemic disease. Considering our results, Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX at most) in myositis.
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Affiliation(s)
- Maria Giovanna Danieli
- Clinica Medica, Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy.
| | - Alessandro Tonacci
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), Via G. Moruzzi 1, 56124 Pisa, Italy
| | - Alberto Paladini
- PostGraduate School of Internal Medicine, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Eleonora Longhi
- Scuola di Medicina e Chirurgia, Alma Mater Studiorum, Università degli Studi di Bologna, 40126 Bologna, Italy
| | - Gianluca Moroncini
- Clinica Medica, Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; PostGraduate School of Internal Medicine, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Alessandro Allegra
- Division of Haematology, Department of Human Pathology in Adulthood and Childhood "Gaetano Barresi", University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Francesco Sansone
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), Via G. Moruzzi 1, 56124 Pisa, Italy
| | - Sebastiano Gangemi
- School and Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy.
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Classification of myositis from muscle ultrasound images using deep learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.
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Abstract
Purpose of Review The purpose of this review is to critically discuss the use of ultrasound in the evaluation of muscle disorders with a particular focus on the emerging use in inflammatory myopathies. Recent Findings In myopathies, pathologic muscle shows an increase in echogenicity. Muscle echogenicity can be assessed visually, semi-quantitatively, or quantitatively using grayscale analysis. The involvement of specific muscle groups and the pattern of increase in echogenicity can further point to specific diseases. In pediatric neuromuscular disorders, the value of muscle ultrasound for screening and diagnosis is well-established. It has also been found to be a responsive measure of disease change in muscular dystrophies. In chronic forms of myositis like inclusion body myositis, ultrasound is very suitable for detecting markedly increased echogenicity and atrophy in affected muscles. Acute cases of muscle edema show only a mild increase in echogenicity, which can also reverse with successful treatment. Summary Muscle ultrasound is an important imaging modality that is highly adaptable to study various muscle conditions. Although its diagnostic value for neuromuscular disorders is high, the evidence in myositis has only begun to accrue in earnest. Further systematic studies are needed, especially in its role for detecting muscle edema.
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