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Aringhieri G, Astrea G, Marfisi D, Fanni SC, Marinella G, Pasquariello R, Ricci G, Sansone F, Sperti M, Tonacci A, Torri F, Matà S, Siciliano G, Neri E, Santorelli FM, Conte R. Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients. J Funct Morphol Kinesiol 2024; 9:123. [PMID: 39051284 PMCID: PMC11270263 DOI: 10.3390/jfmk9030123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
We aim to develop a deep learning-based algorithm for automated segmentation of thigh muscles and subcutaneous adipose tissue (SAT) from T1-weighted muscle MRIs from patients affected by muscular dystrophies (MDs). From March 2019 to February 2022, adult and pediatric patients affected by MDs were enrolled from Azienda Ospedaliera Universitaria Pisana, Pisa, Italy (Institution 1) and the IRCCS Stella Maris Foundation, Calambrone-Pisa, Italy (Institution 2), respectively. All patients underwent a bilateral thighs MRI including an axial T1 weighted in- and out-of-phase (dual-echo). Both muscles and SAT were manually and separately segmented on out-of-phase image sets by a radiologist with 6 years of experience in musculoskeletal imaging. A U-Net1 and U-Net3 were built to automatically segment the SAT, all the thigh muscles together and the three muscular compartments separately. The dataset was randomly split into the on train, validation, and test set. The segmentation performance was assessed through the Dice similarity coefficient (DSC). The final cohort included 23 patients. The estimated DSC for U-Net1 was 96.8%, 95.3%, and 95.6% on train, validation, and test set, respectively, while the estimated accuracy for U-Net3 was 94.1%, 92.9%, and 93.9%. Both of the U-Nets achieved a median DSC of 0.95 for SAT segmentation. The U-Net1 and the U-Net3 achieved an optimal agreement with manual segmentation for the automatic segmentation. The so-developed neural networks have the potential to automatically segment thigh muscles and SAT in patients affected by MDs.
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Affiliation(s)
- Giacomo Aringhieri
- Department of Translational Research and New Technology in Medicine and Surgery, Academic Radiology, University of Pisa, 56126 Pisa, Italy; (G.A.); (E.N.)
| | - Guja Astrea
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56128 Pisa, Italy; (G.A.); (G.M.); (R.P.); (F.M.S.)
| | - Daniela Marfisi
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), 56124 Pisa, Italy; (D.M.); (F.S.); (A.T.); (R.C.)
| | - Salvatore Claudio Fanni
- Department of Translational Research and New Technology in Medicine and Surgery, Academic Radiology, University of Pisa, 56126 Pisa, Italy; (G.A.); (E.N.)
| | - Gemma Marinella
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56128 Pisa, Italy; (G.A.); (G.M.); (R.P.); (F.M.S.)
| | - Rosa Pasquariello
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56128 Pisa, Italy; (G.A.); (G.M.); (R.P.); (F.M.S.)
| | - Giulia Ricci
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy; (G.R.); (F.T.); (G.S.)
| | - Francesco Sansone
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), 56124 Pisa, Italy; (D.M.); (F.S.); (A.T.); (R.C.)
| | - Martina Sperti
- Department of Neurology, Careggi University Hospital, University of Florence, 50134 Florence, Italy;
| | - Alessandro Tonacci
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), 56124 Pisa, Italy; (D.M.); (F.S.); (A.T.); (R.C.)
| | - Francesca Torri
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy; (G.R.); (F.T.); (G.S.)
| | - Sabrina Matà
- SOD Neurologia 1, Dipartimento Neuromuscolo-Scheletrico e Degli Organi di Senso, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
| | - Gabriele Siciliano
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy; (G.R.); (F.T.); (G.S.)
| | - Emanuele Neri
- Department of Translational Research and New Technology in Medicine and Surgery, Academic Radiology, University of Pisa, 56126 Pisa, Italy; (G.A.); (E.N.)
| | - Filippo Maria Santorelli
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56128 Pisa, Italy; (G.A.); (G.M.); (R.P.); (F.M.S.)
| | - Raffaele Conte
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), 56124 Pisa, Italy; (D.M.); (F.S.); (A.T.); (R.C.)
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Mastropietro A, Casali N, Taccogna MG, D’Angelo MG, Rizzo G, Peruzzo D. Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model. Bioengineering (Basel) 2024; 11:580. [PMID: 38927816 PMCID: PMC11200745 DOI: 10.3390/bioengineering11060580] [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: 04/22/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb-girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.
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Affiliation(s)
- Alfonso Mastropietro
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy; (A.M.); (N.C.)
| | - Nicola Casali
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy; (A.M.); (N.C.)
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Maria Giovanna Taccogna
- Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, 20054 Segrate, Milan, Italy;
| | - Maria Grazia D’Angelo
- Unit of Rehabilitation of Rare Diseases of the Central and Peripheral Nervous System, Scientific Institute IRCCS Eugenio Medea, 23842 Bosisio Parini, Lecco, Italy;
| | - Giovanna Rizzo
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy; (A.M.); (N.C.)
| | - Denis Peruzzo
- Neuroimaging Unit, Scientific Institute IRCCS Eugenio Medea, 23842 Bosisio Parini, Lecco, Italy
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3
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Khagi B, Belousova T, Short CM, Taylor AA, Bismuth J, Shah DJ, Brunner G. Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease. Am J Cardiol 2024; 220:56-66. [PMID: 38580040 DOI: 10.1016/j.amjcard.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 02/27/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024]
Abstract
Peripheral artery disease (PAD) is associated with impaired blood flow in the lower extremities and histopathologic changes of the skeletal calf muscles, resulting in abnormal microvascular perfusion. We studied the use of convolution neural networks (CNNs) to differentiate patients with PAD from matched controls using perfusion pattern features from contrast-enhanced magnetic resonance imaging (CE-MRI) of the skeletal calf muscles. We acquired CE-MRI based skeletal calf muscle perfusion in 56 patients (36 patients with PAD and 20 matched controls). Microvascular perfusion imaging was performed after reactive hyperemia at the midcalf level, with a temporal resolution of 409 ms. We analyzed perfusion scans up to 2 minutes indexed from the local precontrast arrival time frame. Skeletal calf muscles, including the anterior muscle, lateral muscle, deep posterior muscle group, and the soleus and gastrocnemius muscles, were segmented semiautomatically. Segmented muscles were represented as 3-dimensional Digital Imaging and Communications in Medicine stacks of CE-MRI perfusion scans for deep learning (DL) analysis. We tested several CNN models for the 3-dimensional CE-MRI perfusion stacks to classify patients with PAD from matched controls. A total of 2 of the best performing CNNs (resNet and divNet) were selected to develop the final classification model. A peak accuracy of 75% was obtained for resNet and divNet. Specificity was 80% and 94% for resNet and divNet, respectively. In conclusion, DL using CNNs and CE-MRI skeletal calf muscle perfusion can discriminate patients with PAD from matched controls. DL methods may be of interest for the study of PAD.
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Affiliation(s)
- Bijen Khagi
- Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, Pennsylvania
| | - Tatiana Belousova
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, Texas
| | - Christina M Short
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Addison A Taylor
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, Texas; Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
| | - Jean Bismuth
- Division of Vascular Surgery, USF Health Morsani School of Medicine, Tampa, Florida
| | - Dipan J Shah
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, Texas
| | - Gerd Brunner
- Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, Pennsylvania; Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, Texas.
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Khagi B, Belousova T, Short CM, Taylor A, Nambi V, Ballantyne CM, Bismuth J, Shah DJ, Brunner G. A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging. Magn Reson Imaging 2024; 106:31-42. [PMID: 38065273 DOI: 10.1016/j.mri.2023.11.014] [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: 08/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 01/12/2024]
Abstract
Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.
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Affiliation(s)
- Bijen Khagi
- Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Tatiana Belousova
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Christina M Short
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Addison Taylor
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Vijay Nambi
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Christie M Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jean Bismuth
- Division of Vascular Surgery, USF Health Morsani School of Medicine, Tampa, FL, USA
| | - Dipan J Shah
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Gerd Brunner
- Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, PA, USA; Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
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Zhang J, Liu Z, Tang Y, Wang S, Meng J, Li F. Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis. Anal Chem 2024; 96:1205-1213. [PMID: 38191284 DOI: 10.1021/acs.analchem.3c04368] [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: 01/10/2024]
Abstract
Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, organic biomacromolecules, and various ionic mixtures. These components offer valuable insights and can reflect an individual's physiological conditions. Here, we introduced an explainable deep learning (DL)-assisted wearable self-calibrating colorimetric biosensing analysis platform to efficiently and precisely detect the biomarker's concentration in sweat. Specifically, we have integrated the advantages of the colorimetric sensing method, adsorbing-swelling hydrogel, and explainable DL algorithms to develop an enzyme/indicator-immobilized colorimetric patch, which has reliable colorimetric sensing ability and excellent adsorbing-swelling function. A total of 5625 colorimetric images were collected as the analysis data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Zn2+, glucose, and Ca2+ in human sweats could be facilely classified and quantified with 100% accuracy via the convolutional neural network (CNN) model, and the testing results of actual sweats via the DL-assisted colorimetric approach are 91.7-97.2% matching with the classical UV-vis spectrum. Class activation mapping (CAM) was utilized to visualize the inner working mechanism of CNN operation, which contributes to verify and explicate the design rationality of the noninvasive biosensing technology. An "end-to-end" model was established to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for health monitoring, disease prevention, and clinical diagnosis.
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Affiliation(s)
- Jiabing Zhang
- Xidian University, Xi'an 710071, P. R. China
- Graduate School of Medical School of Chinese PLA Hospital BeiJing, Beijing 100853, P. R. China
| | - Zhihao Liu
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Yongtao Tang
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- Graduate School of Medical School of Chinese PLA Hospital BeiJing, Beijing 100853, P. R. China
| | - Shuang Wang
- Xidian University, Xi'an 710071, P. R. China
| | - Jianxin Meng
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
| | - Fengyu Li
- College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou 510632, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
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Mühlberg A, Ritter P, Langer S, Goossens C, Nübler S, Schneidereit D, Taubmann O, Denzinger F, Nörenberg D, Haug M, Schürmann S, Horstmeyer R, Maier AK, Goldmann WH, Friedrich O, Kreiss L. SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206319. [PMID: 37582656 PMCID: PMC10558688 DOI: 10.1002/advs.202206319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/30/2023] [Indexed: 08/17/2023]
Abstract
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where often only small and sparse data are available and the objective is knowledge discovery rather than automation. Furthermore, basic research is usually hypothesis-driven and extensive prior knowledge (priors) exists. To address this, the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI) that is designed for multiphoton microscopy (MPM)-based laboratory research is presented. It utilizes meta-learning to optimize prior (and hypothesis) integration, data representation, and neural network architecture simultaneously. By this, the method allows hypothesis testing with DL and provides interpretable feedback about the origin of biological information in 3D images. SEMPAI performs multi-task learning of several related tasks to enable prediction for small datasets. SEMPAI is applied on an extensive MPM database of single muscle fibers from a decade of experiments, resulting in the largest joint analysis of pathologies and function for single muscle fibers to date. It outperforms state-of-the-art biomarkers in six of seven prediction tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only approaches.
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Affiliation(s)
- Alexander Mühlberg
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
| | - Paul Ritter
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
- Erlangen Graduate School in Advanced Optical TechnologiesPaul‐Gordan‐Str. 691052ErlangenGermany
| | - Simon Langer
- Pattern Recognition LabDepartment of Computer ScienceFriedrich‐Alexander University Erlangen‐NurembergMartensstr. 391058ErlangenGermany
| | - Chloë Goossens
- Clinical Division and Laboratory of Intensive Care MedicineKU LeuvenUZ Herestraat 49 – P.O. box 7003Leuven3000Belgium
| | - Stefanie Nübler
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
| | - Dominik Schneidereit
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
- Erlangen Graduate School in Advanced Optical TechnologiesPaul‐Gordan‐Str. 691052ErlangenGermany
| | - Oliver Taubmann
- Pattern Recognition LabDepartment of Computer ScienceFriedrich‐Alexander University Erlangen‐NurembergMartensstr. 391058ErlangenGermany
| | - Felix Denzinger
- Pattern Recognition LabDepartment of Computer ScienceFriedrich‐Alexander University Erlangen‐NurembergMartensstr. 391058ErlangenGermany
| | - Dominik Nörenberg
- Department of Radiology and Nuclear MedicineUniversity Medical Center MannheimMedical Faculty MannheimTheodor‐Kutzer‐Ufer 1–368167MannheimGermany
| | - Michael Haug
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
| | - Sebastian Schürmann
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
| | - Roarke Horstmeyer
- Computational Optics LabDepartment of Biomedical EngineeringDuke University101 Science DrDurhamNC27708USA
| | - Andreas K. Maier
- Pattern Recognition LabDepartment of Computer ScienceFriedrich‐Alexander University Erlangen‐NurembergMartensstr. 391058ErlangenGermany
| | - Wolfgang H. Goldmann
- Biophysics GroupDepartment of PhysicsFriedrich‐Alexander University Erlangen‐NurembergHenkestr. 9191052ErlangenGermany
| | - Oliver Friedrich
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
- Erlangen Graduate School in Advanced Optical TechnologiesPaul‐Gordan‐Str. 691052ErlangenGermany
| | - Lucas Kreiss
- Institute of Medical BiotechnologyDepartment of Chemical and Biological EngineeringFriedrich‐Alexander University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
- Erlangen Graduate School in Advanced Optical TechnologiesPaul‐Gordan‐Str. 691052ErlangenGermany
- Computational Optics LabDepartment of Biomedical EngineeringDuke University101 Science DrDurhamNC27708USA
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8
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Astrea G, Morrow JM, Manzur A, Gunny R, Battini R, Mercuri E, Reilly MM, Muntoni F, Yousry TA. Muscle "islands": An MRI signature distinguishing neurogenic from myopathic causes of early onset distal weakness. Neuromuscul Disord 2021; 32:142-149. [PMID: 35033413 DOI: 10.1016/j.nmd.2021.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/12/2021] [Accepted: 11/10/2021] [Indexed: 10/19/2022]
Abstract
Muscle MRI has an increasing role in diagnosis of inherited neuromuscular diseases, but no features are known which reliably differentiate myopathic and neurogenic conditions. Using patients presenting with early onset distal weakness, we aimed to identify an MRI signature to distinguish myopathic and neurogenic conditions. We identified lower limb MRI scans from patients with either genetically (n = 24) or clinically (n = 13) confirmed diagnoses of childhood onset distal myopathy or distal spinal muscular atrophy. An initial exploratory phase reviewed 11 scans from genetically confirmed patients identifying a single potential discriminatory marker concerning the pattern of fat replacement within muscle, coined "islands". This pattern comprised small areas of muscle tissue with normal signal intensity completely surrounded by areas with similar intensity to subcutaneous fat. In the subsequent validation phase, islands correctly classified scans from all 12 remaining genetically confirmed patients, and 12/13 clinically classified patients. In the genetically confirmed patients MRI classification of neurogenic/myopathic aetiology had 100% accuracy (24/24) compared with 65% accuracy (15/23) for EMG, and 79% accuracy (15/19) for muscle biopsy. Future studies are needed in other clinical contexts, however the presence of islands appears to highly suggestive of a neurogenic aetiology in patients presenting with early onset distal motor weakness.
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Affiliation(s)
- Guja Astrea
- Department of Developmental Neuroscience, IRCCS Stella Maris, Calambrone, Pisa, Italy; Dubowitz Neuromuscular Center, UCL GOS Institute of Child Health, UK
| | - Jasper M Morrow
- Queen Square Center for Neuromuscular Diseases, National Hospital for Neurology and Neurosurgery, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.
| | - Adnan Manzur
- Dubowitz Neuromuscular Center, UCL GOS Institute of Child Health, UK
| | - Roxana Gunny
- Paediatric neuroradiology, Sidra Medicine, Qatar
| | - Roberta Battini
- Department of Developmental Neuroscience, IRCCS Stella Maris, Calambrone, Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eugenio Mercuri
- Department of Pediatric Neurology, Catholic University, Rome, Italy; Centro Clinico Nemo, Policlinico Universitario A Gemelli IRCCS, Rome, Italy
| | - Mary M Reilly
- Queen Square Center for Neuromuscular Diseases, National Hospital for Neurology and Neurosurgery, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Francesco Muntoni
- NIHR Great Ormond Street Hospital Biomedical Research Center, Great Ormond Street Institute of Child Health University College London and Great Ormond Street Hospital Trust, London, UK
| | - Tarek A Yousry
- Neuroradiological Academic Unit, Queen Square UCL Institute of Neurology and Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, UCLH, London, UK
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ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-06201-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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10
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Zheng Y, Cassol CA, Jung S, Veerapaneni D, Chitalia VC, Ren KYM, Bellur SS, Boor P, Barisoni LM, Waikar SS, Betke M, Kolachalama VB. Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1442-1453. [PMID: 34033750 PMCID: PMC8453248 DOI: 10.1016/j.ajpath.2021.05.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/01/2021] [Accepted: 05/11/2021] [Indexed: 12/25/2022]
Abstract
Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.
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Affiliation(s)
- Yi Zheng
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; Department of Computer Science, College of Arts and Sciences, Boston University, Boston, Massachusetts
| | - Clarissa A Cassol
- Arkana Laboratories, Little Rock, Arkansas; Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Saemi Jung
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Divya Veerapaneni
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Vipul C Chitalia
- Section of Nephrology, Boston University School of Medicine & Boston Medical Center, Boston, Massachusetts
| | - Kevin Y M Ren
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada
| | - Shubha S Bellur
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada; Medical Renal and Genitourinary Pathology, William Osler Health System, Brampton, Ontario, Canada
| | - Peter Boor
- Institute of Pathology & Department of Nephrology & Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany
| | - Laura M Barisoni
- Department of Pathology and Medicine, Duke University, Durham, North Carolina
| | - Sushrut S Waikar
- Section of Nephrology, Boston University School of Medicine & Boston Medical Center, Boston, Massachusetts
| | - Margrit Betke
- Department of Computer Science, College of Arts and Sciences, Boston University, Boston, Massachusetts; Faculty of Computing and Data Sciences, Boston University, Boston, Massachusetts
| | - Vijaya B Kolachalama
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; Department of Computer Science, College of Arts and Sciences, Boston University, Boston, Massachusetts; Faculty of Computing and Data Sciences, Boston University, Boston, Massachusetts.
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11
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Yildiz A, Zan H, Said S. Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102720] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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12
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The increasing role of muscle MRI to monitor changes over time in untreated and treated muscle diseases. Curr Opin Neurol 2021; 33:611-620. [PMID: 32796278 DOI: 10.1097/wco.0000000000000851] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW This review aims to discuss the recent results of studies published applying quantitative MRI sequences to large cohorts of patients with neuromuscular diseases. RECENT FINDINGS Quantitative MRI sequences are now available to identify and quantify changes in muscle water and fat content. These two components have been associated with acute and chronic injuries, respectively. Studies show that the increase in muscle water is not only reversible if therapies are applied successfully but can also predict fat replacement in neurodegenerative diseases. Muscle fat fraction correlates with muscle function tests and increases gradually over time in parallel with the functional decline of patients with neuromuscular diseases. There are new spectrometry-based sequences to quantify other components, such as glycogen, electrolytes or the pH of the muscle fibre, extending the applicability of MRI to the study of several processes in neuromuscular diseases. SUMMARY The latest results obtained from the study of long cohorts of patients with various neuromuscular diseases open the door to the use of this technology in clinical trials, which would make it possible to obtain a new measure for assessing the effectiveness of new treatments. The challenge is currently the popularization of these studies and their application to the monitoring of patients in the daily clinic.
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13
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van Doorn JLM, Pennati F, Hansen HHG, van Engelen BGM, Aliverti A, Doorduin J. Respiratory muscle imaging by ultrasound and MRI in neuromuscular disorders. Eur Respir J 2021; 58:13993003.00137-2021. [PMID: 33863737 DOI: 10.1183/13993003.00137-2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/26/2021] [Indexed: 11/05/2022]
Abstract
Respiratory muscle weakness is common in neuromuscular disorders and leads to significant respiratory difficulties. Therefore, reliable and easy assessment of respiratory muscle structure and function in neuromuscular disorders is crucial. In the last decade, ultrasound and MRI emerged as promising imaging techniques to assess respiratory muscle structure and function. Respiratory muscle imaging directly measures the respiratory muscles and, in contrast to pulmonary function testing, is independent of patient effort. This makes respiratory muscle imaging suitable to use as tool in clinical respiratory management and as outcome parameter in upcoming drug trials for neuromuscular disorders, particularly in children. In this narrative review, we discuss the latest studies and technological developments in imaging of the respiratory muscles by US and MR, and its clinical application and limitations. We aim to increase understanding of respiratory muscle imaging and facilitate its use as outcome measure in daily practice and clinical trials.
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Affiliation(s)
- Jeroen L M van Doorn
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Francesca Pennati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Hendrik H G Hansen
- Department of Medical Imaging, Medical Ultrasound Imaging Center (MUSIC), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Baziel G M van Engelen
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Jonne Doorduin
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
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14
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Yang M, Zheng Y, Xie Z, Wang Z, Xiao J, Zhang J, Yuan Y. A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images. BMC Neurol 2021; 21:13. [PMID: 33430797 PMCID: PMC7798322 DOI: 10.1186/s12883-020-02036-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 12/23/2020] [Indexed: 12/31/2022] Open
Abstract
Background Dystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promising but its diagnostic results highly depends on doctors’ experiences. This study intends to explore a way of deploying a deep learning model for muscle MRI images to diagnose dystrophinopathies. Methods This study collected 2536 T1WI images from 432 cases who had been diagnosed by genetic analysis and/or muscle biopsy, including 148 cases with dystrophinopathies and 284 cases with other diseases. The data was randomly divided into three sets: the data from 233 cases were used to train the CNN model, the data from 97 cases for the validation experiments, and the data from 102 cases for the test experiments. We also validated our models expertise at diagnosing by comparing the model’s results on the 102 cases with those of three skilled radiologists. Results The proposed model achieved 91% (95% CI: 0.88, 0.93) accuracy on the test set, higher than the best accuracy of 84% in radiologists. It also performed better than the skilled radiologists in sensitivity : sensitivities of the models and the doctors were 0.89 (95% CI: 0.85 0.93) versus 0.79 (95% CI:0.73, 0.84; p = 0.190). Conclusions The deep model achieved excellent accuracy and sensitivity in identifying cases with dystrophinopathies. The comparable performance of the model and skilled radiologists demonstrates the potential application of the model in diagnosing dystrophinopathies through MRI images.
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Affiliation(s)
- Mei Yang
- Department of Neurology, Peking University First Hospital, Beijing, China.,Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yiming Zheng
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Zhiying Xie
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Zhaoxia Wang
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Jiangxi Xiao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Jue Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yun Yuan
- Department of Neurology, Peking University First Hospital, Beijing, China.
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15
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Functional changes of the lateral pterygoid muscle in patients with temporomandibular disorders: a pilot magnetic resonance images texture study. Chin Med J (Engl) 2020; 133:530-536. [PMID: 32049744 PMCID: PMC7065862 DOI: 10.1097/cm9.0000000000000658] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Texture features were the intrinsic properties of the human tissues and could efficiently detect the subtle functional changes of involved tissue. The pathologic changes of the lateral pterygoid muscle (LPM) were significantly correlated with the temporomandibular disc displacement. However, the occult functional changes of LPM could not be detected by the naked eye on the medical images. The current study was aimed to evaluate the functional changes of the LPM in the patients with temporomandibular disorders (TMDs) using texture analysis. METHODS Twenty-nine patients with TMD were performed with magnetic resonance (MR) imaging on a 3.0T MR scanner, who were consecutively recruited from the TMD clinic of Hainan Hospital of Chinese People's Liberation Army General Hospital from February 2019 to September 2019. The patients were classified into three groups according to the disc displacement: disc without displacement (DWoD), disc displacement with reduction (DDWR) and disc displacement without reduction (DDWoR). The gray-level co-occurrence matrix method was applied with the texture analysis of LPM on the axial T2-weighted imaging. The texture features included angular second moment, contrast, correlation, inverse different moment, and entropy. One-way analysis of variance was used for grouped comparisons and receiver operating characteristics (ROC) curve analysis was applied to evaluate the diagnostic efficacy of the texture parameters. RESULTS Texture contrast of LPM presented significantly lower in DDWoR (46.30 [35.03, 94.48]) than that in DWoD (123.85 [105.06, 143.23]; test statistic = 23.05; P < 0.001). Texture entropy of LPM showed significant differences among DWoD (7.62 ± 0.33), DDWR (6.76 ± 0.35), and DDWoR (6.46 ± 0.39) (PDWoD-DDWR < 0.001, PDWoD-DDWoR < 0.001, and PDDWR-DDWoR = 0.014). Area under the ROC curve (AUC) demonstrated that texture entropy had an excellent diagnostic accuracy for DWoD-DDWR (AUC = 0.96) and DWoD-DDWoR (AUC = 0.98). CONCLUSION The texture contrast and entropy could identify the altered functional status of LPM in patients with TMD and could be considered as the effective imaging biomarker to evaluate the functional changes of LPM in TMD.
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16
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Felisaz PF, Colelli G, Ballante E, Solazzo F, Paoletti M, Germani G, Santini F, Deligianni X, Bergsland N, Monforte M, Tasca G, Ricci E, Bastianello S, Figini S, Pichiecchio A. Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy. Eur J Radiol 2020; 134:109460. [PMID: 33296803 DOI: 10.1016/j.ejrad.2020.109460] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/04/2020] [Accepted: 11/29/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. METHOD Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. RESULTS Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. CONCLUSION This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.
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Affiliation(s)
- Paolo Florent Felisaz
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Radiology, Desio Hospital, ASST Monza, Desio, Italy.
| | - Giulia Colelli
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Mathematics, University of Pavia, Pavia, Italy
| | - Elena Ballante
- Department of Mathematics, University of Pavia, Pavia, Italy; BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Francesca Solazzo
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Paoletti
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Francesco Santini
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Xeni Deligianni
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Mauro Monforte
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giorgio Tasca
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Enzo Ricci
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Stefano Bastianello
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, PV, Italy
| | - Anna Pichiecchio
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
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17
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Long RKM, Moriarty KP, Cardoen B, Gao G, Vogl AW, Jean F, Hamarneh G, Nabi IR. Super resolution microscopy and deep learning identify Zika virus reorganization of the endoplasmic reticulum. Sci Rep 2020; 10:20937. [PMID: 33262363 PMCID: PMC7708840 DOI: 10.1038/s41598-020-77170-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 10/28/2020] [Indexed: 01/08/2023] Open
Abstract
The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flaviviruses such as Zika virus (ZIKV) induce reorganization of ER membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER. Viral non-structural proteins NS4B and NS2B associate with replication complexes within the ZIKV-induced tubular matrix and exhibit distinct ER distributions outside this central ER region. Deep neural networks trained to distinguish ZIKV-infected versus mock-infected cells successfully identified ZIKV-induced central ER tubular matrices as a determinant of viral infection. Super resolution microscopy and deep learning are therefore able to identify and localize morphological features of the ER and allow for better understanding of how ER morphology changes due to viral infection.
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Affiliation(s)
- Rory K M Long
- Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.,Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.,Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Kathleen P Moriarty
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Ben Cardoen
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Guang Gao
- Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.,Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - A Wayne Vogl
- Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.,Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - François Jean
- Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada. .,Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
| | - Ghassan Hamarneh
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
| | - Ivan R Nabi
- Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada. .,Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada. .,School of Biomedical Engineering, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
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18
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Araujo ECA, Marty B, Carlier PG, Baudin P, Reyngoudt H. Multiexponential Analysis of the Water
T2
‐Relaxation in the Skeletal Muscle Provides Distinct Markers of Disease Activity Between Inflammatory and Dystrophic Myopathies. J Magn Reson Imaging 2020; 53:181-189. [DOI: 10.1002/jmri.27300] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Ericky C. A. Araujo
- NMR laboratory, Neuromuscular Investigation Center Institute of Myology Paris France
- CEA, DRF, IBFJ, MIRCen Paris France
| | - Benjamin Marty
- NMR laboratory, Neuromuscular Investigation Center Institute of Myology Paris France
- CEA, DRF, IBFJ, MIRCen Paris France
| | - Pierre G. Carlier
- NMR laboratory, Neuromuscular Investigation Center Institute of Myology Paris France
- CEA, DRF, IBFJ, MIRCen Paris France
| | | | - Harmen Reyngoudt
- NMR laboratory, Neuromuscular Investigation Center Institute of Myology Paris France
- CEA, DRF, IBFJ, MIRCen Paris France
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19
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MRI patterns of muscle involvement in type 2 and 3 spinal muscular atrophy patients. J Neurol 2019; 267:898-912. [DOI: 10.1007/s00415-019-09646-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/05/2019] [Accepted: 11/18/2019] [Indexed: 12/17/2022]
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20
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Jin R, Luk KD, Cheung JPY, Hu Y. Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods. NMR IN BIOMEDICINE 2019; 32:e4114. [PMID: 31131933 DOI: 10.1002/nbm.4114] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 04/20/2019] [Accepted: 04/22/2019] [Indexed: 06/09/2023]
Abstract
Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy-five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one-year follow-up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k-nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF-SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10-fold cross-validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF-SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF-SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM.
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Affiliation(s)
- Richu Jin
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong
| | - Keith Dk Luk
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong
| | - Jason Pui Yin Cheung
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong
| | - Yong Hu
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong
- Shenzhen Key Laboratory for Innovative Technology in Orthopaedic Trauma, Department of Orthopaedics and Traumatology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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21
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Leung DG. Advancements in magnetic resonance imaging-based biomarkers for muscular dystrophy. Muscle Nerve 2019; 60:347-360. [PMID: 31026060 DOI: 10.1002/mus.26497] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2019] [Indexed: 12/26/2022]
Abstract
Recent years have seen steady progress in the identification of genetic muscle diseases as well as efforts to develop treatment for these diseases. Consequently, sensitive and objective new methods are required to identify and monitor muscle pathology. Magnetic resonance imaging offers multiple potential biomarkers of disease severity in the muscular dystrophies. This Review uses a pathology-based approach to examine the ways in which MRI and spectroscopy have been used to study muscular dystrophies. Methods that have been used to quantitate intramuscular fat, edema, fiber orientation, metabolism, fibrosis, and vascular perfusion are examined, and this Review describes how MRI can help diagnose these conditions and improve upon existing muscle biomarkers by detecting small increments of disease-related change. Important challenges in the implementation of imaging biomarkers, such as standardization of protocols and validating imaging measurements with respect to clinical outcomes, are also described.
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Affiliation(s)
- Doris G Leung
- Center for Genetic Muscle Disorders, Hugo W. Moser Research Institute at Kennedy Krieger Institute, 716 North Broadway, Room 411, Baltimore, Maryland, 21205.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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