Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.info:eu-repo/semantics/acceptedVersio

Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features

(C) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.info:eu-repo/semantics/acceptedVersio

Texture Analysis of T1-weighted Turbo Spin-Echo MRI for the Diagnosis and Follow-up of Collagen VI-related Myopathy

Muscle texture analysis in Magnetic Resonance Imaging (MRI) has revealed a good correlation with typical histological changes resulting from neuromuscular disorders. In this research, we assess the effectiveness of several features in describing intramuscular texture alterations in cases of Collagen VI-related myopathy. A T1-weighted Turbo Spin-Echo MRI dataset was used (Nsubj = 26), consisting of thigh scans from subjects diagnosed with Ullrich Congenital Muscular Dystrophy or Bethlem Myopathy, with different severity levels, as well as healthy subjects. A total of 355 texture features were studied, including attributes derived from the Gray-Level Co-occurrence Matrix, the Run-Length Matrix, Wavelet and Gabor filters. The extracted features were ranked using the Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm with Correlation Bias Reduction, prior to cross-validated classification with a Gaussian kernel SVM.info:eu-repo/semantics/acceptedVersionhttps://ieeexplore.ieee.org/document/9433942

Muscle "islands": an MRI signature distinguishing neurogenic from myopathic causes of early onset distal weakness

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

Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology

La tesi affronta la possibilità di utilizzare metodi matematici, tecniche di simulazione, teorie fisiche riadattate e algoritmi di intelligenza artificiale per soddisfare le esigenze cliniche in neuroradiologia e neurologia al fine di descrivere e prevedere i patterns e l’evoluzione temporale di una malattia, nonché di supportare il processo decisionale clinico. La tesi è suddivisa in tre parti. La prima parte riguarda lo sviluppo di un workflow radiomico combinato con algoritmi di Machine Learning al fine di prevedere parametri che favoriscono la descrizione quantitativa dei cambiamenti anatomici e del coinvolgimento muscolare nei disordini neuromuscolari, con particolare attenzione alla distrofia facioscapolo-omerale. Il workflow proposto si basa su sequenze di risonanza magnetica convenzionali disponibili nella maggior parte dei centri neuromuscolari e, dunque, può essere utilizzato come strumento non invasivo per monitorare anche i più piccoli cambiamenti nei disturbi neuromuscolari oltre che per la valutazione della progressione della malattia nel tempo. La seconda parte riguarda l’utilizzo di un modello cinetico per descrivere la crescita tumorale basato sugli strumenti della meccanica statistica per sistemi multi-agente e che tiene in considerazione gli effetti delle incertezze cliniche legate alla variabilità della progressione tumorale nei diversi pazienti. L'azione dei protocolli terapeutici è modellata come controllo che agisce a livello microscopico modificando la natura della distribuzione risultante. Viene mostrato come lo scenario controllato permetta di smorzare le incertezze associate alla variabilità della dinamica tumorale. Inoltre, sono stati introdotti metodi di simulazione numerica basati sulla formulazione stochastic Galerkin del modello cinetico sviluppato. La terza parte si riferisce ad un progetto ancora in corso che tenta di descrivere una porzione di cervello attraverso la teoria quantistica dei campi e di simularne il comportamento attraverso l'implementazione di una rete neurale con una funzione di attivazione costruita ad hoc e che simula la funzione di risposta del modello biologico neuronale. E’ stato ottenuto che, nelle condizioni studiate, l'attività della porzione di cervello può essere descritta fino a O(6), i.e, considerando l’interazione fino a sei campi, come un processo gaussiano. Il framework quantistico definito può essere esteso anche al caso di un processo non gaussiano, ovvero al caso di una teoria di campo quantistico interagente utilizzando l’approccio della teoria wilsoniana di campo efficace.The thesis addresses the possibility of using mathematical methods, simulation techniques, repurposed physical theories and artificial intelligence algorithms to fulfill clinical needs in neuroradiology and neurology. The aim is to describe and to predict disease patterns and its evolution over time as well as to support clinical decision-making processes. The thesis is divided into three parts. Part 1 is related to the development of a Radiomic workflow combined with Machine Learning algorithms in order to predict parameters that quantify muscular anatomical involvement in neuromuscular diseases, with special focus on Facioscapulohumeral dystrophy. The proposed workflow relies on conventional Magnetic Resonance Imaging sequences available in most neuromuscular centers and it can be used as a non-invasive tool to monitor even fine change in neuromuscular disorders and to evaluate longitudinal diseases’ progression over time. Part 2 is about the description of a kinetic model for tumor growth by means of classical tools of statistical mechanics for many-agent systems also taking into account the effects of clinical uncertainties related to patients’ variability in tumor progression. The action of therapeutic protocols is modeled as feedback control at the microscopic level. The controlled scenario allows the dumping of uncertainties associated with the variability in tumors’ dynamics. Suitable numerical methods, based on Stochastic Galerkin formulation of the derived kinetic model, are introduced. Part 3 refers to a still-on going project that attempts to describe a brain portion through a quantum field theory and to simulate its behavior through the implementation of a neural network with an ad-hoc activation function mimicking the biological neuron model response function. Under considered conditions, the brain portion activity can be expressed up to O(6), i.e., up to six fields interaction, as a Gaussian Process. The defined quantum field framework may also be extended to the case of a Non-Gaussian Process behavior, or rather to an interacting quantum field theory in a Wilsonian Effective Field theory approach

The Need for Standardized Assessment of Muscle Quality in Skeletal Muscle Function Deficit and Other Aging-Related Muscle Dysfunctions: A Symposium Report

A growing body of scientific literature suggests that not only changes in skeletal muscle mass, but also other factors underpinning muscle quality, play a role in the decline in skeletal muscle function and impaired mobility associated with aging. A symposium on muscle quality and the need for standardized assessment was held on April 28, 2016 at the International Conference on Frailty and Sarcopenia Research in Philadelphia, Pennsylvania. The purpose of this symposium was to provide a venue for basic science and clinical researchers and expert clinicians to discuss muscle quality in the context of skeletal muscle function deficit and other aging-related muscle dysfunctions. The present article provides an expanded introduction concerning the emerging definitions of muscle quality and a potential framework for scientific inquiry within the field. Changes in muscle tissue composition, based on excessive levels of inter- and intra-muscular adipose tissue and intramyocellular lipids, have been found to adversely impact metabolism and peak force generation. However, methods to easily and rapidly assess muscle tissue composition in multiple clinical settings and with minimal patient burden are needed. Diagnostic ultrasound and other assessment methods continue to be developed for characterizing muscle pathology, and enhanced sonography using sensors to provide user feedback and improve reliability is currently the subject of ongoing investigation and development. In addition, measures of relative muscle force such as specific force or grip strength adjusted for body size have been proposed as methods to assess changes in muscle quality. Furthermore, performance-based assessments of muscle power via timed tests of function and body size estimates, are associated with lower extremity muscle strength may be responsive to age-related changes in muscle quality. Future aims include reaching consensus on the definition and standardized assessments of muscle quality, and providing recommendations to address critical clinical and technology research gaps within the field

Intelligent Early Diagnosis System against Strep Throat Infection Using Deep Neural Networks

The most frequent bacterial pathogen causing acute pharyngitis is Group-A hemolytic Streptococcus (GAS), and sore throat is the second most frequent acute infection. The immunological reaction to group A Streptococcus-induced pharyngitis results in Acute Rheumatic Fever (ARF). A genetically vulnerable host for ARF is a streptococcal infection. ARF, which can affect various organs and cause irreparable valve damage and heart failure, is the antecedent to Rheumatic Heart Disease (RHD). RHD, in many countries is Cardiovascular Disease (CVD) refers to a range of conditions that affect the heart and blood vessels, including coronary artery disease, heart attack, heart failure, and stroke. It is important to note that while this approach has demonstrated promising results, further studies and validation are necessary to establish its clinical feasibility and reliability. Further research can also be done to evaluate the generalization of the model to larger and diverse patient populations. The results showed that using Image Synthesis-based augmentation improved the ROC-AUC scores compared to basic data augmentation. The proposed method could be a valuable tool for healthcare professionals to quickly and accurately diagnose strep throat, leading to timely treatment and improved patient outcomes. The experimental findings indicate that the suggested detection approach for strep throat has a high level of accuracy and effectiveness. The approach has an average sensitivity of 93.1%, average specificity of 96.7%, and an overall accuracy of 96.3%. The ROC-AUC of 0.989 suggests that the approach is effective at distinguishing between positive and negative cases of strep throat. These results indicate that the suggested detection approach is a promising tool for accurately identifying cases of strep throat

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Biochemical Biomarkers and Neurodegenerative Diseases

In this book, we collected scientific articles, including reviews and research articles, showcasing the lastest literature on the importance of biochemical biomarkers in the management of neurodegenerative diseases, from screening to diagnosis, prognosis, and treatment

Imaging of the muscle and bone from benchtop to bedside

Studies have begun to show that muscles and bones play a role in the regulation of biological functions through a combination of biomechanical and biochemical signals. In vivo and ex vivo imaging techniques are crucial in the understanding of the morphology and architecture of muscle and bone for further understanding of musculoskeletal physiology and pathophysiology. This systematic review of the literature summarizes current knowledge and outlines new insights into the functions of muscle and bone elucidated by imaging techniques, with a focus on the recent advances in the musculoskeletal system enabled by novel technologies, such as CLARITY, Fast Free-of-Acrylamide Clearing Tissue (FACT), computed tomography (CT), and positron emission tomography (PET). This may serve as guidance for the development of new strategies to prevent and diagnose motor or metabolism disorders related to the malfunction of muscle and bone