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Lopergolo D, Rosini F, Pretegiani E, Bargagli A, Serchi V, Rufa A. Autosomal recessive cerebellar ataxias: a diagnostic classification approach according to ocular features. Front Integr Neurosci 2024; 17:1275794. [PMID: 38390227 PMCID: PMC10883068 DOI: 10.3389/fnint.2023.1275794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/10/2023] [Indexed: 02/24/2024] Open
Abstract
Autosomal recessive cerebellar ataxias (ARCAs) are a heterogeneous group of neurodegenerative disorders affecting primarily the cerebellum and/or its afferent tracts, often accompanied by damage of other neurological or extra-neurological systems. Due to the overlap of clinical presentation among ARCAs and the variety of hereditary, acquired, and reversible etiologies that can determine cerebellar dysfunction, the differential diagnosis is challenging, but also urgent considering the ongoing development of promising target therapies. The examination of afferent and efferent visual system may provide neurophysiological and structural information related to cerebellar dysfunction and neurodegeneration thus allowing a possible diagnostic classification approach according to ocular features. While optic coherence tomography (OCT) is applied for the parametrization of the optic nerve and macular area, the eye movements analysis relies on a wide range of eye-tracker devices and the application of machine-learning techniques. We discuss the results of clinical and eye-tracking oculomotor examination, the OCT findings and some advancing of computer science in ARCAs thus providing evidence sustaining the identification of robust eye parameters as possible markers of ARCAs.
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Affiliation(s)
- Diego Lopergolo
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
- UOC Neurologia e Malattie Neurometaboliche, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Francesca Rosini
- UOC Stroke Unit, Department of Emergenza-Urgenza, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Elena Pretegiani
- Unit of Neurology, Centre Hospitalier Universitaire Vaudoise Lausanne, Unit of Neurology and Cognitive Neurorehabilitation, Universitary Hospital of Fribourg, Fribourg, Switzerland
| | - Alessia Bargagli
- Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Valeria Serchi
- Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Alessandra Rufa
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
- UOC Neurologia e Malattie Neurometaboliche, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
- Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy
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Seoni S, Jahmunah V, Salvi M, Barua PD, Molinari F, Acharya UR. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023). Comput Biol Med 2023; 165:107441. [PMID: 37683529 DOI: 10.1016/j.compbiomed.2023.107441] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | | | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Sigawi T, Ilan Y. Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics (Basel) 2023; 8:359. [PMID: 37622964 PMCID: PMC10452845 DOI: 10.3390/biomimetics8040359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.
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Affiliation(s)
| | - Yaron Ilan
- Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem P.O. Box 12000, Israel;
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Milanés-Hermosilla D, Trujillo-Codorniú R, Lamar-Carbonell S, Sagaró-Zamora R, Tamayo-Pacheco JJ, Villarejo-Mayor JJ, Delisle-Rodriguez D. Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:703. [PMID: 36679501 PMCID: PMC9862912 DOI: 10.3390/s23020703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.
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Affiliation(s)
| | - Rafael Trujillo-Codorniú
- Department of Automatic Engineering, University of Oriente, Santiago de Cuba 90500, Cuba
- Electronics, Communications and Computing Services Company for the Nickel Industry, Holguín 80100, Cuba
| | | | - Roberto Sagaró-Zamora
- Department of Mechanical Engineering, University of Oriente, Santiago de Cuba 90500, Cuba
| | | | - John Jairo Villarejo-Mayor
- Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianopolis 88040-900, SC, Brazil
| | - Denis Delisle-Rodriguez
- Postgraduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba 59280-000, RN, Brazil
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Cabeza-Ruiz R, Velázquez-Pérez L, Pérez-Rodríguez R, Reetz K. ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications. Med Biol Eng Comput 2023; 61:1-24. [PMID: 36385616 DOI: 10.1007/s11517-022-02714-w] [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: 09/16/2021] [Accepted: 10/26/2022] [Indexed: 11/17/2022]
Abstract
Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results. In this work, we present the main clinical and imaging features of polyglutamine SCAs, and the basics of CNNs. Finally, we review studies that have used this approach to automatically process brain medical images and may be applied to SCAs detection. We conclude by discussing the possible limitations and opportunities of using ConvNets for SCAs diagnose in the future.
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Affiliation(s)
| | - Luis Velázquez-Pérez
- Cuban Academy of Sciences, La Habana, Cuba
- Center for the Research and Rehabilitation of Hereditary Ataxias, Holguín, Cuba
| | - Roberto Pérez-Rodríguez
- CAD/CAM Study Center, University of Holguín, Holguín, Cuba
- Cuban Academy of Sciences, La Habana, Cuba
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification. SENSORS 2021; 21:s21217241. [PMID: 34770553 PMCID: PMC8588128 DOI: 10.3390/s21217241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/24/2021] [Accepted: 10/28/2021] [Indexed: 11/16/2022]
Abstract
Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition.
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BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.07.024] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Desuky AS, Hussain S. An Improved Hybrid Approach for Handling Class Imbalance Problem. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 46:3853-3864. [PMID: 33532169 PMCID: PMC7841761 DOI: 10.1007/s13369-021-05347-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 01/12/2021] [Indexed: 11/28/2022]
Abstract
Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.
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Affiliation(s)
| | - Sadiq Hussain
- Examination Branch, Dibrugarh University, Dibrugarh, India
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Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data. MATHEMATICS 2020. [DOI: 10.3390/math8071078] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Uncertainty quantification in deep learning models is especially important for the medical applications of this complex and successful type of neural architectures. One popular technique is Monte Carlo dropout that gives a sample output for a record, which can be measured statistically in terms of average probability and variance for each diagnostic class of the problem. The current paper puts forward a convolutional–long short-term memory network model with a Monte Carlo dropout layer for obtaining information regarding the model uncertainty for saccadic records of all patients. These are next used in assessing the uncertainty of the learning model at the higher level of sets of multiple records (i.e., registers) that are gathered for one patient case by the examining physician towards an accurate diagnosis. Means and standard deviations are additionally calculated for the Monte Carlo uncertainty estimates of groups of predictions. These serve as a new collection where a random forest model can perform both classification and ranking of variable importance. The approach is validated on a real-world problem of classifying electrooculography time series for an early detection of spinocerebellar ataxia 2 and reaches an accuracy of 88.59% in distinguishing between the three classes of patients.
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