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Advanced Machine Learning Algorithms for Biomedical Data and Imaging

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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 6659

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Special Issue Editor

Dr. Mohammad Tanveer

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Guest Editor

Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
Interests: deep learning; machine learning; support vector machines; data science; neuroimaging

Special Issue Information

Dear Colleagues,

Deep learning is one of the most important revolutions in the field of artificial intelligence over the last decade. It has achieved great success in different tasks in computer vision, image processing, biomedical analysis and related fields. Researchers in deep and shallow machine learning, including those working in computer vision, image processing, biomedical analysis and related fields, when working with experienced clinicians, can play a significant role in the understanding of and work on complex medical data, which ultimately improves patient care. Developing a novel deep or shallow machine learning algorithm specific to medical data is a main challenge and need at present. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data-mining methods to extract the knowledge from the available information. Biomedical data contain several challenges in data analysis, including high dimensionality, class imbalance and a low number of samples. Although the current research in this field has shown promising results, several research issues need to be explored, as follows. There is a need to explore novel feature selection methods to improve predictive performance and interpretation, and to explore large-scale data in the biomedical sciences.

This Special Issue aims to bring together the current research progress from both academia and industry on novel machine learning methods to address the challenges in the complex biomedical data. Special attention will be devoted to handling feature selection, class imbalances, and data fusion in biomedical and machine learning applications. This will attract medical experts who have access to interesting sources of data but lack the expertise in the effective use of machine learning techniques

Dr. Mohammad Tanveer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

deep learning
machine learning
support vector machines
data science
neuroimaging

Published Papers (5 papers)

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20 pages, 4987 KiB

Open AccessArticle

Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging

by Wentao Du, Kuiying Yin and Jingping Shi

Brain Sci. 2023, 13(11), 1549; https://doi.org/10.3390/brainsci13111549 - 4 Nov 2023

Cited by 2 | Viewed by 955

Abstract

In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biomedical Data and Imaging)

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13 pages, 3341 KiB

Open AccessArticle

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

by Hoon-Seok Yoon, Jeongmin Oh and Yoon-Chul Kim

Brain Sci. 2023, 13(11), 1512; https://doi.org/10.3390/brainsci13111512 - 26 Oct 2023

Viewed by 979

Abstract

This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels’ tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects’ three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels’ diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model’s age predictions in patients with intracranial vessel diseases. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biomedical Data and Imaging)

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19 pages, 3132 KiB

Open AccessArticle

Towards Environment-Aware Fall Risk Assessment: Classifying Walking Surface Conditions Using IMU-Based Gait Data and Deep Learning

by Abdulnasır Yıldız

Brain Sci. 2023, 13(10), 1428; https://doi.org/10.3390/brainsci13101428 - 8 Oct 2023

Viewed by 1053

Abstract

Fall risk assessment (FRA) helps clinicians make decisions about the best preventative measures to lower the risk of falls by identifying the different risks that are specific to an individual. With the development of wearable technologies such as inertial measurement units (IMUs), several free-living FRA methods based on fall predictors derived from IMU-based data have been introduced. The performance of such methods could be improved by increasing awareness of the individuals’ walking environment. This study aims to introduce and analyze a 25-layer convolutional neural network model for classifying nine walking surface conditions using IMU-based gait data, providing a basis for environment-aware FRAs. A database containing data collected from thirty participants who wore six IMU sensors while walking on nine surface conditions was employed. A systematic analysis was conducted to determine the effects of gait signals (acceleration, magnetic field, and rate of turn), sensor placement, and signal segment size on the method’s performance. Accuracies of 0.935 and 0.969 were achieved using a single and dual sensor, respectively, reaching an accuracy of 0.971 in the best-case scenario with optimal settings. The findings and analysis can help to develop more reliable and interpretable fall predictors, eventually leading to environment-aware FRA methods. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biomedical Data and Imaging)

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15 pages, 1116 KiB

Open AccessArticle

A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score

by Marilena Briguglio, Laura Turriziani, Arianna Currò, Antonella Gagliano, Gabriella Di Rosa, Daniela Caccamo, Alessandro Tonacci and Sebastiano Gangemi

Brain Sci. 2023, 13(6), 883; https://doi.org/10.3390/brainsci13060883 - 31 May 2023

Cited by 3 | Viewed by 1754

Abstract

Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis. However, owing to ASD heterogeneity, the presence of false positives remains a challenge for clinicians. In this study, retrospective data from patients with ASD and multi-systemic developmental disorder (MSDD), a term used to describe children under the age of 3 with impaired communication but with strong emotional attachments, were tested by machine learning (ML) models to assess the best predictors of disease development as well as the items that best describe these two autism spectrum disorder presentations. Maternal and infant data as well as ADOS-2 score were included in different ML testing models. Depending on the outcome to be estimated, a best-performing model was selected. RIDGE regression model showed that the best predictors for ADOS social affect score were gut disturbances, EEG retrievals, and sleep problems. Linear Regression Model showed that term pregnancy, psychomotor development status, and gut disturbances were predicting at best for the ADOS Repetitive and Restricted Behavior score. The LASSO regression model showed that EEG retrievals, sleep disturbances, age at diagnosis, term pregnancy, weight at birth, gut disturbances, and neurological findings were the best predictors for the overall ADOS score. The CART classification and regression model showed that age at diagnosis and weight at birth best discriminate between ASD and MSDD. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biomedical Data and Imaging)

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12 pages, 1921 KiB

Open AccessBrief Report

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections

by Johan Jönemo, Muhammad Usman Akbar, Robin Kämpe, J. Paul Hamilton and Anders Eklund

Brain Sci. 2023, 13(9), 1329; https://doi.org/10.3390/brainsci13091329 - 15 Sep 2023

Cited by 2 | Viewed by 1229

Abstract

Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20–50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biomedical Data and Imaging)

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