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Machine and Deep Learning in the Health Domain 2024

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A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 16940

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Dr. Hersh Sagreiya Sagreiya

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Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Interests: machine learning; deep learning; informatics; medical imaging
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Dear Colleagues,

There has been a recent revolution in the application of machine learning and deep learning within healthcare, with interest in this area increasing exponentially at both medical society meetings and computer science conferences. Unlike prior attempts at medical AI and computer-aided diagnosis, these algorithms do not rely on predetermined features and can discern patterns in the data that would be impossible for an individual to detect.

The healthcare domain provides rich data that these algorithms can draw upon, including clinical notes, vital signs, laboratory values, genomic data, pathology, radiological images, and medical sensors, just to name a few. In addition, multi-modal and omics data may be applied to solve clinical problems. These data can be used to achieve multiple goals, including diagnosing diseases, prognosticating clinical outcomes, determining responses to therapy, patient monitoring, and drug as well as device development. In addition, these technologies provide researchers with the opportunity to enhance their understanding of disease pathogenesis, leveraging both large volumes of data and advanced machine learning techniques.

These developments allow for new frontiers in medicine. These include learning healthcare systems that improve with time as they incorporate increasing volumes of multimodal data from diverse patient populations. They also enable personalized medicine, the tailoring of healthcare to individual patients. Meanwhile, it is crucial that these algorithms remain robust to perturbations in the input data while remaining trustworthy, ethical, and free of bias. These techniques need to generalize well to heterogeneous patient populations, while maintaining and ultimately improving their performance in the populations in which they were developed. This Special Issue welcomes both original research articles and review articles that investigate the state of the art in machine learning and deep learning applied to healthcare.

Dr. Hersh Sagreiya Sagreiya
Guest Editor

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Keywords

machine learning
deep learning
medicine
health
disease diagnosis
disease prognostication
treatment effectiveness
electronic medical records
medical informatics

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Published Papers (9 papers)

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31 pages, 4735 KiB

Open AccessArticle

Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier

by Tushar Hrishikesh Jaware, Chittaranjan Nayak, Priyadarsan Parida, Nawaf Ali, Yogesh Sharma and Wael Hadi

Computers 2024, 13(10), 260; https://doi.org/10.3390/computers13100260 - 11 Oct 2024

Viewed by 992

Abstract

Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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13 pages, 2937 KiB

Open AccessArticle

An Unsupervised Approach for Treatment Effectiveness Monitoring Using Curvature Learning

by Hersh Sagreiya, Isabelle Durot and Alireza Akhbardeh

Computers 2024, 13(9), 227; https://doi.org/10.3390/computers13090227 - 9 Sep 2024

Viewed by 831

Abstract

Contrast-enhanced ultrasound could assess whether cancer chemotherapeutic agents work in days, rather than waiting 2–3 months, as is typical using the Response Evaluation Criteria in Solid Tumors (RECIST), therefore avoiding toxic side effects and expensive, ineffective therapy. A total of 40 mice were implanted with human colon cancer cells: treatment-sensitive mice in control (n = 10, receiving saline) and treated (n = 10, receiving bevacizumab) groups and treatment-resistant mice in control (n = 10) and treated (n = 10) groups. Each mouse was imaged using 3D dynamic contrast-enhanced ultrasound with Definity microbubbles. Curvature learning, an unsupervised learning approach, quantized pixels into three classes—blue, yellow, and red—representing normal, intermediate, and high cancer probability, both at baseline and after treatment. Next, a curvature learning score was calculated for each mouse using statistical measures representing variations in these three color classes across each frame from cine ultrasound images obtained during contrast administration on a given day (intra-day variability) and between pre- and post-treatment days (inter-day variability). A Wilcoxon rank-sum test compared score distributions between treated, treatment-sensitive mice and all others. There was a statistically significant difference in tumor score between the treated, treatment-sensitive group (n = 10) and all others (n = 30) (p = 0.0051). Curvature learning successfully identified treatment response, detecting changes in tumor perfusion before changes in tumor size. A similar technique could be developed for humans. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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29 pages, 4733 KiB

Open AccessArticle

Flexural Eigenfrequency Analysis of Healthy and Pathological Tissues Using Machine Learning and Nonlocal Viscoelasticity

by Ali Farajpour and Wendy V. Ingman

Computers 2024, 13(7), 179; https://doi.org/10.3390/computers13070179 - 19 Jul 2024

Cited by 1 | Viewed by 1132

Abstract

Biomechanical characteristics can be used to assist the early detection of many diseases, including breast cancer, thyroid nodules, prostate cancer, liver fibrosis, ovarian diseases, and tendon disorders. In this paper, a scale-dependent viscoelastic model is developed to assess the biomechanical behaviour of biological tissues subject to flexural waves. The nonlocal strain gradient theory, in conjunction with machine learning techniques such as extreme gradient boosting, k-nearest neighbours, support vector machines, and random forest, is utilised to develop a computational platform for biomechanical analysis. The coupled governing differential equations are derived using Hamilton’s law. Transverse wave analysis is conducted to investigate different normal and pathological human conditions including ovarian cancer, breast cancer, and ovarian fibrosis. Viscoelastic, strain gradient, and nonlocal effects are used to describe the impact of fluid content, stiffness hardening caused by the gradients of strain components, and stiffness softening associated with the nonlocality of stress components within the biological tissues and cells. The integration of the scale-dependent biomechanical continuum model with machine learning facilitates the adoption of the developed model in practical applications by allowing for learning from clinical data, alongside the intrinsic mechanical laws that govern biomechanical responses. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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15 pages, 617 KiB

Open AccessArticle

Personalized Classifier Selection for EEG-Based BCIs

by Javad Rahimipour Anaraki, Antonina Kolokolova and Tom Chau

Computers 2024, 13(7), 158; https://doi.org/10.3390/computers13070158 - 21 Jun 2024

Viewed by 1038

Abstract

The most important component of an Electroencephalogram (EEG) Brain–Computer Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful commands. The accuracy and speed of the classifier determine the utility of the BCI. However, there is significant intra- and inter-subject variability in EEG data, complicating the choice of the best classifier for different individuals over time. There is a keen need for an automatic approach to selecting a personalized classifier suited to an individual’s current needs. To this end, we have developed a systematic methodology for individual classifier selection, wherein the structural characteristics of an EEG dataset are used to predict a classifier that will perform with high accuracy. The method was evaluated using motor imagery EEG data from Physionet. We confirmed that our approach could consistently predict a classifier whose performance was no worse than the single-best-performing classifier across the participants. Furthermore, Kullback–Leibler divergences between reference distributions and signal amplitude and class label distributions emerged as the most important characteristics for classifier prediction, suggesting that classifier choice depends heavily on the morphology of signal amplitude densities and the degree of class imbalance in an EEG dataset. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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24 pages, 960 KiB

Open AccessArticle

Advancing Skin Cancer Prediction Using Ensemble Models

by Priya Natha and Pothuraju RajaRajeswari

Computers 2024, 13(7), 157; https://doi.org/10.3390/computers13070157 - 21 Jun 2024

Cited by 1 | Viewed by 1271

Abstract

There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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17 pages, 3025 KiB

Open AccessArticle

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

by Ayad E. Korial, Ivan Isho Gorial and Amjad J. Humaidi

Computers 2024, 13(6), 126; https://doi.org/10.3390/computers13060126 - 22 May 2024

Cited by 8 | Viewed by 1851

Abstract

Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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17 pages, 4179 KiB

Open AccessArticle

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

by Michelle Annice Tjitra, Nagisa Eremia Anju, Dodi Sudiana and Mia Rizkinia

Computers 2024, 13(5), 125; https://doi.org/10.3390/computers13050125 - 17 May 2024

Viewed by 2065

Abstract

Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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25 pages, 2999 KiB

Open AccessArticle

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

by Ahmed Bir-Jmel, Sidi Mohamed Douiri, Souad El Bernoussi, Ayyad Maafiri, Yassine Himeur, Shadi Atalla, Wathiq Mansoor and Hussain Al-Ahmad

Computers 2024, 13(4), 93; https://doi.org/10.3390/computers13040093 - 6 Apr 2024

Viewed by 2635

Abstract

Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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21 pages, 6859 KiB

Open AccessSystematic Review

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

by Yin Wang, Weibin Cheng, Fahim Sufi, Qiang Fang and Seedahmed S. Mahmoud

Computers 2024, 13(5), 117; https://doi.org/10.3390/computers13050117 - 9 May 2024

Viewed by 3248

Abstract

In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition’s complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 relevant documents, unveiling a landscape marked by significant advancements and persistent challenges. Through a methodological lens grounded in the PRISMA framework (Version 2020) and Machine Learning-driven tools like VosViewer (Version 1.6.20) and Litmaps (Free Version), the research delineates the high variability in speech patterns, the intricacies of speech recognition, and the hurdles posed by limited and diverse datasets as core obstacles. Innovative solutions such as specialized deep learning models, data augmentation strategies, and the pivotal role of interdisciplinary collaboration in dataset annotation emerge as vital contributions to this field. The analysis culminates in identifying theoretical and practical pathways for surmounting these barriers, highlighting the potential of deep learning technologies to revolutionize aphasia assessment and treatment. This review not only consolidates current knowledge but also charts a course for future research, emphasizing the need for comprehensive datasets, model optimization, and integration into clinical workflows to enhance patient care. Ultimately, this work underscores the transformative power of deep learning in advancing aphasia diagnosis, treatment, and support, heralding a new era of innovation and interdisciplinary collaboration in addressing this challenging disorder. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

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