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Algorithms
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Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction...
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Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 12504

Special Issue Editors

Dr. Umberto Michelucci

E-Mail Website
Guest Editor
1. TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland
2. Department of Computer Science, Lucerne University of Applied Sciences and Arts, CH- 6002 Luzern, Switzerland
Interests: machine learning; deep learning; neural networks; sensors; optics; oxygen sensing; mathematics
Dr. Michela Sperti

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, 10129 Turin, Italy
Interests: biomedical engineering; artificial intelligence; computational biology

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research to this Special Issue of Algorithms, entitled “Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution”.

In recent years, artificial intelligence (in particular machine learning and deep learning) has revolutionized the world of healthcare in all aspects (e.g., diagnosis, prognosis, and treatment), augmenting the doctors’ knowledge and supporting them in their daily decisions. However, the application of artificial intelligence to healthcare is new and non-trivial, and many issues still need to be solved (data sharing privacy issues, interpretability and trustworthiness of models, lack of data, data sparsity, etc.). Moreover, and specifically to the proposed Special Issue, an impactful application of artificial intelligence in the healthcare domain depends heavily on the effective application of algorithms that deal with time dependence of patients’ variables and pattern identification in heterogeneous datasets(e.g., pathology dynamics, follow-up visits events prediction, marker recognition, featurization, evolution and dynamics in signals and images, etc.). This is one of the largest challenges within the field to be undertaken, and has without doubt received inadequate consideration to date.

In the present Special Issue, we are looking for inspiring approaches for based on Machine Learning and/or Deep Learning models to a wide range of clinical use-cases. We invite high-quality papers that address both theoretical and practical issues involving the development of computational models that may be even already established in other fields, but with a potential and novel impact in the healthcare sector. Potential topics include, but are not limited to: supervised approaches to estimate disease trajectories; time-to-event predictions and survival analysis; unsupervised approaches applied to study dynamical clinical patterns; pattern recognition and markers detection by Machine Learning-driven comparative analysis; personalized screening and monitoring; early diagnosis; or treatment effects over time.

Finally, I would like to thank Ms. Michela Sperti and her valuable work for assisting me with this Special Issue.

Dr. Umberto Michelucci
Dr. Michela Sperti
Guest Editors

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. Algorithms 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 1600 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

  • artificial intelligence
  • machine learning
  • deep learning
  • healthcare
  • pathology dynamics
  • survival analysis
  • time-to-event prediction
  • in silico medicine
  • precision medicine
  • bioengineering
  • electronic health records
  • biomarkers

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Published Papers (7 papers)

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28 pages, 7535 KiB  
Article
A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI
by Hanane Dihmani, Abdelmajid Bousselham and Omar Bouattane
Algorithms 2024, 17(10), 462; https://doi.org/10.3390/a17100462 - 18 Oct 2024
Viewed by 1135
Abstract
Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its [...] Read more.
Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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17 pages, 904 KiB  
Article
Explainable Machine Learning Model for Chronic Kidney Disease Prediction
by Muhammad Shoaib Arif, Ateeq Ur Rehman and Daniyal Asif
Algorithms 2024, 17(10), 443; https://doi.org/10.3390/a17100443 - 3 Oct 2024
Viewed by 1437
Abstract
More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has [...] Read more.
More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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35 pages, 2715 KiB  
Article
Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases
by Virginia Sandulescu, Marilena Ianculescu, Liudmila Valeanu and Adriana Alexandru
Algorithms 2024, 17(9), 376; https://doi.org/10.3390/a17090376 - 23 Aug 2024
Cited by 1 | Viewed by 1106
Abstract
Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these [...] Read more.
Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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20 pages, 3783 KiB  
Article
Artificial Intelligence-Based System for Retinal Disease Diagnosis
by Ekaterina V. Orlova
Algorithms 2024, 17(7), 315; https://doi.org/10.3390/a17070315 - 18 Jul 2024
Viewed by 1226
Abstract
The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis [...] Read more.
The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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23 pages, 13655 KiB  
Article
Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications
by Albina V. Lebedeva, Margarita I. Samburova, Vyacheslav V. Razin, Nikolay V. Gromov, Svetlana A. Gerasimova, Tatiana A. Levanova, Lev A. Smirnov and Alexander N. Pisarchik
Algorithms 2024, 17(6), 252; https://doi.org/10.3390/a17060252 - 7 Jun 2024
Viewed by 1354
Abstract
The increasing growth in knowledge about the functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technology developments in recent decades, has led to the emergence of a large number of brain–computer interfaces and neuroprosthetics for regenerative [...] Read more.
The increasing growth in knowledge about the functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technology developments in recent decades, has led to the emergence of a large number of brain–computer interfaces and neuroprosthetics for regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of the restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. The hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for the prediction of hippocampal signals in the CA1 region based on received biological input in the CA3 region. We compare the results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. The proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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25 pages, 1190 KiB  
Article
Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset
by Pablo Caballero, Luis Gonzalez-Abril, Juan A. Ortega and Áurea Simon-Soro
Algorithms 2024, 17(3), 108; https://doi.org/10.3390/a17030108 - 4 Mar 2024
Viewed by 2454
Abstract
Endometriosis (EM) is a chronic inflammatory estrogen-dependent disorder that affects 10% of women worldwide. It affects the female reproductive tract and its resident microbiota, as well as distal body sites that can serve as surrogate markers of EM. Currently, no single definitive biomarker [...] Read more.
Endometriosis (EM) is a chronic inflammatory estrogen-dependent disorder that affects 10% of women worldwide. It affects the female reproductive tract and its resident microbiota, as well as distal body sites that can serve as surrogate markers of EM. Currently, no single definitive biomarker can diagnose EM. For this pilot study, we analyzed a cohort of 21 patients with endometriosis and infertility-associated conditions. A microbiome dataset was created using five sample types taken from the reproductive and gastrointestinal tracts of each patient. We evaluated several machine learning algorithms for EM detection using these features. The characteristics of the dataset were derived from endometrial biopsy, endometrial fluid, vaginal, oral, and fecal samples. Despite limited data, the algorithms demonstrated high performance with respect to the F1 score. In addition, they suggested that disease diagnosis could potentially be improved by using less medically invasive procedures. Overall, the results indicate that machine learning algorithms can be useful tools for diagnosing endometriosis in low-resource settings where data availability and availability are limited. We recommend that future studies explore the complexities of the EM disorder using artificial intelligence and prediction modeling to further define the characteristics of the endometriosis phenotype. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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32 pages, 2034 KiB  
Systematic Review
Artificial Intelligence-Based Algorithms and Healthcare Applications of Respiratory Inductance Plethysmography: A Systematic Review
by Md. Shahidur Rahman, Sowrav Chowdhury, Mirza Rasheduzzaman and A. B. M. S. U. Doulah
Algorithms 2024, 17(6), 261; https://doi.org/10.3390/a17060261 - 14 Jun 2024
Cited by 1 | Viewed by 2111
Abstract
Respiratory Inductance Plethysmography (RIP) is a non-invasive method for the measurement of respiratory rates and lung volumes. Accurate detection of respiratory rates and volumes is crucial for the diagnosis and monitoring of prognosis of lung diseases, for which spirometry is classically used in [...] Read more.
Respiratory Inductance Plethysmography (RIP) is a non-invasive method for the measurement of respiratory rates and lung volumes. Accurate detection of respiratory rates and volumes is crucial for the diagnosis and monitoring of prognosis of lung diseases, for which spirometry is classically used in clinical applications. RIP has been studied as an alternative to spirometry and shown promising results. Moreover, RIP data can be analyzed through machine learning (ML)-based approaches for some other purposes, i.e., detection of apneas, work of breathing (WoB) measurement, and recognition of human activity based on breathing patterns. The goal of this study is to provide an in-depth systematic review of the scope of usage of RIP and current RIP device developments, as well as to evaluate the performance, usability, and reliability of ML-based data analysis techniques within its designated scope while adhering to the PRISMA guidelines. This work also identifies research gaps in the field and highlights the potential scope for future work. The IEEE Explore, Springer, PLoS One, Science Direct, and Google Scholar databases were examined, and 40 publications were included in this work through a structured screening and quality assessment procedure. Studies with conclusive experimentation on RIP published between 2012 and 2023 were included, while unvalidated studies were excluded. The findings indicate that RIP is an effective method to a certain extent for testing and monitoring respiratory functions, though its accuracy is lacking in some settings. However, RIP possesses some advantages over spirometry due to its non-invasive nature and functionality for both stationary and ambulatory uses. RIP also demonstrates its capabilities in ML-based applications, such as detection of breathing asynchrony, classification of apnea, identification of sleep stage, and human activity recognition (HAR). It is our conclusion that, though RIP is not yet ready to replace spirometry and other established methods, it can provide crucial insights into subjects’ condition associated to respiratory illnesses. The implementation of artificial intelligence (AI) could play a potential role in improving the overall effectiveness of RIP, as suggested in some of the selected studies. Full article
(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)
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