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27 pages, 6632 KB

Open AccessArticle

A Study of COVID-19 Diagnosis Applying Artificial Intelligence to X-Rays Images

by Guilherme P. Cardim, Claudio B. Reis Neto, Eduardo S. Nascimento, Henrique P. Cardim, Wallace Casaca, Rogério G. Negri, Flávio C. Cabrera, Renivaldo J. dos Santos, Erivaldo A. da Silva and Mauricio Araujo Dias

Computers 2025, 14(5), 163; https://doi.org/10.3390/computers14050163 - 28 Apr 2025

Viewed by 955

Abstract

X-ray imaging, as a technique of non-destructive testing, has demonstrated considerable promise in COVID-19 diagnosis, particularly if supplemented with artificial intelligence (AI). Both radiologic technologists and AI researchers have raised the alarm about having to use increased doses of radiation in order to [...] Read more.

X-ray imaging, as a technique of non-destructive testing, has demonstrated considerable promise in COVID-19 diagnosis, particularly if supplemented with artificial intelligence (AI). Both radiologic technologists and AI researchers have raised the alarm about having to use increased doses of radiation in order to get more refined images and, hence, enhance diagnostic precision. In this research, we assess whether the disparity in exposure to the radiation dose considerably influences the credibility of AI-based diagnostic systems for COVID-19. A heterogeneous dataset of chest X-rays acquired at varying degrees of radiation exposure was run through four convolutional neural networks: VGG16, VGG19, ResNet50, and ResNet50V2. Results indicated above 91% accuracies, demonstrating that greater radiation exposure does not appreciably enhance diagnostic accuracy. Low radiation exposure sufficient to be utilized by human radiologists is therefore adequate for AI-based diagnosis. These findings are useful to the medical community, emphasizing that maximum diagnostic accuracy using AI does not need increased doses of radiation, thus further guaranteeing the safe application of X-ray imaging in COVID-19 diagnosis and possibly other medical and veterinary applications. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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23 pages, 2184 KB

Open AccessArticle

Lossless Compression of Malaria-Infected Erythrocyte Images Using Vision Transformer and Deep Autoencoders

by Md Firoz Mahmud, Zerin Nusrat and W. David Pan

Computers 2025, 14(4), 127; https://doi.org/10.3390/computers14040127 - 1 Apr 2025

Viewed by 892

Abstract

Lossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malaria in resource-limited regions. This paper presents a [...] Read more.

Lossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malaria in resource-limited regions. This paper presents a novel machine learning-based approach where lossless compression of malaria-infected erythrocyte images is assisted by cutting-edge classifiers. To this end, we first use a Vision Transformer to classify images into two categories: those cells that are infected with malaria and those that are not. We then employ distinct deep autoencoders for each category, which not only reduces the dimensions of the image data but also preserves crucial diagnostic information. To ensure no loss in reconstructed image quality, we further compress the residuals produced by these autoencoders using the Huffman code. Simulation results show that the proposed method achieves lower overall bit rates and thus higher compression ratios than traditional compression schemes such as JPEG 2000, JPEG-LS, and CALIC. This strategy holds significant potential for effective telemedicine applications and can improve diagnostic capabilities in regions impacted by malaria. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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20 pages, 2428 KB

Open AccessArticle

Combining Smartphone Inertial Sensors and Machine Learning Algorithms to Estimate Power Variables in Standing Long Jump

by Beatrice De Lazzari, Giuseppe Vannozzi and Valentina Camomilla

Computers 2025, 14(2), 31; https://doi.org/10.3390/computers14020031 - 21 Jan 2025

Viewed by 1283

Abstract

Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was [...] Read more.

Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was recruited and performed a SLJ task while holding a smartphone, whose inertial sensors were used to collect data. Considering the state-of-the-art in SLJ biomechanics, a set of features was extracted and then selected by Lasso regression and used as inputs to several different optimized machine learning architectures to estimate the SLJ power variables. A Multi-Layer Perceptron Regressor was selected as the best-performing model to estimate total and concentric antero-posterior mean power, with an RMSE of 0.37 W/kg, R² > 0.70, and test phase homoscedasticity (Kendall’s τ < 0.1) in both cases. Model performance was dependent on the dataset size rather than the participants’ sex. A Multi-Layer Perceptron Regressor was able to also estimate the antero-posterior peak power (RMSE = 2.34 W/kg; R² = 0.67), although affected by heteroscedasticity. This study proved the feasibility of combining low-cost smartphone sensors and machine learning to automatically and objectively estimate SLJ power variables in ecological settings. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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29 pages, 4651 KB

Open AccessArticle

Hybrid Vision Transformer and Convolutional Neural Network for Multi-Class and Multi-Label Classification of Tuberculosis Anomalies on Chest X-Ray

by Rizka Yulvina, Stefanus Andika Putra, Mia Rizkinia, Arierta Pujitresnani, Eric Daniel Tenda, Reyhan Eddy Yunus, Dean Handimulya Djumaryo, Prasandhya Astagiri Yusuf and Vanya Valindria

Computers 2024, 13(12), 343; https://doi.org/10.3390/computers13120343 - 17 Dec 2024

Cited by 2 | Viewed by 5498

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global mortality. While TB detection can be performed through chest X-ray (CXR) analysis, numerous studies have leveraged AI to automate and enhance the diagnostic process. However, existing approaches often focus on partial [...] Read more.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global mortality. While TB detection can be performed through chest X-ray (CXR) analysis, numerous studies have leveraged AI to automate and enhance the diagnostic process. However, existing approaches often focus on partial or incomplete lesion detection, lacking comprehensive multi-class and multi-label solutions for the full range of TB-related anomalies. To address this, we present a hybrid AI model combining vision transformer (ViT) and convolutional neural network (CNN) architectures for efficient multi-class and multi-label classification of 14 TB-related anomalies. Using 133 CXR images from Dr. Cipto Mangunkusumo National Central General Hospital and 214 images from the NIH datasets, we tackled data imbalance with augmentation, class weighting, and focal loss. The model achieved an accuracy of 0.911, a loss of 0.285, and an AUC of 0.510. Given the complexity of handling not only multi-class but also multi-label data with imbalanced and limited samples, the AUC score reflects the challenging nature of the task rather than any shortcoming of the model itself. By classifying the most distinct TB-related labels in a single AI study, this research highlights the potential of AI to enhance both the accuracy and efficiency of detecting TB-related anomalies, offering valuable advancements in combating this global health burden. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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15 pages, 657 KB

Open AccessArticle

Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification

by Hai-Long Nguyen, Van Su Pham and Hai-Chau Le

Computers 2024, 13(12), 316; https://doi.org/10.3390/computers13120316 - 27 Nov 2024

Cited by 5 | Viewed by 1672

Abstract

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The [...] Read more.

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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12 pages, 8479 KB

Open AccessArticle

Automated Generation of Lung Cytological Images from Image Findings Using Text-to-Image Technology

by Atsushi Teramoto, Yuka Kiriyama, Ayano Michiba, Natsuki Yazawa, Tetsuya Tsukamoto, Kazuyoshi Imaizumi and Hiroshi Fujita

Computers 2024, 13(11), 303; https://doi.org/10.3390/computers13110303 - 19 Nov 2024

Cited by 1 | Viewed by 1172

Abstract

Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of [...] Read more.

Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of images without bias. However, this is often challenging in practice because of the epidemiologic bias of cancer types and cellular characteristics. The main aim of this study was to develop a method to generate cytological diagnostic images from image findings using text-to-image technology in order to generate diverse images. In the proposed method, we collected Papanicolaou-stained specimens derived from the lung cells of 135 lung cancer patients, from which we extracted 472 patch images. Descriptions of the corresponding findings for these patch images were compiled to create a data set. This dataset was then utilized to finetune the Stable Diffusion (SD) v1 and v2 models. The cell images generated by this method closely resemble real images, and both cytotechnologists and cytopathologists provided positive subjective evaluations. Furthermore, SDv2 demonstrated shapes and contours of nuclei and cytoplasm that were more similar to real images compared to SDv1, showing superior performance in quantitative evaluation metrics. When the generated images were utilized in the classification tasks for cytological images, there was an improvement in classification performance. These results indicate that the proposed method may be effective for generating high-quality cytological images, which enables the image classification model to learn diverse features, thereby improving classification performance. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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14 pages, 2039 KB

Open AccessArticle

Deep Learning Based Breast Cancer Detection Using Decision Fusion

by Doğu Manalı, Hasan Demirel and Alaa Eleyan

Computers 2024, 13(11), 294; https://doi.org/10.3390/computers13110294 - 14 Nov 2024

Cited by 7 | Viewed by 3567

Abstract

Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer [...] Read more.

Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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24 pages, 1353 KB

Open AccessArticle

Application of Deep Learning for Heart Attack Prediction with Explainable Artificial Intelligence

by Elias Dritsas and Maria Trigka

Computers 2024, 13(10), 244; https://doi.org/10.3390/computers13100244 - 25 Sep 2024

Cited by 9 | Viewed by 5299

Abstract

Heart disease remains a leading cause of mortality worldwide, and the timely and accurate prediction of heart attack is crucial yet challenging due to the complexity of the condition and the limitations of traditional diagnostic methods. These challenges include the need for resource-intensive [...] Read more.

Heart disease remains a leading cause of mortality worldwide, and the timely and accurate prediction of heart attack is crucial yet challenging due to the complexity of the condition and the limitations of traditional diagnostic methods. These challenges include the need for resource-intensive diagnostics and the difficulty in interpreting complex predictive models in clinical settings. In this study, we apply and compare the performance of five well-known Deep Learning (DL) models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a Hybrid model, to a heart attack prediction dataset. Each model was properly tuned and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC) as performance metrics. Additionally, by integrating an Explainable Artificial intelligence (XAI) technique, specifically Shapley Additive Explanations (SHAP), we enhance the interpretability of the predictions, making them actionable for healthcare professionals and thereby enhancing clinical applicability. The experimental results revealed that the Hybrid model prevailed, achieving the highest performance across all metrics. Specifically, the Hybrid model attained an accuracy of 91%, precision of 89%, recall of 90%, F1-score of 89%, and an AUC of 0.95. These results highlighted the Hybrid model’s superior ability to predict heart attacks, attributed to its efficient handling of sequential data and long-term dependencies. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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13 pages, 3622 KB

Open AccessArticle

Assessing the Impact of Prolonged Sitting and Poor Posture on Lower Back Pain: A Photogrammetric and Machine Learning Approach

by Valentina Markova, Miroslav Markov, Zornica Petrova and Silviya Filkova

Computers 2024, 13(9), 231; https://doi.org/10.3390/computers13090231 - 14 Sep 2024

Cited by 15 | Viewed by 19709

Abstract

Prolonged static sitting at the workplace is considered one of the main risks for the development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar [...] Read more.

Prolonged static sitting at the workplace is considered one of the main risks for the development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar discomfort and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. The current study focused on evaluating the relationship between LBP and prolonged sitting in poor posture using photogrammetric images, postural angle calculation, machine learning models, and questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed to recognize poor body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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21 pages, 3742 KB

Open AccessArticle

A Framework for Cleaning Streaming Data in Healthcare: A Context and User-Supported Approach

by Obaid Alotaibi, Sarath Tomy and Eric Pardede

Computers 2024, 13(7), 175; https://doi.org/10.3390/computers13070175 - 16 Jul 2024

Viewed by 2195

Abstract

Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, [...] Read more.

Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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Review

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19 pages, 1146 KB

Open AccessReview

Exploring Deep Learning Model Opportunities for Cervical Cancer Screening in Vulnerable Public Health Regions

by Renan Chaves de Lima and Juarez Antonio Simões Quaresma

Computers 2025, 14(5), 202; https://doi.org/10.3390/computers14050202 - 21 May 2025

Viewed by 2206

Abstract

Deep learning models offer innovative solutions for cervical cancer screening in vulnerable regions such as the Brazilian Amazon. These tools are particularly relevant in areas with limited access to healthcare services, where the high prevalence of the disease severely affects riverine and indigenous [...] Read more.

Deep learning models offer innovative solutions for cervical cancer screening in vulnerable regions such as the Brazilian Amazon. These tools are particularly relevant in areas with limited access to healthcare services, where the high prevalence of the disease severely affects riverine and indigenous populations. Artificial intelligence can overcome the limitations of traditional screening methods, providing faster and more accurate diagnoses. This enables early disease detection and reduces mortality, improving equitable access to healthcare. Furthermore, the application of these technologies complements global efforts to eliminate cervical cancer, aligning with the WHO strategies. This review emphasizes the need for model adaptation to local realities, which is essential to ensure their effectiveness in low-infrastructure areas, reinforcing their potential to reduce health disparities and expand access to quality diagnostics. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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28 pages, 1521 KB

Open AccessReview

Advancing Predictive Healthcare: A Systematic Review of Transformer Models in Electronic Health Records

by Azza Mohamed, Reem AlAleeli and Khaled Shaalan

Computers 2025, 14(4), 148; https://doi.org/10.3390/computers14040148 - 14 Apr 2025

Viewed by 4066

Abstract

This systematic study seeks to evaluate the use and impact of transformer models in the healthcare domain, with a particular emphasis on their usefulness in tackling key medical difficulties and performing critical natural language processing (NLP) functions. The research questions focus on how [...] Read more.

This systematic study seeks to evaluate the use and impact of transformer models in the healthcare domain, with a particular emphasis on their usefulness in tackling key medical difficulties and performing critical natural language processing (NLP) functions. The research questions focus on how these models can improve clinical decision-making through information extraction and predictive analytics. Our findings show that transformer models, especially in applications like named entity recognition (NER) and clinical data analysis, greatly increase the accuracy and efficiency of processing unstructured data. Notably, case studies demonstrated a 30% boost in entity recognition accuracy in clinical notes and a 90% detection rate for malignancies in medical imaging. These contributions emphasize the revolutionary potential of transformer models in healthcare, and therefore their importance in enhancing resource management and patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as the reliance on restricted datasets and the need for data format standardization, and provides a road map for future research to improve the applicability and performance of these models in real-world clinical settings. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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37 pages, 353 KB

Open AccessReview

A State-of-the-Art Review of Artificial Intelligence (AI) Applications in Healthcare: Advances in Diabetes, Cancer, Epidemiology, and Mortality Prediction

by Mariano Vargas-Santiago, Diana Assaely León-Velasco, Christian Efraín Maldonado-Sifuentes and Liliana Chanona-Hernandez

Computers 2025, 14(4), 143; https://doi.org/10.3390/computers14040143 - 10 Apr 2025

Cited by 5 | Viewed by 4963

Abstract

Artificial Intelligence (AI) methodologies have profoundly influenced healthcare research, particularly in chronic disease management and public health. This paper provides a comprehensive state-of-the-art review of AI’s applications across diabetes, cancer, epidemiology, and mortality prediction. The analysis highlights advancements in machine learning (ML), deep [...] Read more.

Artificial Intelligence (AI) methodologies have profoundly influenced healthcare research, particularly in chronic disease management and public health. This paper provides a comprehensive state-of-the-art review of AI’s applications across diabetes, cancer, epidemiology, and mortality prediction. The analysis highlights advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) that enable robust predictive models and decision support systems, leading to significant clinical and public health outcomes. The study examines predictive modeling, pattern recognition, and decision support applications, addressing their respective challenges and potential in real-world healthcare settings. Emphasis is placed on the emerging role of explainable AI (XAI), multimodal data fusion, and privacy-preserving techniques such as federated learning, which aim to enhance interpretability, robustness, and ethical compliance. This paper underscores the vital role of interdisciplinary collaboration and adaptive AI systems in creating resilient, scalable, and patient-centric healthcare solutions. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

23 pages, 323 KB

Open AccessReview

Advancements in Parkinson’s Disease Diagnosis: A Comprehensive Survey on Biomarker Integration and Machine Learning

by Ruchira Pratihar and Ravi Sankar

Computers 2024, 13(11), 293; https://doi.org/10.3390/computers13110293 - 14 Nov 2024

Cited by 7 | Viewed by 4604

Abstract

This comprehensive review explores the advancements in machine learning algorithms in the diagnosis of Parkinson’s disease (PD) utilizing different biomarkers. It addresses the challenges in the assessment of PD for accurate diagnosis, treatment decisions, and patient care due to difficulties in early and [...] Read more.

This comprehensive review explores the advancements in machine learning algorithms in the diagnosis of Parkinson’s disease (PD) utilizing different biomarkers. It addresses the challenges in the assessment of PD for accurate diagnosis, treatment decisions, and patient care due to difficulties in early and differential diagnosis, subjective clinical assessments, symptom variability, limited objective biomarkers, comorbidity impacts, uneven access to specialized care, and gaps in clinical research. This review provides a detailed review of ongoing biomarker research, technological advancements for objective assessment, and enhanced healthcare infrastructure. It presents a comprehensive evaluation of the use of diverse biomarkers for diagnosing Parkinson’s disease (PD) across various datasets, utilizing machine learning models. Recent research findings are summarized in tables, showcasing key methodologies such as data preprocessing, feature selection, and classification techniques. This review also explores the performance, benefits, and limitations of different diagnostic approaches, providing valuable insights into their effectiveness in PD diagnosis. Moreover, the review addresses the integration of multimodal biomarkers, combining data from different sources to enhance diagnostic accuracy, and disease monitoring. Challenges such as data heterogeneity, variability in symptom progression, and model generalizability are discussed alongside emerging trends and future directions in the field. Ultimately, the application of machine learning (ML) in leveraging diverse biomarkers offers promising avenues for advancing PD diagnosis, paving the way for personalized treatment strategies and improving patient outcomes. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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