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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2822

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Prof. Dr. Daniele Giansanti

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Centro Nazionale TISP, Istituto Superiore di Sanità, Rome, Italy
Interests: biomedical engineering; robotics; artificial intelligence; digital health; rehabilitation; smart technology; cybersecurity; mental health; animal-assisted therapy; social robotics; acceptance; diagnostic pathology and radiology; medical imaging; patient safety; healthcare quality; health assessment; chronic disease
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is reshaping healthcare, offering powerful tools for disease detection, diagnosis, prognosis, and medical data analysis. Beyond clinical imaging, AI is being applied to assess physiological and pathological states, support decision making, and optimize patient care. This Special Issue aims to explore AI’s broad impact across medicine and healthcare, highlighting its role in transforming diagnostics, prediction, and data-driven interventions.

We invite original research articles, systematic reviews, and technical contributions on AI applications in the following areas:

Disease Detection and Diagnosis: AI models for identifying, classifying, and characterizing diseases across medical disciplines, leveraging deep learning, computer vision, and natural language processing to enhance accuracy, efficiency, and early detection in diverse clinical settings;
Predictive Analytics and Prognosis: AI-driven approaches for risk stratification, outcome forecasting, and treatment optimization, utilizing machine learning and statistical modeling to personalize patient care, anticipate disease progression, and support clinical decision making;
Physiological and Pathological Status Assessment: AI applications in monitoring biological signals, analyzing biomarkers, and tracking disease progression, integrating real-time data from medical devices, biosensors, and imaging technologies for continuous health assessment;
Medical Data Processing and Multimodal Integration: AI applications in electronic health records (EHRs), imaging, genomics, wearable devices, and real-world medical data, enabling comprehensive data fusion, automated insights, and improved interoperability across healthcare systems;
AI in Personalized and Precision Medicine: Adaptive AI models for individualized patient care and therapeutic decision making, incorporating multi-omics data, patient-specific risk profiles, and AI-guided treatment strategies to optimize clinical outcomes;
Clinical Decision Support and Automation: AI-powered systems to enhance diagnostic accuracy, streamline workflows, and improve healthcare delivery, facilitating real-time decision making, automation of routine tasks, and integration into existing clinical infrastructures;
Applications of AI in Biomedical and Medical Images: Exploring AI techniques for image analysis, pattern recognition, anomaly detection, and automated interpretation, with implications for diagnostics, disease monitoring, and therapeutic planning across various medical fields.

Prof. Dr. Daniele Giansanti
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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
deep learning
healthcare
image segmentation
disease prediction
monitoring

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Published Papers (7 papers)

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23 pages, 3830 KiB

Open AccessArticle

A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening

by Emre Yalçın, Serpil Aslan, Mesut Toğaçar and Süleyman Cansun Demir

Diagnostics 2025, 15(12), 1444; https://doi.org/10.3390/diagnostics15121444 - 6 Jun 2025

Viewed by 280

Abstract

Background/Objectives: The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data—including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)—into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. Methods: The dataset consists of 958 anonymous patient records. Each record includes four first trimester screening markers, hCG, PAPP-A, and NT, expressed as multiples of the median. The DS risk outcome was categorized into three classes: high, medium, and low. Three transformer architectures—DeiT3, MaxViT, and Swin—are employed to extract high-level features from the generated barcodes. The extracted features are combined into a unified set, and dimensionality reduction is performed using two feature selection techniques: minimum Redundancy Maximum Relevance (mRMR) and RelieF. Intersecting features from both selectors are retained to form a compact and informative feature subset. The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. Results: The proposed approach achieved up to 100% classification accuracy using the Naive Bayes classifier with 1250 features selected by RelieF and 527 intersecting features from mRMR. By selecting a smaller but more informative subset of features, the system significantly reduced hardware and processing demands while maintaining strong predictive performance. Conclusions: The results suggest that the proposed hybrid AI method offers a promising and resource-efficient solution for DS risk assessment in first trimester screening. However, further comparative studies are recommended to validate its performance in broader clinical contexts. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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23 pages, 1370 KiB

Open AccessArticle

Machine Learning-Based Identification of Phonological Biomarkers for Speech Sound Disorders in Saudi Arabic-Speaking Children

by Deema F. Turki and Ahmad F. Turki

Diagnostics 2025, 15(11), 1401; https://doi.org/10.3390/diagnostics15111401 - 31 May 2025

Viewed by 340

Abstract

Background/Objectives: This study investigates the application of machine learning (ML) techniques in diagnosing speech sound disorders (SSDs) in Saudi Arabic-speaking children, with a specific focus on phonological biomarkers, particularly Infrequent Variance (InfrVar), to improve diagnostic accuracy. SSDs are a significant concern in pediatric speech pathology, affecting an estimated 10–15% of preschool-aged children worldwide. However, accurate diagnosis remains challenging, especially in linguistically diverse populations. Traditional diagnostic tools, such as the Percentage of Consonants Correct (PCC), often fail to capture subtle phonological variations. This study explores the potential of machine learning models to enhance diagnostic accuracy by incorporating culturally relevant phonological biomarkers like InfrVar, aiming to develop a more effective diagnostic approach for SSDs in Saudi Arabic-speaking children. Methods: Data from 235 Saudi Arabic-speaking children aged 2;6 to 5;11 years were analyzed using several machine learning models: Random Forest, Support Vector Machine (SVM), XGBoost, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes. The dataset was used to classify speech patterns into four categories: Atypical, Typical Development (TD), Articulation, and Delay. Phonological features such as Phonological Variance (PhonVar), InfrVar, and Percentage of Consonants Correct (PCC) were used as key variables. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contributions of individual features to model predictions. Results: The XGBoost and Random Forest models demonstrated the highest performance, with an accuracy of 91.49% and an AUC of 99.14%. SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. The K-Means clustering approach identified four distinct subgroups based on speech development patterns: TD (46.61%), Articulation (25.42%), Atypical (18.64%), and Delay (9.32%). Conclusions: Machine learning models, particularly XGBoost and Random Forest, effectively classified speech development categories in Saudi Arabic-speaking children. This study highlights the importance of incorporating culturally specific phonological biomarkers like InfrVar and PhonVar to improve diagnostic precision for SSDs. These findings lay the groundwork for the development of AI-assisted diagnostic tools tailored to diverse linguistic contexts, enhancing early intervention strategies in pediatric speech pathology. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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11 pages, 2541 KiB

Open AccessArticle

Predicting Early Outcomes of Prostatic Artery Embolization Using n-Butyl Cyanoacrylate Liquid Embolic Agent: A Machine Learning Study

by Burak Berksu Ozkara, David Bamshad, Ramita Gowda, Mert Karabacak, Vivian Bishay, Kirema Garcia-Reyes, Ardeshir R. Rastinehad, Dan Shilo and Aaron Fischman

Diagnostics 2025, 15(11), 1351; https://doi.org/10.3390/diagnostics15111351 - 28 May 2025

Viewed by 221

Abstract

Background/Objectives: Prostatic artery embolization (PAE) has been increasingly recognized, especially with recent progress in embolization techniques for the management of lower urinary tract symptoms due to benign prostatic hyperplasia. Nevertheless, a proportion of patients undergoing PAE fail to demonstrate clinical improvement. Machine learning models have the potential to provide valuable prognostic insights for patients undergoing PAE. Methods: A retrospective cohort study was performed utilizing a modified prior-data fitted network architecture to predict short-term (7 weeks) favorable outcomes, defined as a reduction greater than 9 points in the International Prostate Symptom Score (IPSS), in patients who underwent PAE with nBCA glue. Patients were stratified into two groups based on the median IPSS reduction value, and a binary classification model was developed to predict the outcome of interest. The model was developed using clinical tabular data, including both pre-procedural and intra-procedural variables. SHapley Additive ExPlanations (SHAP) were used to uncover the relative importance of features. Results: The final cohort included 109 patients. The model achieved an accuracy of 0.676, an MCC of 0.363, a precision of 0.666, a recall of 0.856, an F1-score of 0.731, and a Brier score of 0.203, with an AUPRC of 0.851 and an AUROC of 0.821. SHAP analysis identified pre-PAE IPSS, prior therapy, right embolization volume, preoperative quality of life, and age as the top five most influential features. Conclusions: Our model showed promising discrimination and calibration in predicting early outcomes of PAE with nBCA glue, highlighting the potential of precision medicine to deliver interpretable, individualized risk assessments. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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29 pages, 4889 KiB

Open AccessArticle

Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models

by Enrico Giarnieri, Elisabetta Carico, Stefania Scarpino, Alberto Ricci, Pierdonato Bruno, Simone Scardapane and Daniele Giansanti

Diagnostics 2025, 15(10), 1240; https://doi.org/10.3390/diagnostics15101240 - 14 May 2025

Viewed by 408

Abstract

Background: Malignant pleural effusions (MPEs) are common in advanced lung cancer patients. Cytological examination of pleural fluid is essential for identifying cell types but presents diagnostic challenges, particularly when reactive mesothelial cells mimic neoplastic cells. AI-powered diagnostic systems have emerged as valuable tools in digital cytopathology. This study explores the applicability of machine-learning (ML) models and highlights the importance of accessible tools for clinicians, enabling them to develop AI solutions and make advanced diagnostic tools available even in resource-limited settings. The focus is on differentiating normal/reactive cells from neoplastic cells in pleural effusions linked to lung adenocarcinoma. Methods: A dataset from the Cytopathology Unit at the Sant’Andrea University Hospital comprising 969 raw images, annotated with 3130 single mesothelial cells and 3260 adenocarcinoma cells, was categorized into two classes based on morphological features. Object-detection models were developed using YOLOv8 and the latest YOLOv11 instance segmentation models. Results: The models achieved an Intersection over Union (IoU) score of 0.72, demonstrating robust performance in class prediction for both categories, with YOLOv11 showing performance improvements over YOLOv8 in different metrics. Conclusions: The application of machine learning in cytopathology offers clinicians valuable support in differential diagnosis while also expanding their ability to engage with AI tools and methodologies. The diagnosis of MPEs is marked by substantial morphological and technical variability, underscoring the need for high-quality datasets and advanced deep-learning models. These technologies have the potential to enhance data interpretation and support more effective clinical treatment strategies in the era of precision medicine. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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12 pages, 567 KiB

Open AccessArticle

Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer

by Joseph Finkelstein, Aref Smiley, Christina Echeverria and Kathi Mooney

Diagnostics 2025, 15(8), 956; https://doi.org/10.3390/diagnostics15080956 - 9 Apr 2025

Viewed by 401

Abstract

Background/Objectives: Predicting symptom escalation during chemotherapy is crucial for timely interventions and improved patient outcomes. This study employs deep learning models to predict the deterioration of 12 self-reported symptoms, categorized into physical (e.g., nausea, fatigue, pain) and mental (e.g., feeling blue, trouble thinking) groups. Methods: The analytical dataset comprises daily self-reported symptom logs from individuals undergoing chemotherapy. To address class imbalance—where 84% of cases showed no escalation—symptoms were grouped into intervals of 3 to 7 days. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were trained on 80% of the data and evaluated on the remaining 20%. Results: Results showed that 3-day intervals yielded the best predictive performance. CNNs excelled in predicting physical symptoms, achieving 79.2% accuracy, 84.1% precision, 78.8% recall, and an F1 score of 81.4%. For mental symptoms, GRU outperformed other models, with an accuracy of 77.2%, precision of 71.6%, recall of 62.2%, and an F1 score of 66.6%. Performance declined for longer intervals due to reduced temporal resolution and fewer training samples, though CNNs and GRU remained relatively stable. Conclusions: The findings emphasize the advantage of categorizing symptoms for more tailored predictions and demonstrate the potential of deep learning in forecasting symptom escalation. Integrating these predictive models into clinical workflows could facilitate proactive symptom management, allowing timely interventions and enhanced patient care during chemotherapy. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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20 pages, 2958 KiB

Open AccessArticle

Advanced Assessment of Oxidative Stress and Inflammation in Military Personnel: Development of a Novel IIRPM Score Using Artificial Intelligence

by Florina-Diana Mihai, Emil-Tiberius Trasca, Patricia-Mihaela Radulescu, Razvan Mercut, Elena-Irina Caluianu, Eleonora Daniela Ciupeanu-Calugaru, Dan Marian Calafeteanu, Georgiana-Andreea Marinescu, Suzana Danoiu and Dumitru Radulescu

Diagnostics 2025, 15(7), 832; https://doi.org/10.3390/diagnostics15070832 - 25 Mar 2025

Cited by 1 | Viewed by 458

Abstract

Background/Objectives: The health of military personnel in modern operational settings is critical for sustaining defense readiness. Extended exposure to extreme conditions can cause oxidative stress and systemic inflammation, potentially affecting performance. To address this problem, we developed an innovative diagnostic tool, the Post-Mission Integrated Risk Index (IIRPM), which integrates hematologic markers with key clinical variables. A novel aspect of the approach is the incorporation of ΔNLR, thus quantifying the change in the neutrophil-to-lymphocyte ratio measured before and after deployment, thereby providing a sensitive indicator of the inflammatory impact of operational stress. Methods: In this retrospective study, we analyzed comprehensive clinical and biological data from 443 military personnel over a ten-year period, with measurements taken before and after missions. We applied robust statistical techniques, including paired t-tests and Pearson correlation analyses, to assess variations in hematologic and metabolic parameters. Data segmentation was performed using Gaussian mixture models, and the predictive performance of the resulting model was validated with a multi-layer perceptron (MLP) neural network. Results: The analysis revealed significant post-mission increases in both the baseline NLR and ΔNLR, accompanied by notable shifts in metabolic markers. Data segmentation identified three distinct profiles: a reference profile characterized by stable immunologic parameters, an acute inflammatory response profile, and a proinflammatory metabolic profile marked by elevated cholesterol levels and higher mean age. Remarkably, the MLP model achieved 100% accuracy on the test set, with an average cross-validation accuracy of 97%. Conclusions: The IIRPM—which incorporates ΔNLR, age, mission duration, and cholesterol levels—offers a novel strategy to assess inflammatory risk among military personnel, thus facilitating personalized preventive interventions. Further validation in multicenter and longitudinal studies is anticipated to consolidate the clinical utility of this tool, ultimately fostering a more adaptive approach in military medicine to address the complex challenges of modern operational theaters. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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26 pages, 718 KiB

Open AccessSystematic Review

Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review

by Qiaoyi Xu, Afzan Adam, Azizi Abdullah and Nurkhairul Bariyah

Diagnostics 2025, 15(9), 1150; https://doi.org/10.3390/diagnostics15091150 - 30 Apr 2025

Viewed by 447

Abstract

Background: Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection of lymphocytes and molecular biomarkers is essential for improving diagnostic precision and patient prognosis. Whole slide images (WSIs) are central to digital pathology workflows in breast cancer assessment. However, applying deep learning techniques to WSIs presents persistent challenges, including variability in image quality, limited availability of high-quality annotations, poor model interpretability, high computational demands, and suboptimal processing efficiency. Methods: This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), examines deep learning-based detection methods for breast cancer published between 2020 and 2024. The analysis includes 39 peer-reviewed studies and 20 widely used WSI datasets. Results: To enhance clinical relevance and guide model development, this study introduces a five-dimensional evaluation framework covering accuracy and performance, robustness and generalization, interpretability, computational efficiency, and annotation quality. The framework facilitates a balanced and clinically aligned assessment of both established methods and recent innovations. Conclusions: This review offers a comprehensive analysis and proposes a practical roadmap for addressing core challenges in WSI-based breast cancer detection. It fills a critical gap in the literature and provides actionable guidance for researchers, clinicians, and developers seeking to optimize and translate WSI-based technologies into clinical workflows for comprehensive breast cancer assessment. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)

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