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Diagnostics
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A New Era in Diagnosis: From Biomarkers to Artificial Intelligence
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A New Era in Diagnosis: From Biomarkers to Artificial Intelligence

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 June 2025 | Viewed by 9164

Special Issue Editors

Prof. Dr. Tudor Drugan

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Guest Editor
Department of Medical Informatics and Biostatistics, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, Louis Pasteur Str., No. 6, 400349 Cluj-Napoca, Romania
Interests: medical research methodology; biostatistics; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Leucuta

E-Mail Website
Guest Editor
Department of Medical Informatics and Biostatistics, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, Louis Pasteur Str., No. 6, 400349 Cluj-Napoca, Romania
Interests: medical research methodology; biostatistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Exploring new diagnostic approaches utilizing biomarkers and artificial intelligence (AI) has become increasingly significant in recent years. In fact, technologies such as AI and machine learning possess potential power to change the diagnosis of cancer treatment and identify predictive biomarkers.

The scope of the Special Issue includes developing multimodal machine learning, a subfield of machine learning that works on the development and training of models to leverage the potential of various data sources—genomic, proteomic, imaging data—to improve their predictability performance. It has the advantage of being able to integrate different data modalities. For example, imaging data can be converted into data in sound form to make some of the earlier systems differentiate between malignant and benign lesions more effectively. This integration of multimodal clinical data into a cohesive AI model represents a significant step toward making more holistic representations of clinical data.

In addition to AI's role in integrating multimodal data, there is a fundamental shift toward using quantitative digital data in clinical research, leveraging the massive amounts of biomarker data generated worldwide. AI, particularly machine learning and high-performance computing, has been identified as uniquely capable of combining vast datasets from genomics, proteomics and other 'omics' technologies. This shift enables the establishment of novel therapies and predictive models of drug response, moving beyond the concept of a single biomarker to utilizing combinations of biomarkers for enhanced diagnostic accuracy and treatment decisions.

In addition, most biomarker discovery for diseases has relied on using methodologies in AI toward obtaining predictive biomarkers or scores to accelerate the development of diagnosis and treatment. Using supervised and non-supervised machine learning algorithms while analyzing vast datasets without any bias is evident in the identification of novel biomarker candidates. Medicines for different illnesses have an unmet need for biomarker discovery, highlighting that AI will have a high impact in predictive diagnostics and therapeutic strategies.

These breakthroughs reinforce the critical role of AI and machine learning in enhancing understanding and capacity toward the proper diagnosis and treatment of diseases. These are necessary in an era of precision medicine, in which data-driven insights steer health solutions into more accurate and effective directions.

Prof. Dr. Tudor Drugan
Dr. Daniel Leucuta
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. 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

  • diagnosis
  • precision medicine
  • predictive diagnostics
  • therapeutic strategies
  • biomarkers
  • artificial intelligence
  • machine learning

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Published Papers (6 papers)

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Research

36 pages, 2561 KiB  
Article
A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students
by Flórián Gubics, Ádám Nagy, József Dombi, Antónia Pálfi, Zoltán Szabó, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki and István Szendi
Diagnostics 2025, 15(4), 454; https://doi.org/10.3390/diagnostics15040454 - 13 Feb 2025
Viewed by 888
Abstract
Background: Early and accurate diagnosis is crucial for effective prevention and treatment of severe mental illnesses, such as schizophrenia and bipolar disorder. However, identifying these conditions in their early stages remains a significant challenge. Our goal was to develop a method capable [...] Read more.
Background: Early and accurate diagnosis is crucial for effective prevention and treatment of severe mental illnesses, such as schizophrenia and bipolar disorder. However, identifying these conditions in their early stages remains a significant challenge. Our goal was to develop a method capable of detecting latent disease liability in healthy volunteers. Methods: Using questionnaires examining affective temperament and schizotypal traits among voluntary, healthy university students (N = 710), we created three groups. These were a group characterized by an emphasis on positive schizotypal traits (N = 20), a group showing cyclothymic temperament traits (N = 17), and a control group showing no susceptibility in either direction (N = 21). We performed a resting-state EEG examination as part of a complex psychological, electrophysiological, psychophysiological, and laboratory battery, and we developed feature-selection machine-learning methods to differentiate the low-risk groups. Results: Both low-risk groups could be reliably (with 90% accuracy) separated from the control group. Conclusions: Models applied to the data allowed us to differentiate between healthy university students with latent schizotypal or bipolar tendencies. Our research may improve the sensitivity and specificity of risk-state identification, leading to more effective and safer secondary prevention strategies for individuals in the prodromal phases of these disorders. Full article
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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20 pages, 5206 KiB  
Article
Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters
by Mehrnaz Saghab Torbati, Ahmad Zandbagleh, Mohammad Reza Daliri, Amirmasoud Ahmadi, Reza Rostami and Reza Kazemi
Diagnostics 2025, 15(3), 316; https://doi.org/10.3390/diagnostics15030316 - 29 Jan 2025
Viewed by 1436
Abstract
Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide [...] Read more.
Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Methods: Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Results: Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. Conclusions: This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis. Full article
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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25 pages, 1685 KiB  
Article
Federated Learning with Privacy Preserving for Multi- Institutional Three-Dimensional Brain Tumor Segmentation
by Mohammed Elbachir Yahiaoui, Makhlouf Derdour, Rawad Abdulghafor, Sherzod Turaev, Mohamed Gasmi, Akram Bennour, Abdulaziz Aborujilah and Mohamed Al Sarem
Diagnostics 2024, 14(24), 2891; https://doi.org/10.3390/diagnostics14242891 - 23 Dec 2024
Cited by 3 | Viewed by 1368
Abstract
Background and Objectives: Brain tumors are complex diseases that require careful diagnosis and treatment. A minor error in the diagnosis may easily lead to significant consequences. Thus, one must place a premium on accurately identifying brain tumors. However, deep learning (DL) models often [...] Read more.
Background and Objectives: Brain tumors are complex diseases that require careful diagnosis and treatment. A minor error in the diagnosis may easily lead to significant consequences. Thus, one must place a premium on accurately identifying brain tumors. However, deep learning (DL) models often face challenges in obtaining sufficient medical imaging data due to legal, privacy, and technical barriers hindering data sharing between institutions. This study aims to implement a federated learning (FL) approach with privacy-preserving techniques (PPTs) directed toward segmenting brain tumor lesions in a distributed and privacy-aware manner.Methods: The suggested approach employs a model of 3D U-Net, which is trained using federated learning on the BraTS 2020 dataset. PPTs, such as differential privacy, are included to ensure data confidentiality while managing privacy and heterogeneity challenges with minimal communication overhead. The efficiency of the model is measured in terms of Dice similarity coefficients (DSCs) and 95% Hausdorff distances (HD95) concerning the target areas concerned by tumors, which include the whole tumor (WT), tumor core (TC), and enhancing tumor core (ET). Results: In the validation phase, the partial federated model achieved DSCs of 86.1%, 83.3%, and 79.8%, corresponding to 95% values of 25.3 mm, 8.61 mm, and 9.16 mm for WT, TC, and ET, respectively. On the final test set, the model demonstrated improved performance, achieving DSCs of 89.85%, 87.55%, and 86.6%, with HD95 values of 22.95 mm, 8.68 mm, and 8.32 mm for WT, TC, and ET, respectively, which indicates the effectiveness of the segmentation approach, and its privacy preservation.Conclusion: This study presents a highly competitive, collaborative federated learning model with PPTs that can successfully segment brain tumor lesions without compromising patient data confidentiality. Future work will improve model generalizability and extend the framework to other medical imaging tasks. Full article
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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19 pages, 2207 KiB  
Article
Exploring Voice Acoustic Features Associated with Cognitive Status in Korean Speakers: A Preliminary Machine Learning Study
by Jiho Lee, Nayeon Kim, Ji-Wan Ha, Kyunghun Kang, Eunhee Park, Janghyeok Yoon and Ki-Su Park
Diagnostics 2024, 14(24), 2837; https://doi.org/10.3390/diagnostics14242837 - 17 Dec 2024
Viewed by 969
Abstract
Objective: To develop a non-invasive cognitive impairment detection system using speech data analysis, addressing the growing global dementia crisis and enabling accessible early screening through daily health monitoring. Methods: Speech data from 223 Korean patients were collected across eight tasks. Patients [...] Read more.
Objective: To develop a non-invasive cognitive impairment detection system using speech data analysis, addressing the growing global dementia crisis and enabling accessible early screening through daily health monitoring. Methods: Speech data from 223 Korean patients were collected across eight tasks. Patients were classified based on Korean Mini-Mental State Examination scores. Four machine learning models were tested for three binary classification tasks. Voice acoustic features were extracted and analyzed. Results: The Deep Neural Network model performed best in two classification tasks, with Precision-Recall Area Under the Curve scores of 0.737 for severe vs. no impairment and 0.726 for mild vs. no impairment, while Random Forest achieved 0.715 for severe + mild vs. no impairment. Several acoustic features emerged as potentially important indicators, with DDA shimmer from the /i/ task and stdevF0 from the /puh-tuh-kuh/ task showing consistent patterns across classification tasks. Conclusions: This preliminary study suggests that certain acoustic features may be associated with cognitive status, though demographic factors significantly influence these relationships. Further research with demographically matched populations is needed to validate these findings. Full article
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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28 pages, 6322 KiB  
Article
Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms
by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar and Punit Fulzele
Diagnostics 2024, 14(22), 2514; https://doi.org/10.3390/diagnostics14222514 - 9 Nov 2024
Cited by 2 | Viewed by 2345
Abstract
Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Leveraging the power of machine learning, this paper presents a systematic approach to [...] Read more.
Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. These factors include demographic attributes, medical history, lifestyle elements, and physiological metrics. Method: An effective random sampling method is proposed to handle the highly biased data of stroke. The stroke pre-diction using optimized boosting machine learning algorithms is supported with explainable AI using LIME and SHAP. This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. Results: The performance of three boosting algorithms is studied for stroke prediction, which include Gradient Boosting (GB), AdaBoost (ADB), and XGBoost (XGB) with XGB achieved the best outcome overall with a training accuracy of 96.97% and testing accuracy of 92.13%. Conclusions: Through this approach, the study seeks to uncover actionable insights to guide healthcare practitioners in devising personalized treatment strategies for stroke patients. Full article
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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21 pages, 3702 KiB  
Article
Enhancing Influenza Detection through Integrative Machine Learning and Nasopharyngeal Metabolomic Profiling: A Comprehensive Study
by Md. Shaheenur Islam Sumon, Md Sakib Abrar Hossain, Haya Al-Sulaiti, Hadi M. Yassine and Muhammad E. H. Chowdhury
Diagnostics 2024, 14(19), 2214; https://doi.org/10.3390/diagnostics14192214 - 4 Oct 2024
Cited by 1 | Viewed by 1286
Abstract
Background/Objectives: Nasal and nasopharyngeal swabs are commonly used for detecting respiratory viruses, including influenza, which significantly alters host cell metabolites. This study aimed to develop a machine learning model to identify biomarkers that differentiate between influenza-positive and -negative cases using clinical metabolomics data. [...] Read more.
Background/Objectives: Nasal and nasopharyngeal swabs are commonly used for detecting respiratory viruses, including influenza, which significantly alters host cell metabolites. This study aimed to develop a machine learning model to identify biomarkers that differentiate between influenza-positive and -negative cases using clinical metabolomics data. Method: A publicly available dataset of 236 nasopharyngeal samples screened via liquid chromatography–quadrupole time-of-flight (LC/Q-TOF) mass spectrometry was used. Among these, 118 samples tested positive for influenza (40 A H1N1, 39 A H3N2, 39 Influenza B), while 118 were negative controls. A stacking-based model was proposed using the top 20 selected features. Thirteen machine learning models were initially trained, and the top three were combined using predicted probabilities to form a stacking classifier. Results: The ExtraTrees stacking model outperformed other models, achieving 97.08% accuracy. External validation on a prospective cohort of 96 symptomatic individuals (48 positive and 48 negatives for influenza) showed 100% accuracy. SHAP values were used to enhance model explainability. Metabolites such as Pyroglutamic Acid (retention time: 0.81 min, m/z: 84.0447) and its in-source fragment ion (retention time: 0.81 min, m/z: 130.0507) showed minimal impact on influenza-positive cases. On the other hand, metabolites with a retention time of 10.34 min and m/z 106.0865, and a retention time of 8.65 min and m/z 211.1376, demonstrated significant positive contributions. Conclusions: This study highlights the effectiveness of integrating metabolomics data with machine learning for accurate influenza diagnosis. The stacking-based model, combined with SHAP analysis, provided robust performance and insights into key metabolites influencing predictions. Full article
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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