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Diagnostics
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Advances in Artificial Intelligence in Healthcare
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Advances in Artificial Intelligence in Healthcare

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: 31 December 2025 | Viewed by 13365

Special Issue Editors

Prof. Dr. Kelvin K. L. Wong

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Interests: medical imaging; computational hemodynamics; simulation modeling experience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dhanjoo N. Ghista

E-Mail Website
Guest Editor
University 2020 Foundation, Northborough, MA, USA
Interests: engineering; biomedical engineering; medical and health sciences; fitness & sports science; healthcare engineering & management; cognitive science; psychology & behaviorism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of precision medicine, digital radiology and AI-driven radiomics analysis are at the forefront of healthcare transformation. This convergence enables personalized preventive and therapeutic interventions tailored to individual patient characteristics, minimizing the costs and side effects. Medical imaging plays a pivotal role by facilitating screening, early diagnosis, response evaluation and recurrence assessment. Radiomics extracts mineable, high-dimensional data from routine medical images to create an 'imaging phenotype,' which categorizes disease severity, predicts therapy response and forecasts patient outcomes. The aim of this research proposal is to investigate and promote the integration of digital radiology and AI-driven radiomics for precision medicine in healthcare.

Recent advances in smart computer vision have spurred significant interest in AI applications within radiology and in healthcare. While some AI software applications have received clinical approval, numerous unexplored possibilities remain. AI-driven computer-assisted detection and diagnosis, utilizing deep neural networks, automates tasks such as image classification and object localization. AI also extends its potential to clinical decision support, protocol optimization and workflow improvement. This Special Issue explores various network architectures suitable for digitalization in radiology and will be able to advance healthcare based on AI. We seek the support and engagement of experts, researchers and authors in this exciting endeavor to advance healthcare through the fusion of radiology and AI.

The convergence of artificial intelligence and digital imaging offers a unique opportunity to shift from qualitative to quantitative data, fostering the development of clinical decision support systems. Radiomics and deep learning, as two prominent quantitative imaging techniques, promise efficiency, minimal invasiveness and high accuracy. Challenges such as model explainability, feature reproducibility and sensitivity to imaging variations must be addressed before clinical implementation. This narrative review assesses the status of quantitative medical image analysis, outlines challenges in the field, proposes a robust radiomics analysis framework and discusses future prospects. The Special Issue we aim to create will consolidate research findings on AI in digital radiology and inspire a new era of precision medicine and smart healthcare.

  • Radiology and AI-driven radiomics as the key for precise, cost-effective and personalized medicine;
  • Medical imaging as the pivot for screening, diagnosis and treatment monitoring in this convergence;
  • AI's role in digital radiology, particularly in computer-assisted detection and diagnosis;
  • The 'imaging phenotype' via radiomics for disease categorization and outcome prediction;
  • Deep convolutional neural networks for radiology tasks;
  • Seeking research community support to advance digital radiology and AI in healthcare;
  • Quantitative image analysis potential and Special Issue for precision medicine.

Prof. Dr. Kelvin K.L. Wong
Prof. Dr. Dhanjoo N. Ghista
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

  • artificial intelligence
  • AI-driven radiomics
  • healthcare
  • digital radiology
  • precision medicine

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Published Papers (7 papers)

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Research

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15 pages, 1376 KiB  
Article
Dynamic Prediction of Physical Exertion: Leveraging AI Models and Wearable Sensor Data During Cycling Exercise
by Aref Smiley and Joseph Finkelstein
Diagnostics 2025, 15(1), 52; https://doi.org/10.3390/diagnostics15010052 - 28 Dec 2024
Viewed by 881
Abstract
Background/Objectives: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. Methods: Both traditional machine learning and deep learning methods for classification and regression were assessed. The research involved 27 healthy participants [...] Read more.
Background/Objectives: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. Methods: Both traditional machine learning and deep learning methods for classification and regression were assessed. The research involved 27 healthy participants engaged in controlled cycling exercises. Physiological data, including ECG, heart rate, oxygen saturation, and pedal speed (RPM), were collected during these sessions, which were divided into eight two-minute segments. Heart rate variability (HRV) was also calculated to serve as a predictive indicator. We employed two feature selection algorithms to identify the most relevant features for model training: Minimum Redundancy Maximum Relevance (MRMR) for both classification and regression, and Univariate Feature Ranking for Classification. A total of 34 traditional models were developed using MATLAB’s Classification Learner App, utilizing 20% of the data for testing. In addition, Long Short-Term Memory (LSTM) networks were trained on the top features selected by the MRMR and Univariate Feature Ranking algorithms to enhance model performance. Finally, the MRMR-selected features were used for regression to train the LSTM model for predicting continuous outcomes. Results: The LSTM model for regression demonstrated robust predictive capabilities, achieving a mean squared error (MSE) of 0.8493 and an R-squared value of 0.7757. The classification models also showed promising results, with the highest testing accuracy reaching 89.2% and an F1 score of 91.7%. Conclusions: These results underscore the effectiveness of combining feature selection algorithms with advanced machine learning (ML) and deep learning techniques for predicting physical exertion levels using wearable sensor data. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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37 pages, 14727 KiB  
Article
Enhancing the Understanding of Abdominal Trauma During the COVID-19 Pandemic Through Co-Occurrence Analysis and Machine Learning
by Dumitru Radulescu, Dan Marian Calafeteanu, Patricia-Mihaela Radulescu, Gheorghe-Jean Boldea, Razvan Mercut, Eleonora Daniela Ciupeanu-Calugaru, Eugen-Florin Georgescu, Ana Maria Boldea, Ion Georgescu, Elena-Irina Caluianu, Georgiana-Andreea Marinescu and Emil-Tiberius Trasca
Diagnostics 2024, 14(21), 2444; https://doi.org/10.3390/diagnostics14212444 - 31 Oct 2024
Cited by 2 | Viewed by 879
Abstract
Background: This study examines the impact of the COVID-19 pandemic on abdominal trauma management by comparing pre-pandemic (17 February 2018–26 February 2020) and pandemic periods (27 February 2020–7 March 2022). Methods: Analyzing data from 118 patients at the Emergency County Clinical Hospital of [...] Read more.
Background: This study examines the impact of the COVID-19 pandemic on abdominal trauma management by comparing pre-pandemic (17 February 2018–26 February 2020) and pandemic periods (27 February 2020–7 March 2022). Methods: Analyzing data from 118 patients at the Emergency County Clinical Hospital of Craiova, we identified significant shifts in clinical practices affecting patient outcomes. Results: During the pandemic, a moderate increase in surgical interventions for specific abdominal traumas indicated the effective adaptation of the medical system. Prioritizing critical cases and deferring non-urgent procedures optimized limited resources. Demographic and clinical factors—including age, sex, body mass index (BMI), and red cell distribution width (RDW)—significantly influenced the hospitalization duration and recovery outcomes. Gender disparities in mortality lessened during the pandemic, possibly due to standardized interventions and the physiological effects of SARS-CoV-2. The link between occupation and obesity highlighted how work environments impact trauma severity, especially as lifestyle changes affect BMI. While age remained a major predictor of mortality, its influence slightly decreased, potentially due to improved protocols for elderly patients. RDW emerged as an important prognostic marker for disease severity and mortality risk. Conclusions: Employing advanced co-occurrence analysis enhanced with machine learning, we uncovered complex relationships between clinical and demographic variables often overlooked by traditional methods. This innovative approach provided deeper insights into the collective impact of various factors on patient outcomes. Our findings demonstrate the healthcare system’s rapid adaptations during the pandemic and offer critical insights for optimizing medical strategies and developing personalized interventions in global crises. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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39 pages, 1496 KiB  
Article
Interactions between Cognitive, Affective, and Respiratory Profiles in Chronic Respiratory Disorders: A Cluster Analysis Approach
by Iulian-Laurențiu Buican, Victor Gheorman, Ion Udriştoiu, Mădălina Olteanu, Dumitru Rădulescu, Dan Marian Calafeteanu, Alexandra Floriana Nemeş, Cristina Călăraşu, Patricia-Mihaela Rădulescu and Costin-Teodor Streba
Diagnostics 2024, 14(11), 1153; https://doi.org/10.3390/diagnostics14111153 - 30 May 2024
Cited by 2 | Viewed by 1330
Abstract
This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed [...] Read more.
This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed to have asthma or COPD underwent extensive evaluations using standardized instruments such as the mMRC scale, the CAT test, and spirometry. Complementary cognitive and affective assessments were performed employing the MMSE, MoCA, and the Hamilton Anxiety and Depression Scale, furnishing a holistic view of patient health statuses. The analysis delineated three distinct clusters: Moderate Cognitive Respiratory, Severe Cognitive Respiratory, and Stable Cognitive Respiratory, each characterized by unique profiles that underscore the necessity for tailored therapeutic strategies. These clusters exhibited significant correlations between the severity of respiratory symptoms and their effects on cognitive and affective conditions. The results highlight the benefits of an integrated treatment approach for COPD and asthma, which is personalized based on the intricate patterns identified through clustering. Such a strategy promises to enhance the management of these diseases, potentially elevating the quality of life and everyday functionality of the patients. These findings advocate for treatment customization according to the specific interplays among cognitive, affective, and respiratory dimensions, presenting substantial prospects for clinical advancement and pioneering new avenues for research in the domain of chronic respiratory disease management. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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19 pages, 4142 KiB  
Article
Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network
by Qingzhao Qin, Yinglong Li, Aonan Wen, Yujia Zhu, Zixiang Gao, Shenyao Shan, Hongyu Wu, Yijiao Zhao and Yong Wang
Diagnostics 2024, 14(6), 603; https://doi.org/10.3390/diagnostics14060603 - 12 Mar 2024
Viewed by 1627
Abstract
(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect’s complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this [...] Read more.
(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect’s complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The ‘3D surface deviation’ between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the ‘3D surface deviation’ between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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Review

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19 pages, 2683 KiB  
Review
A Review of Artificial Intelligence-Based Dyslexia Detection Techniques
by Yazeed Alkhurayyif and Abdul Rahaman Wahab Sait
Diagnostics 2024, 14(21), 2362; https://doi.org/10.3390/diagnostics14212362 - 23 Oct 2024
Cited by 2 | Viewed by 2660
Abstract
Problem: Dyslexia is a learning disorder affecting an individual’s ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or [...] Read more.
Problem: Dyslexia is a learning disorder affecting an individual’s ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or incorrect diagnosis. Motivation: Timely identification is essential to provide personalized treatment and improve the individual’s quality of life. The development of artificial intelligence techniques offers a platform to identify dyslexia using behavior and neuroimaging data. However, the limited datasets and black-box nature of ML models reduce the generalizability and interpretability of dyslexia detection (DD) models. The dimensionality reduction technique (DRT) plays a significant role in providing dyslexia features to enhance the performance of machine learning (ML)- and deep learning (DL)-based DD techniques. Aim: In this review, the authors intend to investigate the role of DRTs in enhancing the performance of ML- and DL-based DD models. Methodology: The authors conducted a comprehensive search across multiple digital libraries, including Scopus, Web of Science, PubMed, and IEEEXplore, to identify articles associated with DRTs in identifying dyslexia. They extracted 479 articles using these digital libraries. After an extensive screening procedure, a total of 39 articles were included in this review. Results: The review findings revealed various DRTs for identifying critical dyslexia patterns from multiple modalities. A significant number of studies employed principal component analysis (PCA) for feature extraction and selection. The authors presented the essential features associated with DD. In addition, they outlined the challenges and limitations of existing DRTs. Impact: The authors emphasized the need for the development of novel DRTs and their seamless integration with advanced DL techniques for robust and interpretable DD models. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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35 pages, 2429 KiB  
Review
Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?
by Andrea Lastrucci, Yannick Wandael, Angelo Barra, Renzo Ricci, Giovanni Maccioni, Antonia Pirrera and Daniele Giansanti
Diagnostics 2024, 14(13), 1333; https://doi.org/10.3390/diagnostics14131333 - 23 Jun 2024
Cited by 12 | Viewed by 3460
Abstract
This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its [...] Read more.
This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR’s integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR’s transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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Other

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12 pages, 1164 KiB  
Systematic Review
Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis
by Dewa Putu Wisnu Wardhana, Sri Maliawan, Tjokorda Gde Bagus Mahadewa, Rohadi Muhammad Rosyidi and Sinta Wiranata
Diagnostics 2024, 14(21), 2354; https://doi.org/10.3390/diagnostics14212354 - 22 Oct 2024
Viewed by 1193
Abstract
Background: Glioblastoma, the predominant primary tumor among all central nervous systems, accounts for around 80% of cases. Prognosis in neuro-oncology involves assessing the disease’s progression in different individuals, considering the time between the initial pathological diagnosis and the time until the disease worsens. [...] Read more.
Background: Glioblastoma, the predominant primary tumor among all central nervous systems, accounts for around 80% of cases. Prognosis in neuro-oncology involves assessing the disease’s progression in different individuals, considering the time between the initial pathological diagnosis and the time until the disease worsens. A noninvasive therapeutic approach called radiomic features (RFs), which involves the application of artificial intelligence in MRI, has been developed to address this issue. This study aims to systematically gather evidence and evaluate the prognosis significance of radiomics in glioblastoma using RFs. Methods: We conducted an extensive search across the PubMed, ScienceDirect, EMBASE, Web of Science, and Cochrane databases to identify relevant original studies examining the use of RFs to evaluate the prognosis of patients with glioblastoma. This thorough search was completed on 25 July 2024. Our search terms included glioblastoma, MRI, magnetic resonance imaging, radiomics, and survival or prognosis. We included only English-language studies involving human subjects, excluding case reports, case series, and review studies. The studies were classified into two quality categories: those rated 4–6 were considered moderate-, whereas those rated 7–9 were high-quality using the Newcastle–Ottawa Scale (NOS). Hazard ratios (HRs) and their 95% confidence intervals (CIs) for OS and PFS were combined using random effects models. Results: In total, 253 studies were found in the initial search across the five databases. After screening the articles, 40 were excluded due to not meeting the eligibility criteria, and we included only 14 studies. All twelve OS and eight PFS trials were considered, involving 1.639 and 747 patients, respectively. The random effects model was used to calculate the pooled HRs for OS and PFS. The HR for OS was 3.59 (95% confidence interval [CI], 1.80–7.17), while the HR for PFS was 4.20 (95% CI, 1.02–17.32). Conclusions: An RF-AI-based approach offers prognostic significance for OS and PFS in patients with glioblastoma. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence in Healthcare)
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