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Diagnostics
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Artificial Intelligence and Deep Learning in Clinical Classification and Prediction
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Artificial Intelligence and Deep Learning in Clinical Classification and Prediction

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 July 2025 | Viewed by 6374

Special Issue Editor

Prof. Dr. Gemma Piella

E-Mail Website
Guest Editor
Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Barcelona, Spain
Interests: medical image analysis; machine learning and artificial intelligence for computer-aided diagnosis and treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and deep learning (DL) technologies have seen widespread application in the medical field, particularly in the classification and prediction of clinical diseases. These methods can uncover complex patterns and correlations from large clinical datasets, thereby improving the accuracy of diagnosis and prognosis.

By training deep neural network models, accurate classification of disease types, severity, treatment response, and other factors can be achieved. For example, in cancer diagnosis, DL algorithms can identify tumor characteristics from medical images, assisting clinicians in making diagnostic decisions. In cardiovascular disease prediction, DL models can forecast the risk of heart attacks by incorporating biomarkers, symptoms, and other data. These applications significantly enhance the efficiency and accuracy of clinical decision-making, contributing to more precise medical care.

Despite the tremendous success of AI and DL in healthcare, challenges such as data privacy, model interpretability, and generalizability remain. Moving forward, it will be crucial to further improve the reliability and safety of these technologies to maximize their benefits in clinical practice.

Prof. Dr. Gemma Piella
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
  • clinical classification
  • clinical prediction

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Published Papers (6 papers)

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Research

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21 pages, 4528 KiB  
Article
Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders
by Roberta Bruschetta, Angela Caruso, Martina Micai, Simona Campisi, Gennaro Tartarisco, Giovanni Pioggia and Maria Luisa Scattoni
Diagnostics 2025, 15(2), 136; https://doi.org/10.3390/diagnostics15020136 - 8 Jan 2025
Viewed by 778
Abstract
Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims [...] Read more.
Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims to address this aspect by employing a marker-less Artificial Intelligence (AI) approach for the automatic assessment of infants’ movements from single-camera video recordings. Methods: A total of 74 high-risk infants were selected from the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA) database and closely observed at five different time points, ranging from 10 days to 24 weeks of age. Automatic motion tracking was performed using deep learning to capture infants’ body landmarks and extract a set of kinematic parameters. Results: Our findings revealed significant differences between infants later diagnosed with NDD and typically developing (TD) infants in three lower limb features at 10 days old: ‘Median Velocity’, ‘Area differing from moving average’, and ‘Periodicity’. Using a Support Vector Machine (SVM), we achieved an accuracy rate of approximately 85%, a sensitivity of 64%, and a specificity of 100%. We also observed that the disparities in lower limb movements diminished over time points. Furthermore, the tracking accuracy was assessed through a comparative analysis with a validated semi-automatic algorithm (Movidea), obtaining a Pearson correlation (R) of 93.96% (88.61–96.60%) and a root mean square error (RMSE) of 9.52 pixels (7.29–12.37). Conclusions: This research highlights the potential of AI movement analysis for the early detection of NDDs, providing valuable insights into the motor development of infants at risk. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Clinical Classification and Prediction)
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13 pages, 1622 KiB  
Article
Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods
by Eylem Gul Ates, Gokcen Coban and Jale Karakaya
Diagnostics 2024, 14(24), 2802; https://doi.org/10.3390/diagnostics14242802 - 13 Dec 2024
Viewed by 748
Abstract
Backgrounds: Although COVID-19 is primarily known as a respiratory disease, there is growing evidence of neurological complications, such as ischemic stroke, in infected individuals. This study aims to evaluate the impact of COVID-19 on acute ischemic stroke (AIS) using radiomic features extracted from [...] Read more.
Backgrounds: Although COVID-19 is primarily known as a respiratory disease, there is growing evidence of neurological complications, such as ischemic stroke, in infected individuals. This study aims to evaluate the impact of COVID-19 on acute ischemic stroke (AIS) using radiomic features extracted from brain MR images and machine learning methods. Methods: This retrospective study included MRI data from 57 patients diagnosed with AIS who presented to the Department of Radiology at Hacettepe University Hospital between March 2020 and September 2021. Patients were stratified into COVID-19-positive (n = 30) and COVID-19-negative (n = 27) groups based on PCR results. Radiomic features were extracted from brain MR images following image processing steps. Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. Model performance was evaluated using a range of classification metrics, including measures of predictive accuracy and diagnostic reliability, with 95% confidence intervals provided to enhance reliability. Results: This study assessed the performance of dimensionality reduction and classification algorithms in distinguishing COVID-19-negative and COVID-19-positive cases using radiomics data from brain MR scans. Without feature selection, ANN achieved the highest AUC of 0.857 (95% CI: 0.806–0.900), demonstrating strong discriminative power. Using the Boruta method for feature selection, the k-NN classifier attained the best performance, with an AUC of 0.863 (95% CI: 0.816–0.904). LASSO-based feature selection showed comparable results across k-NN, RF, and ANN classifiers, while SVM exhibited excellent specificity and high PPV. The RFE method yielded the highest overall performance, with the k-NN classifier achieving an AUC of 0.882 (95% CI: 0.838–0.924) and an accuracy of 79.1% (95% CI: 73.6–83.8). Among the methods, RFE provided the most consistent results, with k-NN and the ANN identified as the most effective classifiers for COVID-19 detection. Conclusions: The proposed radiomics-based classification model effectively distinguishes AIS associated with COVID-19 from brain MRI. These findings demonstrate the potential of AI-driven diagnostic tools to identify high-risk patients, support optimized treatment strategies, and ultimately improve clinical implications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Clinical Classification and Prediction)
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15 pages, 4632 KiB  
Article
An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories
by Aziz Aksoy
Diagnostics 2024, 14(18), 2093; https://doi.org/10.3390/diagnostics14182093 - 22 Sep 2024
Cited by 1 | Viewed by 1369
Abstract
Background: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed [...] Read more.
Background: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed to eliminate the disadvantages of manual examination and improve the workload of clinical laboratories. Objectives: Regular analysis of peripheral blood cells and careful interpretation of their results are critical for protecting individual health and early diagnosis of diseases. Because many diseases can occur due to this, this study aims to detect white blood cells automatically. Methods: A hybrid model has been developed for this purpose. In the developed model, feature extraction has been performed with MobileNetV2 and EfficientNetb0 architectures. In the next step, the neighborhood component analysis (NCA) method eliminated unnecessary features in the feature maps so that the model could work faster. Then, different features of the same image were combined, and the extracted features were combined to increase the model’s performance. Results: The optimized feature map was classified into different classifiers in the last step. The proposed model obtained a competitive accuracy value of 95.6%. Conclusions: The results obtained in the proposed model show that the proposed model can be used in the detection of white blood cells. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Clinical Classification and Prediction)
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16 pages, 1777 KiB  
Article
Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence
by Fatma Hilal Yagin, Radwa El Shawi, Abdulmohsen Algarni, Cemil Colak, Fahaid Al-Hashem and Luca Paolo Ardigò
Diagnostics 2024, 14(18), 2049; https://doi.org/10.3390/diagnostics14182049 - 15 Sep 2024
Viewed by 1321
Abstract
Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We [...] Read more.
Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite’s individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model’s predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Clinical Classification and Prediction)
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15 pages, 5293 KiB  
Article
LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment
by Gemma Piella, Nicolau Farré, Daniel Esono, Miguel Ángel Cordobés, Javier Vázquez-Corral, Itxarone Bilbao and Concepción Gómez-Gavara
Diagnostics 2024, 14(15), 1654; https://doi.org/10.3390/diagnostics14151654 - 31 Jul 2024
Viewed by 1094
Abstract
Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is [...] Read more.
Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver’s colour and texture, which are prone to errors and heavily depend on the surgeon’s experience. The aim of this study was to develop and validate a simple, rapid, and accurate method for detecting steatosis in donor livers to improve the decision-making process during liver procurement. We developed LiverColor, a co-designed software platform that integrates image analysis and machine learning to classify a liver graft into valid or non-valid according to its steatosis level. We utilized an in-house dataset of 192 cases to develop and validate the classification models. Colour and texture features were extracted from liver photographs, and graft classification was performed using supervised machine learning techniques (random forests and support vector machine). The performance of the algorithm was compared against biopsy results and surgeons’ classifications. Usability was also assessed in simulated and real clinical settings using the Mobile Health App Usability Questionnaire. The predictive models demonstrated an area under the receiver operating characteristic curve of 0.82, with an accuracy of 85%, significantly surpassing the accuracy of visual inspections by surgeons. Experienced surgeons rated the platform positively, appreciating not only the hepatic steatosis assessment but also the dashboarding functionalities for summarising and displaying procurement-related data. The results indicate that image analysis coupled with machine learning can effectively and safely identify valid livers during procurement. LiverColor has the potential to enhance the accuracy and efficiency of liver assessments, reducing the reliance on subjective visual inspections and improving transplantation outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Clinical Classification and Prediction)
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Review

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29 pages, 1598 KiB  
Review
Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI
by Augusto Leone, Veronica Di Napoli, Nicola Pio Fochi, Giuseppe Di Perna, Uwe Spetzger, Elena Filimonova, Flavio Angileri, Francesco Carbone and Antonio Colamaria
Diagnostics 2025, 15(3), 251; https://doi.org/10.3390/diagnostics15030251 - 22 Jan 2025
Viewed by 458
Abstract
Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: [...] Read more.
Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including “MGMT”, “methylation”, “glioma”, “glioblastoma”, “machine learning”, “deep learning”, and “radiomics”, were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Clinical Classification and Prediction)
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