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Applied Sciences
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Artificial Intelligence in Medical Diagnostics: Second Edition
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Artificial Intelligence in Medical Diagnostics: Second Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 3728

Special Issue Editors

Prof. Dr. Jan Mulawka

E-Mail Website
Guest Editor
Electronics and Information Technology, Warsaw University of Technology, 00-661 Warsaw, Poland
Interests: artificial intelligence; expert systems; data bases; methods of knowledge representation; software engineering; medical informatics; machine learning; neural networks; bioinformatics; metabolomics; radiomics; nutrigenomics; logic; biocomputers
Special Issues, Collections and Topics in MDPI journals
Dr. Dorota Dunin-Wąsowicz

E-Mail Website
Guest Editor
Neurology and Epileptology Department, The Children’s Memorial Health Institute, 04-730 Warsaw, Poland
Interests: epilepsy; neuroinfections; TORCH infections; neuroimaging; neonatal neurology
Prof. Dr. Piotr Socha

E-Mail Website
Guest Editor
Public Health Department, Children’s Memorial Health Institute, 04-730 Warsaw, Poland
Interests: research and clinical work mainly devoted to cholestatic liver disease; non-alcoholic fatty liver disease; rare metabolic liver diseases (e.g., Wilson disease, newly described PGM-1); nutrition in hepatology and gastroenterology (e.g., LCPUFA deficiency); obesity prevention and therapy; feeding disorders; protracted diarrhea of infancy and early childhood
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue entitled “Artificial Intelligence in Medical Diagnostics: Second Edition”.

Over the past few decades, artificial intelligence (AI) has revolutionized almost every aspect of our modern world. The domain of medicine has been particularly affected by these innovations. This Special Issue is devoted to the application of AI methods in various research fields of medical diagnostics. For this purpose, different branches of artificial intelligence are used. In our Special Issue, we focus on two of them: expert systems and radiomics, which can be applied in different areas of medicine. Expert systems are traditionally seen as one of the first branches of AI. These systems are specifically designed to emulate the decision-making ability of a human expert in a particular field of medicine. They are based on inference engines and symbolic knowledge. However, one of the most common techniques used in the medical diagnosis of patients is imaging, which is simple in principle but extremely powerful. It allows doctors to see abnormalities in patients’ bodies in a non-invasive manner and helps them to choose suitable treatments. Medical imaging provides a lot of data to doctors; however, years of practice are often required to detect abnormalities, as some changes might be very hard to detect. Radiomics refers to the extraction and analysis of large amounts of advanced quantitative imaging features with a high throughput. This field has been greatly developed in recent years through machine learning architectures, such as different types of neural networks. The further development of aforementioned research fields is important in order to automate and accelerate the process of medical diagnoses, which can be crucial for the efficient and proper treatment of the ill.

Prof. Dr. Jan Mulawka
Dr. Dorota Dunin-Wąsowicz
Prof. Dr. Piotr Socha
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. Applied Sciences 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 2400 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
  • medical diagnostics
  • expert systems
  • radiomics
  • knowledge acquisition
  • machine learning
  • neural networks

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Published Papers (4 papers)

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Research

18 pages, 2917 KiB  
Article
A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer
by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone and Lidia Strigari
Appl. Sci. 2025, 15(6), 3070; https://doi.org/10.3390/app15063070 - 12 Mar 2025
Viewed by 601
Abstract
Background/Objectives: The aim of this study was the early identification of endometriosis-associated ovarian cancer (EAOC) versus non-endometriosis associated ovarian cancer (NEOC) or non-cancerous tissues using pre-surgery contrast-enhanced-Computed Tomography (CE-CT) images in patients undergoing surgery for suspected ovarian cancer (OC). Methods: A [...] Read more.
Background/Objectives: The aim of this study was the early identification of endometriosis-associated ovarian cancer (EAOC) versus non-endometriosis associated ovarian cancer (NEOC) or non-cancerous tissues using pre-surgery contrast-enhanced-Computed Tomography (CE-CT) images in patients undergoing surgery for suspected ovarian cancer (OC). Methods: A prospective trial was designed to enroll patients undergoing surgery for suspected OC. Volumes of interest (VOIs) were semiautomatically segmented on CE-CT images and classified according to the histopathological results. The entire dataset was divided into training (70%), validation (10%), and testing (20%). A Python pipeline was developed using the transfer learning approach, adopting four different convolution neural networks (CNNs). Each architecture (i.e., VGG19, Xception, ResNet50, and DenseNet121) was trained on each of the axial slices of CE-CT images and refined using the validation dataset. The results of each CNN model for each slice within a VOI were combined using three rival machine learning (ML) models, i.e., Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbor (KNN), to obtain a final output distinguishing between EAOC and NEOC, and between EAOC/NEOC and non-tumoral tissues. Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. Each model’s performance was assessed based on the area under the receiver operating characteristic (ROC) curve (AUC), F1-score, sensitivity, and specificity. These metrics were then integrated into a Machine Learning Cumulative Performance Score (MLcps) to provide a comprehensive evaluation on the test dataset. Results: An MLcps value of 0.84 identified the VGG19 + majority voting ensemble as the optimal model for distinguishing EAOC from NEOC, achieving an AUC of 0.85 (95% CI: 0.70–0.98). In contrast, the VGG19 + SVM model, with an MLcps value of 0.76, yielded an AUC of 0.79 (95% CI: 0.63–0.93). For differentiating EAOC/NEOC from non-tumoral tissues, the VGG19 + SVM model demonstrated superior performance, with an MLcps value of 0.93 and an AUC of 0.97 (95% CI: 0.92–1.00). Conclusions: Hybrid models based on CE-CT have the potential to differentiate EAOC and NEOC patients as well as between OC (EAOC and NEOC) and non-tumoral ovaries, thus potentially supporting gynecological surgeons in personalized surgical approaches such as more conservative procedures. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Diagnostics: Second Edition)
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16 pages, 1421 KiB  
Article
Detecting Methotrexate in Pediatric Patients Using Artificial Neural Networks
by Alejandro Medina Santiago, Jorge Iván Bermúdez Rodríguez, Jorge Antonio Orozco Torres, Julio Alberto Guzmán Rabasa, José Manuel Villegas Izaguirre and Gladys Falconi Alejandro
Appl. Sci. 2025, 15(1), 306; https://doi.org/10.3390/app15010306 - 31 Dec 2024
Viewed by 590
Abstract
Methotrexate is an antimetabolic agent with proliferative and immunosuppressive activity. It has been demonstrated to be an effective treatment for acute lymphoblastic leukemia (ALL) in children. However, there is evidence of an association between methotrexate and toxicity risks, which influences the personalization of [...] Read more.
Methotrexate is an antimetabolic agent with proliferative and immunosuppressive activity. It has been demonstrated to be an effective treatment for acute lymphoblastic leukemia (ALL) in children. However, there is evidence of an association between methotrexate and toxicity risks, which influences the personalization of treatment, particularly in the case of childhood ALL. This article presents the development and implementation of an algorithm based on artificial neural networks to detect methotrexate toxicity in pediatric patients with acute lymphoblastic leukemia. The algorithm utilizes historical clinical and laboratory data, with an effectiveness of 99% in the tests performed with the patient dataset. The use of neural networks in medicine is often linked to disease diagnosis systems. However, neural networks are not only capable of recognizing examples but also hold very important information. For this reason, one of the main areas of application of neural networks is the interpretation of medical data. In this article, we diagnose, with the application of neural networks in medicine, a concrete example: detecting methotrexate in its early stages in pediatric patients. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Diagnostics: Second Edition)
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17 pages, 21911 KiB  
Article
TurboPixels: A Superpixel Segmentation Algorithm Suitable for Real-Time Embedded Applications
by Abiel Aguilar-González, Alejandro Medina Santiago, Jorge Antonio Orozco Torres, J. A. de Jesús Osuna-Coutiño, Madaín Pérez Patricio and Néstor A. Morales-Navarro
Appl. Sci. 2024, 14(24), 11912; https://doi.org/10.3390/app142411912 - 19 Dec 2024
Viewed by 892
Abstract
Superpixel segmentation aims to produce a consistent grouping of pixels. In recent years, the importance of superpixel segmentation has increased in computer vision since it offers useful primitives for extracting image features and simplifies the complexity of other image processing steps. In this [...] Read more.
Superpixel segmentation aims to produce a consistent grouping of pixels. In recent years, the importance of superpixel segmentation has increased in computer vision since it offers useful primitives for extracting image features and simplifies the complexity of other image processing steps. In this work, we propose the TurboPixels algorithm, whose main contribution is a hardware architecture for superpixel segmentation. Compared with previous approaches, our superpixels are computed without the need for iterative loops. This makes it possible to reduce algorithmic complexity and increases processing speed. The experimental results indicate that our approach enables a small-scale FPGA-based implementation suitable for embedded applications. In addition, the results demonstrate that robust superpixel segmentation can be achieved with processing speeds up to 86 times faster than in previous works in the current literature. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Diagnostics: Second Edition)
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14 pages, 2784 KiB  
Article
Application of Artificial Intelligence in Support of NAFLD Diagnosis
by Jakub Płudowski and Jan Mulawka
Appl. Sci. 2024, 14(22), 10237; https://doi.org/10.3390/app142210237 - 7 Nov 2024
Viewed by 1049
Abstract
A comprehensive system for automated medical data analysis and diagnosis of non-alcoholic fatty liver disease using artificial intelligence has been developed. The system consists of several modules: medical data aggregation, AI model training using advanced machine learning algorithms, Explainable AI generating reports, and [...] Read more.
A comprehensive system for automated medical data analysis and diagnosis of non-alcoholic fatty liver disease using artificial intelligence has been developed. The system consists of several modules: medical data aggregation, AI model training using advanced machine learning algorithms, Explainable AI generating reports, and patient diagnosis by ensemble model. Those models have achieved diagnostic accuracy higher than 95%, and the system is designed for continuous improvement by aggregating more data and automatically retraining models. It is a modern, flexible, and scalable tool designed to support medical diagnosis. It can make doctors’ work easier and faster, and the discovered biomarkers of a disease can increase the quality of its diagnosis. The ensemble model generating diagnoses achieved nearly perfect quality and, using explainable artificial intelligence, it was possible to determine attributes and their values that constitute non-alcoholic-fatty-liver-disease (NAFLD) biomarkers. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Diagnostics: Second Edition)
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