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Applied Sciences
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Artificial Intelligence Techniques for Medical Data Analytics
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Artificial Intelligence Techniques for Medical Data Analytics

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 882

Special Issue Editor

Dr. Rui Fonseca-Pinto

E-Mail Website
Guest Editor
Polytechnic Institute of Leiria, Leiria, Portugal
Interests: application of decision support techniques in medicine; using machine learning/artificial intelligence methodologies applied to biomedical data

Special Issue Information

Dear Colleagues,

Artificial intelligence methodologies have great potential and applicability in the context of healthcare provision and in medicine in particular.

The large amount of data that modern complementary means of diagnosis produces is not compatible with the human capacity for simultaneous information processing. In the era of Big Data, intelligent data analysis methodologies for clinical decision support are a key ally for diagnosis and also for the personalised design of treatment, which is characteristic of individualised medicine. This individualised approach must be based on data analysis and artificial intelligence methodologies, and this Special Issue aims to showcase research that, using AI techniques, is a practical example of this paradigm shift.

******************************************

We are pleased to invite you to contribute to our Special Issue, which aims to explore cutting-edge advancements in the intersection of technology and healthcare.

Research areas of interest include, but are not limited to, the following:

  1. Image processing and pattern classification;
  2. Radiomics;
  3. Mobile computing in healthcare;
  4. Telemedicine;
  5. Predictive models in epidemiology;
  6. Proactive Health Monitoring;
  7. Natural Language Processing and management of clinical data;
  8. Data management in health.

We look forward to receiving your contributions.

Dr. Rui Fonseca-Pinto
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. 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

  • medical data analysis
  • radiomics
  • predictive models
  • telemedicine

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Published Papers (1 paper)

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Research

16 pages, 1769 KiB  
Article
Advanced Brain Tumor Segmentation Using SAM2-UNet
by Rohit Viswakarma Pidishetti, Maaz Amjad and Victor S. Sheng
Appl. Sci. 2025, 15(6), 3267; https://doi.org/10.3390/app15063267 - 17 Mar 2025
Viewed by 671
Abstract
Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The [...] Read more.
Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image data. These methods have also shown great results in tumor segmentation and the identification of anomalies, which assist health care professionals in treatment planning. Despite advancements made in the domain of deep learning for medical image segmentation, the precise segmentation of tumors remains challenging because of the complex structures of tumors across patients. Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the strengths of both architectures to improve segmentation accuracy and consumes less computational resources by maintaining efficiency. The proposed model possesses the ability to perform explicitly well on scarce data, and we trained this model on the Brain Tumor Segmentation Challenge 2020 (BraTS) dataset. This architecture is inspired by U-Networks that are based on the encoder and decoder architecture. The Hiera pre-trained model is set as a backbone to this architecture to capture multi-scale features. Adapters are embedded into the encoder to achieve parameter-efficient fine-tuning. The dataset contains four channels of MRI scans of 369 glioma patients as T1, T1ce, T2, and T2-flair and a segmentation mask for each patient consisting of non-tumor (NT), necrotic and non-enhancing tumor (NCR/NET), and peritumoral edema or GD-enhancing tumor (ET) as the ground-truth value. These experiments yielded good segmentation performance and achieved balanced performance based on the metrics discussed next in this paragraph for each tumor region. Our experiments yielded the following results with minimal hardware resources, i.e., 16 GB RAM with 30 epochs: a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an Sα score of 0.692, a weighted F-beta score (Fβw) of 0.267, a F-beta score (Fβ) of 0.261, an Eϕ score of 0.857, and a Mean Absolute Error (MAE) of 0.04 on the BraTS 2020 dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques for Medical Data Analytics)
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