Algorithms for Computer Aided Diagnosis: 2nd Edition

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 512

Special Issue Editor


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Guest Editor
Mathematics and Computer Science Department, College of Natural Sciences and Mathematics, Louisiana State University of Alexandria, Alexandria, LA 71302, USA
Interests: medical Imaging; non-invasive computer-assisted diagnosis systems; image and video processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Algorithms stand at the forefront of modern medical diagnostics, catalyzing a paradigm shift away from conventional methods toward more efficient and precise healthcare solutions. Within the realm of medical technology, a diverse array of instruments come into play, including temperature probes, heart rate monitors, and respiration rate counters. However, it is algorithms that serve as the linchpin of this transformation. These computational powerhouses breathe life into these devices, interpreting complex physiological data with unprecedented accuracy. For instance, electrocardiogram readings capture the heart's electrical activity, while respiration rate data count chest movements per minute. Through the seamless incorporation of artificial intelligence techniques, the diagnostic process has been revolutionized, streamlining a once time-consuming and cumbersome endeavor.

In this Special Issue, we will delve deep into cutting-edge applications of AI in medical diagnostics, showcasing state-of-the-art approaches that promise to reshape the healthcare landscape. These algorithms, finely tuned for this purpose, are driving diagnoses in a myriad of diseases and disorders, utilizing data sourced from various medical instruments. As we are striving toward a future marked by comprehensive and automated computer-aided diagnosis, it is important to shine a light on these specialized machine learning algorithms. This journey transcends the confines of conventional practices, paving the way for innovative applications within the medical field. With each passing day, algorithms continue to reshape healthcare, propelling us toward a future in which precision and efficiency will define the standard of medical practice, ultimately leading to improved patient outcomes.

The scope of this Special Issue includes, but is not limited to, the following:

  • Innovative technological advancements in the medical field;
  • Developing computer-aided diagnosis systems;
  • Machine learning algorithms for medical images;
  • Artificial intelligence algorithms in healthcare;
  • Algorithm-driven wearable devices for comprehensive health assessment;
  • Enhanced medical image analysis with machine learning algorithms.

Dr. Ahmed Shaffie
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • algorithms
  • machine learning
  • artificial intelligence (AI)
  • computer-aided diagnosis (CAD)
  • healthcare revolution
  • medical devices

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Published Papers (1 paper)

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Research

12 pages, 1696 KiB  
Article
Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
by Liyuan Chen, Avanka Lowe and Jing Wang
Algorithms 2024, 17(10), 435; https://doi.org/10.3390/a17100435 - 1 Oct 2024
Viewed by 353
Abstract
Positron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging [...] Read more.
Positron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging the potential of artificial intelligence (AI), we aimed to develop a hybrid fusion model integrating radiomics and Convolutional Neural Network (CNN) architectures to improve differentiation between benign post-radiation changes and residual or recurrent malignancies on PET/CT images. We retrospectively collected post-radiation PET/CTs with identified labels for benign changes or residual/recurrent malignant lesions from 95 lung cancer patients who received radiation therapy. Firstly, we developed separate radiomics and CNN models using handcrafted and self-learning features, respectively. Then, to build a more reliable model, we fused the probabilities from the two models through an evidential reasoning approach to derive the final prediction probability. Five-folder cross-validation was performed to evaluate the proposed radiomics, CNN, and fusion models. Overall, the hybrid fusion model outperformed the other two models in terms of sensitivity, specificity, accuracy, and the area under the curve (AUC) with values of 0.67, 0.72, 0.69, and 0.72, respectively. Evaluation results on the three AI models we developed suggest that handcrafted features and learned features may provide complementary information for residual or recurrent malignancy identification in PET/CT. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)
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