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Diagnostics
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Artificial Intelligence Approaches for Medical Diagnostics in the USA
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Artificial Intelligence Approaches for Medical Diagnostics in the USA

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 August 2025 | Viewed by 334

Special Issue Editors

Dr. Jinzhong Yang

E-Mail Website
Guest Editor
Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Interests: deformable image registration; image segmentation; quantitative imaging biomarkers for MR-guided adaptive radiation therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Yao Zhao

E-Mail Website
Guest Editor Assistant
Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Interests: adaptive radiation therapy; image segmentation; breast cancer

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into medical diagnostics has revolutionized healthcare in the United States, enhancing the accuracy, efficiency, and accessibility of diagnostic procedures. This Special Issue aims to highlight the latest advancements and research on the application of AI technologies to improve medical imaging diagnostic procedures. The focus includes cutting-edge AI applications in medical image computing, with particular emphasis on advancements in image segmentation, image registration, and quality enhancement. Additional topics include methodologies for artifact reduction, radiomics-based predictive analytics, and novel image reconstruction techniques. By highlighting these innovations, this issue underscores how AI is reshaping the diagnostic landscape in the USA, advancing precision diagnosis and fostering patient-centric care. Contributions are encouraged from multidisciplinary teams, including expertise in medical physics, computer science, and clinical practice, to accelerate the translation of AI innovations into clinical settings for patient care.

Dr. Jinzhong Yang
Guest Editor

Dr. Yao Zhao
Guest Editor Assistant

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
  • medical image segmentation
  • image registration
  • image quality enhancement
  • image artifact reduction
  • radiomics
  • image reconstruction
  • deep learning in diagnostics
  • AI-driven medical Imaging
  • diagnostic accuracy
  • diagnostic imaging innovations

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Published Papers (1 paper)

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Research

16 pages, 926 KiB  
Article
Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults
by Oriehi Anyaiwe, Nandini Nataraj and Bhargava Sai Gudikandula
Diagnostics 2025, 15(11), 1327; https://doi.org/10.3390/diagnostics15111327 - 26 May 2025
Viewed by 141
Abstract
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to [...] Read more.
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. Methods: The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers—including cerebrospinal fluid (CSF) amyloid-beta 42 (Aβ42), amyloid positron emission tomography (amyloid PET), cerebrospinal fluid Tau protein (CSF Tau), and magnetic resonance imaging with fluorodeoxyglucose positron emission tomography (MRI FDG-PET)—were analyzed simultaneously to compute the cognitive impairment (CI) score of instances, dynamically adjusted for age. Higher CSF Aβ42 levels consistently demonstrated a protective effect, while elevated amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. To demonstrate its applicability, we applied the full four-stage framework—comprising data aggregation and cleaning, sigmoid-based synthetic biomarker simulation with descriptive analysis, parameter accumulation modeling, and correlation-driven CI classification—on a curated dataset of 307 instances (ages 10–110) from Kaggle, the Alzheimer’s Disease Neuroimaging Initiative (ANDI), and the Open Access Series of Imaging Studies (OASIS) to evaluate age-specific stratification of preclinical AD risk. Results: The study highlights the model’s potential to identify individuals in risk zones from a pool of 150 instances, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Conclusions: Future work aims to validate the model using longitudinal, inclusive, real-world datasets and expand its predictive capacity through machine learning techniques and integrating genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer’s disease by providing a scalable computational tool for early risk assessment and intervention planning. Full article
(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in the USA)
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