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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 912

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 (2 papers)

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Research

12 pages, 2431 KiB  
Article
Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts
by Emre Uysal, Gorkem Durak, Ayse Kotek Sedef, Ulas Bagci, Tanju Berber, Necla Gurdal and Berna Akkus Yildirim
Diagnostics 2025, 15(14), 1747; https://doi.org/10.3390/diagnostics15141747 - 10 Jul 2025
Abstract
Background/Objectives: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. Addressing this, we aim to employ unsupervised cluster analysis to identify prognostic [...] Read more.
Background/Objectives: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. Addressing this, we aim to employ unsupervised cluster analysis to identify prognostic subgroups of non-small-cell lung cancer (NSCLC) patients with brain metastasis (BM). Simple-yet-effective algorithms designed to identify similar group characteristics will assist clinicians in categorizing patients effectively. Methods: We retrospectively collected data from 95 NSCLC patients with BM treated at two oncology centers. To identify clinically distinct subgroups, two types of unsupervised clustering methods—two-step clustering (TSC) and hierarchical cluster analysis (HCA)—were applied to the baseline clinical data. Patients were categorized into prognostic classes according to the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA). Survival curves for the clusters and DS-GPA classes were generated using Kaplan–Meier analysis, and the differences were assessed with the log-rank test. The discriminative ability of three categorical variables on survival was compared using the concordance index (C-index). Results: The mean age of the patients was 61.8 ± 0.9 years, and the majority (77.9%) were men. Extracranial metastasis was present in 71.6% of the patients, with most (63.2%) having a single BM. The DS-GPA classification significantly divided the patients into prognostic classes (p < 0.001). Furthermore, statistical significance was observed between clusters created by TSC (p < 0.001) and HCA (p < 0.001). HCA showed the highest discriminatory power (C-index = 0.721), followed by the DS-GPA (C-index = 0.709) and TSC (C-index = 0.650). Conclusions: Our findings demonstrated that the TSC and HCA models were comparable in prognostic performance to the DS-GPA index in NSCLC patients with BM. These results suggest that unsupervised clustering may offer a data-driven perspective on patient stratification, though further validation is needed to clarify its role in prognostic modeling. Full article
(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in the USA)
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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 661
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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