Reset
Showing 10 of 450 academic articles

Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping

Rieke Löper, Lennart Abels, Daniel Otero Baguer, Felix Bremmer et al. • Dermatopathology (2025)

Lentigo maligna (LM) is a melanoma in situ with high cumulative sun damage. Histological evaluation of resection margins is difficult and time-consuming. Melanocyte density (MD) is a suitable, quantifiable, and reproducible diagnostic crite...

DOAJ Open Access (DOAJ) DOI: 10.3390/dermatopathology13010001

Artificial intelligence and echocardiography

Maryam Alsharqi, William J. Woodward, J. A. Mumith, D. C. Markham et al. • Echo Research and Practice (2018)

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experie...

OPENALEX cc-by DOI: https://doi.org/10.1530/erp-18-0056 PMC: 6280250

Achieving health equity through healthcare technology: Perspective from India

Prakamya Gupta, Ranjan Choudhury, Atul Kotwal • Journal of Family Medicine and Primary Care (2023)

India aims to provide universal health coverage to all individuals and communities thus ensuring accessibility, promotive, curative, preventive, rehabilitative, and palliative health services to all. Healthcare technologies play a critical ...

DOAJ Open Access (DOAJ) DOI: 10.4103/jfmpc.jfmpc_321_23

AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders

Diego D. Díaz-Guerra, Marena de la C. Hernández-Lugo, Yunier Broche-Pérez, Carlos Ramos-Galarza et al. • Frontiers in Psychiatry (2025)

IntroductionEvaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificia...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2024.1516065

Identification of Aspergillus at section and species levels by artificial intelligence-based microscopic morphology image recognition

Meng Tan, Zhe Guo, Yanyi Wang, Xinyi Xu et al. • Journal of Clinical Microbiology (2026)

ABSTRACT Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phyl...

DOAJ Open Access (DOAJ) DOI: 10.1128/jcm.00012-26

Association between myosteatosis and impaired glucose metabolism: A deep learning whole‐body magnetic resonance imaging population phenotyping approach

Matthias Jung, Hanna Rieder, Marco Reisert, Susanne Rospleszcz et al. • Journal of Cachexia, Sarcopenia and Muscle (2024)

Abstract Background There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with th...

DOAJ Open Access (DOAJ) DOI: 10.1002/jcsm.13527

Artificial intelligence in retina

Ursula Margarethe Schmidt-Erfurth, Amir Sadeghipour, Bianca S. Gerendas, Sebastian M. Waldstein et al. • Progress in Retinal and Eye Research (2018)

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in ...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.preteyeres.2018.07.004