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Showing 6 of 46 academic articles

Explainable and reliable kidney CT image classification using self-supervised DeiT-Tiny transformer

Sai Sri Hemantha Konala, Srinivas Koppu • Frontiers in Medicine (2026)

IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmed.2026.1893419

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

Andrea Steuwe, Birte Valentin, Oliver T. Bethge, Alexandra Ljimani et al. • Diagnostics (2022)

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenu...

DOAJ Open Access (DOAJ) DOI: 10.3390/diagnostics12071627

The Integration of Cone Beam Computed Tomography, Artificial Intelligence, Augmented Reality, and Virtual Reality in Dental Diagnostics, Surgical Planning, and Education: A Narrative Review

Aida Meto, Gerta Halilaj • Applied Sciences (2025)

(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15116308

AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction

Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang • JMIR Medical Informatics (2025)

This viewpoint article explores the transformative role of artificial intelligence (AI) in predicting perioperative hypoxemia through the integration of deep learning with multimodal clinical data, including lung imaging, pulmonary function...

DOAJ Open Access (DOAJ) DOI: 10.2196/73995