From Text to Hologram: Creation of High-Quality Holographic Stereograms Using Artificial Intelligence DOI Creative Commons
Philippe Gentet,

Matteo Coffin,

Yves Gentet

et al.

Photonics, Journal Year: 2024, Volume and Issue: 11(9), P. 787 - 787

Published: Aug. 23, 2024

This study simplified the creation of holographic stereograms using AI-generated prompts, overcoming conventional need for complex equipment and professional software. AI enabled generation detailed perspective images suitable various content styles. The generated were interpolated, upscaled, printed a CHIMERA holoprinter to obtain high-quality holograms. method significantly reduces required time expertise, thereby making accessible. approach demonstrated that can effectively streamline production high-fidelity holograms, suggesting exciting future advancements in technology.

Language: Английский

AI in neurosurgical education: Can machines learn to see like surgeons? DOI
Ari Metalin Ika Puspita, Mimin Ninawati,

Farida Istianah

et al.

Journal of Clinical Neuroscience, Journal Year: 2025, Volume and Issue: unknown, P. 111153 - 111153

Published: March 1, 2025

Language: Английский

Citations

1
Alzheimer’s disease diagnosis using deep learning techniques: datasets, challenges, research gaps and future directions DOI
Asifa Nazir, Assif Assad, Ahsan Hussain

et al.

International Journal of Systems Assurance Engineering and Management, Journal Year: 2024, Volume and Issue: unknown

Published: July 30, 2024

Language: Английский

Citations

7
AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review DOI Creative Commons
Aanuoluwapo Clement David-Olawade, David B. Olawade, Laura Vanderbloemen

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 689 - 689

Published: March 11, 2025

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic minimizing exposure remains fundamental challenge in radiology. Artificial intelligence (AI) emerged transformative solution, enabling low-dose protocols that enhance image quality while significantly reducing doses. This review explores the role AI-assisted imaging, CT, X-ray, magnetic resonance (MRI), highlighting advancements deep learning models, convolutional neural networks (CNNs), other AI-based approaches. These technologies have demonstrated substantial improvements noise reduction, artifact removal, real-time optimization parameters, thereby enhancing accuracy mitigating risks. Additionally, AI contributed to improved radiology workflow efficiency cost reduction by need for repeat scans. also discusses emerging directions AI-driven including hybrid systems integrate post-processing with data acquisition, personalized tailored patient characteristics, expansion applications fluoroscopy positron emission (PET). However, challenges model generalizability, regulatory constraints, ethical considerations, computational requirements must be addressed facilitate broader clinical adoption. potential revolutionize safety, optimizing quality, improving healthcare efficiency, paving way more advanced sustainable future

Language: Английский

Citations

0
An Open Data Collection of 3D Tool and Equipment Models for Neonatology DOI Creative Commons
Serena Bardelli, Gianpaolo Coro, Rosa Teresa Scaramuzzo

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104236 - 104236

Published: Feb. 1, 2025

Language: Английский

Citations

0
HTRecNet: a deep learning study for efficient and accurate diagnosis of hepatocellular carcinoma and cholangiocarcinoma DOI Creative Commons
Jingze Li,

Yupeng Niu,

Jiang Du

et al.

Frontiers in Cell and Developmental Biology, Journal Year: 2025, Volume and Issue: 13

Published: March 24, 2025

Background Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) represent the primary liver cancer types. Traditional diagnostic techniques, reliant on radiologist interpretation, are both time-intensive often inadequate for detecting less prevalent CCA. There is an emergent need to explore automated methods using deep learning address these challenges. Methods This study introduces HTRecNet, a novel framework enhanced precision efficiency. The model incorporates sophisticated data augmentation strategies optimize feature extraction, ensuring robust performance even with constrained sample sizes. A comprehensive dataset of 5,432 histopathological images was divided into 5,096 training validation, 336 external testing. Evaluation conducted five-fold cross-validation applying metrics such as accuracy, area under receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC) against established clinical benchmarks. Results validation cohorts comprised 1,536 normal tissue, 3,380 HCC, 180 HTRecNet showed exceptional efficacy, consistently achieving AUC values over 0.99 across all categories. In testing, reached accuracy 0.97 MCC 0.95, affirming its reliability in distinguishing between normal, CCA tissues. Conclusion markedly enhances capability early accurate differentiation HCC from Its high efficiency position it invaluable tool settings, potentially transforming protocols. system offers substantial support refining workflows healthcare environments focused malignancies.

Language: Английский

Citations

0
Explainable AI in Healthcare Imaging for Medical Diagnosis DOI

Vandana Babbar,

Chetna Kaushal

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 107 - 120

Published: March 7, 2025

The most cutting-edge machine learning and deep techniques in the healthcare industry are presented by Digital Revolution of AI, with an emphasis on explainable artificial intelligence (XAI). This chapter examines how XAI may advance medical field to increase end users' confidence. It covers new ideas uses XAI, making it a intellectual resource for scholars practitioners interested this developing provides comprehensive explanation AI precision medicine, including all aspects. importance Explainable (XAI) is main topic discussion. Also offers real-world case studies examples offer useful insights into use medicine.

Language: Английский

Citations

0
Advancing explainable AI and deep learning in medical imaging for precision medicine and ethical healthcare DOI
Tariq Mahmood, Yu Wang,

Amjad Rehman Khan

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 305 - 338

Published: Jan. 1, 2025

Language: Английский

Citations

0
From Text to Hologram: Creation of High-Quality Holographic Stereograms Using Artificial Intelligence DOI Creative Commons
Philippe Gentet,

Matteo Coffin,

Yves Gentet

et al.

Photonics, Journal Year: 2024, Volume and Issue: 11(9), P. 787 - 787

Published: Aug. 23, 2024

This study simplified the creation of holographic stereograms using AI-generated prompts, overcoming conventional need for complex equipment and professional software. AI enabled generation detailed perspective images suitable various content styles. The generated were interpolated, upscaled, printed a CHIMERA holoprinter to obtain high-quality holograms. method significantly reduces required time expertise, thereby making accessible. approach demonstrated that can effectively streamline production high-fidelity holograms, suggesting exciting future advancements in technology.

Language: Английский

Citations

0