Radiology

Ground-State Descriptor Enables Machine Learning-Assisted Virtual Screening of AIE-Active Mechanofluorochromic Molecules with High Contrast.

Aggregation-induced emission mechanofluorochromic (AIE-MFC) molecules with high-contrast are in high demand for pressure-sensing devices and optoelectronic devices. However, developing AIE-MFC molecules with high-contrast beyond 100 nm still highly relies on scientific intuition through experimental and traditional trial-and-error methods. Herein, we establish a new electronic descriptor E (the or...

Performance of AI methods in PET-based imaging for outcome prediction in lymphoma: A systematic review and meta-analysis.

OBJECTIVES: To evaluate the predictive performance of artificial intelligence (AI) methods using pre-treatment PET-based imaging for outcome prediction in lymphoma through a systematic review and meta-analysis.

Emerging Imaging Technologies in Forensic Medicine: A Systematic Review of Innovations, Ethical Challenges, and Future Directions.

Forensic medicine has increasingly integrated advanced imaging technologies to improve the accuracy and efficiency of investigations. Techniques such ...

Dual-energy CT-based virtual monoenergetic imaging via unsupervised learning.

Since its development, virtual monoenergetic imaging (VMI) derived from dual-energy computed tomography (DECT) has been shown to be valuable in many c...

Accelerated proton resonance frequency-based magnetic resonance thermometry by optimized deep learning method.

BACKGROUND: Proton resonance frequency (PRF)-based magnetic resonance (MR) thermometry plays a critical role in thermal ablation therapies through foc...

From Guidelines to Intelligence: How AI Refines Thyroid Nodule Biopsy Decisions.

OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetic...

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics.

OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) ra...

Selection, visualization, and explanation of deep features from resting-state fMRI for Alzheimer's disease diagnosis.

Despite the remarkable achievements of deep learning networks in analyzing neuroimaging data for various tasks linked to brain functions and disorders...

Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.

Artificial intelligence (AI) and deep learning are increasingly applied in cardiovascular imaging. However, the "black box" nature of these models ra...

Optimizing Attenuation Correction in Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement.

Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In Ga-PSMA PET, however, artifacts such as respiratory mo...

Efficacy of rTMS on SCZ symptoms: Insights from connectome mapping.

Repetitive transcranial magnetic stimulation (rTMS) is a potential treatment for schizophrenia (SCZ), yet its efficacy and underlying mechanisms remai...

Automated labeling using tracked ultrasound imaging: Application in tracking vertebrae during spine surgery.

PURPOSE: Recent advancements in machine learning (ML) allow for rapid analysis of complex image data, which supports the use of ultrasound (US)-based ...

Deep learning reconstruction for T2-weighted and contrast-enhanced T1-weighted magnetic resonance enterography imaging in patients with Crohn's disease: Assessment of image quality and clinical utility.

PURPOSE: To investigate the image quality of deep learning-reconstructed T2-weighted half-Fourier single-shot turbo spin echo (DL T2 HASTE) and contra...

One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.

Although iron is an essential element for vital body functions, iron overload (IO) is accompanied by significant cellular damage due to its accumulati...

The value of artificial intelligence in PSMA PET: a pathway to improved efficiency and results.

INTRODUCTION: This systematic review investigates the potential of artificial intelligence (AI) in improving the accuracy and efficiency of prostate-s...

Machine Learning Models of Voxel-Level [F] Fluorodeoxyglucose Positron Emission Tomography Data Excel at Predicting Progressive Supranuclear Palsy Pathology.

OBJECTIVE: To determine whether a machine learning model of voxel level [f]fluorodeoxyglucose positron emission tomography (PET) data could predict pr...

Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA.

OBJECTIVES: To train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate...

Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.

OBJECTIVES: Accurate preoperative detection and analysis of lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC) is essential ...

Deploying a novel deep learning framework for segmentation of specific anatomical structures on cone-beam CT.

AIM: Cone-beam computed tomography (CBCT) imaging plays a crucial role in dentistry, with automatic prediction of anatomical structures on CBCT images...

End-to-end 2D/3D registration from pre-operative MRI to intra-operative fluoroscopy for orthopedic procedures.

PURPOSE: Soft tissue pathologies and bone defects are not easily visible in intra-operative fluoroscopic images; therefore, we develop an end-to-end M...