Radiology

Risk inventory and mitigation actions for AI in medical imaging-a qualitative study of implementing standalone AI for screening mammography.

BACKGROUND: Recent prospective studies have shown that AI may be integrated in double-reader setting...

Navigating the Noise: Thoughtful Artificial Intelligence Engagement in Medicine and Radiology.

Gout Diagnosis From Ultrasound Images Using a Patch-Wise Attention Deep Network.

OBJECTIVES: The rising global prevalence of gout necessitates advancements in diagnostic methodologi...

Deep sensorless tracking of ultrasound probe orientation during freehand transperineal biopsy with spatial context for symmetry disambiguation.

BACKGROUND: Diagnosis of prostate cancer requires histopathology of tissue samples. Following an MRI...

Radiomics, machine learning, and deep learning for hippocampal sclerosis identification: a systematic review and diagnostic meta-analysis.

PURPOSE: Hippocampal sclerosis (HS) is the primary pathological finding in temporal lobe epilepsy (T...

Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model.

Unilateral vocal fold paralysis (UVFP) is a condition characterized by impaired vocal fold mobility,...

AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons.

The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer...

Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.

Assessing MGMT promoter methylation is crucial for determining appropriate glioblastoma therapy. Pre...

A novel deep learning-based brain age prediction framework for routine clinical MRI scans.

Physiological brain aging is associated with cognitive impairment and neuroanatomical changes. Brain...

K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Study.

BACKGROUND: Breast cancer has proven to be the most common type of cancer among females around the w...

Exploring Neural Idiosyncrasies in Response to Autonomous Sensory Meridian Response Videos: Naturalistic Functional Magnetic Resonance Imaging Study of Stress and Sensory Processing.

BACKGROUND: Autonomous sensory meridian response (ASMR) videos have been increasingly popularized as...

Development and validation of a cranial ultrasound imaging-based deep learning model for periventricular-intraventricular haemorrhage detection and grading: a two-centre study.

BACKGROUND: Periventricular-intraventricular haemorrhage (IVH) is the most prevalent type of neonata...

Advances in non-invasive diagnostic tools for endometriosis: A narrative review of the past ten years.

Endometriosis is a chronic condition defined by endometrium-like tissue outside the uterus, with sym...

From Data to Decision: Reinventing Hypoxic-Ischemic Encephalopathy Prognosis with Magnetic Resonance Imaging and Machine Learning.

Response by Hijazi et al to Letter Regarding Article, "Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure".

Use of imaging biomarkers and ambulatory functional endpoints in Duchenne muscular dystrophy clinical trials: Systematic review and machine learning-driven trend analysis.

Duchenne muscular dystrophy (DMD) is a rare X-linked genetic muscle disorder affecting primarily ped...

Radiomics meets transformers: A novel approach to tumor segmentation and classification in mammography for breast cancer.

ObjectiveThis study aimed to develop a robust framework for breast cancer diagnosis by integrating a...

Radiomics with Machine Learning Improves the Prediction of Microscopic Peritumoral Small Cancer Foci and Early Recurrence in Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop an interpretable machine learning model using ...

Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.

OBJECTIVE: This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in...

Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images.

Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing ...

Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection.

Breast cancer is a relatively common carcinoma among women worldwide and remains a considerable publ...