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Can CTA-Based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy Is Insufficient?

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Despite advances in endovascular stroke therapy (EST) devices and techniques, many patients are left with substantial disability, even if the final infarct volumes (FIVs) remain small. Here, we evaluate the performance of a ma...

Machine Learning-Based Prediction of Delayed Neurologic Sequelae in Carbon Monoxide Poisoning Using Automatically Extracted MR Imaging Features.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Delayed neurologic sequelae are among the most serious complications of carbon monoxide poisoning. However, no reliable tools are available for evaluating their potential risk. We aimed to assess whether machine learning model...

Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.

European radiology
OBJECTIVES: Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (CRC), causing unnecessarily extended lymphadenectomy. This study aimed to develop and validate a machine learning (ML) classifier utilizing multi-phase...

Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease.

Gut microbes
BACKGROUND AND AIMS: Fecal calprotectin (FCP) has limited specificity as diagnostic biomarker of pediatric inflammatory bowel disease (IBD), leading to unnecessary invasive endoscopies. This study aimed to develop and validate a fecal microbiota and ...

Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement.

Abdominal radiology (New York)
BACKGROUND: Accurate liver-volume measurements from CT scans are essential for treatment planning, particularly in liver resection cases, to avoid postoperative liver failure. However, manual segmentation is time-consuming and prone to variability. A...

Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents.

Skeletal radiology
OBJECTIVE: This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents.

Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.

Artificial cells, nanomedicine, and biotechnology
Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore m...

Improved Breast Cancer Detection with Artificial Intelligence in a Real-World Digital Breast Tomosynthesis Screening Program.

Clinical breast cancer
OBJECTIVE: The purpose of this study is to compare radiologists' breast cancer screening performance before and after the implementation of an artificial intelligence (AI) detection system for digital breast tomosynthesis (DBT).

Relationship between spleen volume and diameter for assessment of response to treatment on CT in patients with hematologic malignancies enrolled in clinical trials.

Abdominal radiology (New York)
PURPOSE: Investigate spleen diameter (d) and volume (v) relationship in patients with hematologic malignancies (HM) by determining volumetric thresholds that best correlate to established diameter thresholds for assessing response to treatment. Explo...