AI Medical Compendium Journal:
Abdominal radiology (New York)

Showing 1 to 10 of 100 articles

Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
OBJECTIVE: Microsatellite instability (MSI) is a novel predictive biomarker for chemotherapy and immunotherapy response, as well as prognostic indicator in colorectal cancer (CRC). The current standard for MSI identification is polymerase chain react...

External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT.

Abdominal radiology (New York)
PURPOSE: This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geo...

Multi-modal large language models in radiology: principles, applications, and potential.

Abdominal radiology (New York)
Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most exist...

Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
OBJECTIVE: To evaluate the diagnostic performance of Machine Learning (ML) and Deep Learning (DL) models for predicting preoperative Lymph Node Metastasis (LNM) in Colorectal Cancer (CRC) patients.

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.

Abdominal radiology (New York)
BACKGROUND: In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-a...

Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques.

Abdominal radiology (New York)
Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and develop...

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

Abdominal radiology (New York)
PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.

Abdominal radiology (New York)
An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is...

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Abdominal radiology (New York)
Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques,...