OBJECTIVE: To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound-aided mammograms.
At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse events in cancer survivors. The radiation oncologist weighs numerous disease and patient-level factors when considering the expected risk-benefit ratio...
OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH).
OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the fea...
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...
OBJECTIVE: A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lun...
OBJECTIVE: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning.
Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic ...
OBJECTIVE: This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis.