An AI-powered platform designed to assist radiologists in automatically detecting abnormal lesions in the liver through CT imaging, provide clinical solutions to aid in the early identification of liver cancer, as well as assist oncologists in developing treatment plans.
DrAid™ CT Liver Cancer D&T
Imagine having an Artificial Intelligence-powered platform that could automatically identify any potential lesions, saving you valuable time and reducing the risk of human errors.
DrAid™ Liver Cancer CT is a revolutionary platform that uses cutting-edge AI technology to help radiologists identify abnormal lesions in the liver with greater accuracy and speed. This innovative platformnot only aids in the detection of cancerous growths, but also assists oncologists with treatment planning, providing them with critical information about the size, location, and type of lesion.
With DrAid™ Liver Cancer CT, doctors can feel confident that they have the most advanced technology at their fingertips, enabling them to provide their patients with the highest quality of care. So why wait? Join the thousands of doctors who have already benefitted from DrAid™ Liver Cancer CT and take your diagnostic and treatment planning capabilities to the next level.
Can classify 4 types of findings: Hepatocellular Carcinoma (HCC),Other malignant lesions than HCC, Benign lesions, Ambiguous lesions
Can detect liver lesions as small as 5mm in diameter
Displays clear warnings without disrupting the doctor’s work
Address societal concerns about dangerous cancer
Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison - - - - - - Authors: Van Ha Tang, Soan T. M. Duong, Thanh M. Huynh, Vo T. Duc, Trung Bui & Steven Q. H. Truong,...
Deep Learning Model With Convolutional Neural Network for Detecting and Segmenting Hepatocellular Carcinoma in CT: A Preliminary Study - - - - - - Authors: Vo Tan Duc, Phan Cong Chien, Le Duy Mai Huyen, Tran Le Minh Chau,...
Dual consistency assisted multi-confident learning for the hepatic vessel segmentation using noisy labels - - - - - - Authors: Nam Phuong Nguyen, Tuan Van Vo, Soan T. M. Duong, Trung Bui, Steven Q. H. Truong