Explore Cutting-Edge ASI Clinical Studies
A new breast cancer study demonstrates how ASI’s HiPath Pro platform support AI-driven predictive oncology through high-quality digital pathology imaging.
ASI’s second abstract presented at ECP 2025 demonstrates strong agreement between manual and computational scoring of Ki-67 in breast cancer. The study validates ASI’s AI-powered quantification tools as a reliable complement to routine pathology assessment.
This study shows that computer-aided scoring provides a reliable and reproducible method for assessing HER2 immunostaining in breast cancer with high intra-tumoral heterogeneity.
Mendel Laboratories in Mexico used ASI’s HiBand system to demonstrate the efficiency gains of AI-powered karyotyping – significantly reducing analysis time for the analysis of both normal and abnormal cases.
Discover ASI’s new AI-powered Q-banded karyotyping solution, delivering high accuracy and fewer manual corrections. This innovation streamlines cytogenetic workflows for faster, more efficient chromosome analysis.
A joint study by Soroka Medical Center and Applied Spectral Imaging shows that digital FISH using ASI’s HiFISH platform significantly reduces analysis time – by 40% – compared to manual methods.
This abstract demonstrates the practical impact of AI-assisted chromosome analysis using ASI’s HiBand platform. Read how AI can reduce analysis time, maintain high accuracy across varied band resolutions, and support lab efficiency.
This recent review by Wake Forest University School of Medicine explores the emergence of AI-guided karyotyping, featuring ASI’s HiBand system as a leading example.
ASI enhances interoperability in chromosome analysis, enabling AI-powered karyotyping without replacing existing imaging systems. Our latest study demonstrates the seamless import of third-party scanned metaphases into ASI’s platform, automating karyogram generation and optimizing analysis efficiency.
ASI’s AI-powered karyotyping technology enhances efficiency in cytogenetics labs by significantly reducing analysis time. Our latest study demonstrates a 53% reduction in karyotyping time for peripheral blood samples using AI-based automation, improving accuracy and streamlining workflows.
A recent study published by Dr. Lui Ng and Colleagues highlights the use of digital image analysis for assessing prognostic biomarkers like CDH17 in colorectal cancer (CRC).
Detecting genetic abnormalities in cancer, crucial for diagnosis and treatment, often uses fluorescence in-situ hybridization (FISH). Automated slide scanners enhance FISH analysis by reducing microscope bottlenecks, improving accuracy, and enabling remote reviews.