Shehan Bala
Co-founder
See-Mode Technologies
See-Mode utilizes AI algorithms to automatically analyze thyroid and breast ultrasound images, generating precise TI-RADS and BI-RADS reports that enhance diagnostic accuracy. This technology addresses the issues of prolonged reporting times and variability in operator assessments, ensuring consistent and reliable results for radiologists.
- Artificial Intelligence
- Healthcare Technology
- Software Only
Updated 23 months ago
Funding
Raised to date
$11.5MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Manual analysis of thyroid and breast ultrasound images is time-consuming and prone to inter-operator variability, leading to potential inconsistencies in diagnostic reporting using TI-RADS and BI-RADS classifications. This can result in delayed diagnoses and increased workload for radiologists.
Solution
See-Mode offers an AI-powered platform that automates the analysis of thyroid and breast ultrasound images to generate standardized radiology reports. The software automatically detects, sizes, and characterizes thyroid nodules, creating complete TI-RADS sonographer worksheets and preliminary radiologist impressions. For breast imaging, See-Mode identifies lesions and automates BI-RADS reporting, including lesion characteristics and suspicion levels. By reducing reporting time and variability, See-Mode enhances diagnostic confidence and ensures consistent, high-quality ultrasound assessments.
Target Audience
The primary target audience includes radiologists and sonographers in hospitals, imaging centers, and radiology clinics who perform thyroid and breast ultrasound imaging.
Features
- AI-driven automatic detection, sizing, and characterization of thyroid nodules in ultrasound images
- Automated generation of TI-RADS sonographer worksheets and preliminary radiologist impressions
- AI-powered identification of breast lesions in ultrasound imaging
- Automated BI-RADS reporting, including lesion characteristics and suspicion level
- Reduction in inter-operator variability in ultrasound assessment
- Streamlined workflow for radiologists, reducing reporting time