Orchid Imaging develops a body imaging system that utilizes geometric modeling and deep learning to automate lesion detection and real-time comparison. This technology provides a cost-effective solution for accurate cancer risk assessment, reducing unnecessary biopsies and improving patient outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional methods for lesion detection in body imaging are often manual, time-consuming, and prone to human error, leading to potential delays in diagnosis and treatment. The lack of automated tools for real-time comparison of imaging data makes it challenging to accurately assess cancer risk and monitor disease progression. This can result in unnecessary biopsies and increased healthcare costs.
Solution
Orchid Imaging is developing a body imaging system that leverages geometric modeling and deep learning to automate lesion detection and enable real-time comparison of imaging data. The system aims to improve the accuracy and efficiency of cancer risk assessment by providing clinicians with an automated solution for identifying and tracking lesions over time. By reducing the need for manual analysis and minimizing the potential for human error, Orchid Imaging's technology seeks to reduce unnecessary biopsies and improve patient outcomes. The system's automated capabilities also aim to streamline the diagnostic process, potentially leading to earlier detection and treatment of cancer.
Target Audience
The primary target audience includes radiologists, oncologists, and other healthcare professionals involved in cancer diagnosis and treatment, as well as hospitals and imaging centers.
Features
- Automated lesion detection using deep learning algorithms
- Geometric modeling for precise anatomical representation
- Real-time comparison of imaging data for tracking lesion changes over time
- Automated cancer risk assessment based on imaging analysis