Funding
Funding not disclosed
Founders
Product
Problem
Current methods for evaluating spinal conditions often rely on manual analysis of medical images, which can be time-consuming, subjective, and prone to variability. This can lead to inconsistencies in diagnosis, treatment planning, and post-operative assessment.
Solution
MSKai has developed an AI-powered software platform designed to automate and enhance the analysis of musculoskeletal spine imaging. The platform uses deep learning algorithms to identify, measure, and classify spinal anatomy and pathologies from multi-modality imaging data within seconds. By providing quantitative and qualitative assessments, MSKai aims to improve the accuracy, consistency, and efficiency of spinal care, supporting pre-surgical authorizations, treatment appropriateness evaluations, and risk assessments. The software generates customizable reports with AI-driven segmentations and measurements, assisting radiologists and physicians in making informed clinical decisions.
Target Audience
The primary target audience includes radiologists, physicians, and healthcare providers specializing in spinal care, as well as employers seeking risk assessment tools for employee-injury potential.
Features
- AI-driven segmentation and labeling of T2-weighted lumbar spine MRIs
- Quantitative and qualitative measurement tools for spinal anatomy and pathologies
- Automated identification, measurement, and classification of spinal structures across modalities
- Customizable report generation for consistent and efficient workflows
- AI-generated segmentation and measurements overlaid on a dynamic MRI viewer
- Level-by-level quantitative measurement output based on predetermined threshold language
- Integration of patient imaging data into a single library