Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis

2026-02-10

AI Summary

This study addresses the critical gap in rigorously validated open-source medical imaging tools and the limited transparency of commercial solutions, which often hinder successful clinical deployment. The authors developed and validated two fully open-source, FDA 510(k)-cleared deep learning pipelines for opportunistic analysis of CT images: one for abdominal aortic quantification (AAQ) to assess aneurysm size and another for bone mineral density (BMD) estimation to screen for osteoporosis risk. Leveraging automated segmentation of the abdominal aorta and vertebrae, the models enable precise aortic diameter measurement and trabecular bone density estimation. Evaluated on 258 and 371 patients respectively, the AAQ pipeline achieved a mean absolute error of 1.57 mm, while the BMD pipeline demonstrated 81.0% sensitivity and 78.4% specificity—performance levels meeting clinical utility thresholds. This work represents the first instance of open-source AI algorithms for medical imaging receiving FDA clearance, substantially enhancing regulatory transparency and enabling both clinical prescreening and scientific reproducibility.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: AI for Accessibility

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imaging adds value to medical imaging without additional imaging costs or patient radiation exposure. However, many open-source image analysis solutions lack rigorous validation while commercial solutions lack transparency, leading to unexpected failures when deployed. Here, we report development and validation for two of the first fully open-sourced, FDA-510(k)-cleared deep learning pipelines to mitigate both challenges: Abdominal Aortic Quantification (AAQ) and Bone Mineral Density (BMD) estimation are both offered within the Comp2Comp package for opportunistic analysis of computed tomography scans. AAQ segments the abdominal aorta to assess aneurysm size; BMD segments vertebral bodies to estimate trabecular bone density and osteoporosis risk. AAQ-derived maximal aortic diameters were compared against radiologist ground-truth measurements on 258 patient scans enriched for abdominal aortic aneurysms from four external institutions. BMD binary classifications (low vs. normal bone density) were compared against concurrent DXA scan ground truths obtained on 371 patient scans from four external institutions. AAQ had an overall mean absolute error of 1.57 mm (95% CI 1.38-1.80 mm). BMD had a sensitivity of 81.0% (95% CI 74.0-86.8%) and specificity of 78.4% (95% CI 72.3-83.7%). Comp2Comp AAQ and BMD demonstrated sufficient accuracy for clinical use. Open-sourcing these algorithms improves transparency of typically opaque FDA clearance processes, allows hospitals to test the algorithms before cumbersome clinical pilots, and provides researchers with best-in-class methods.

Problem

• open-source
• FDA clearance
• medical imaging
• algorithm validation
• transparency

Innovation

• open-source AI
• FDA-cleared deep learning
• opportunistic imaging
• computed tomography biomarkers
• clinical validation