Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis
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.
Cite
@article{arxiv.2602.10364,
title = {Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis},
author = {Adrit Rao and Malte Jensen and Andrea T. Fisher and Louis Blankemeier and Pauline Berens and Arash Fereydooni and Seth Lirette and Eren Alkan and Felipe C. Kitamura and Juan M. Zambrano Chaves and Eduardo Reis and Arjun Desai and Marc H. Willis and Jason Hom and Andrew Johnston and Leon Lenchik and Robert D. Boutin and Eduardo M. J. M. Farina and Augusto S. Serpa and Marcelo S. Takahashi and Jordan Perchik and Steven A. Rothenberg and Jamie L. Schroeder and Ross Filice and Leonardo K. Bittencourt and Hari Trivedi and Marly van Assen and John Mongan and Kimberly Kallianos and Oliver Aalami and Akshay S. Chaudhari},
journal= {arXiv preprint arXiv:2602.10364},
year = {2026}
}
Comments
Adrit Rao, Malte Jensen, Andrea T. Fisher, Louis Blankemeier: Co-first authors. Oliver Aalami, Akshay S. Chaudhari: Co-senior authors