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M
Mahmut S. Gokmen
University of Kentucky
M
Moneera N. Haque
University of Kentucky
S
Steve W. Leung
University of Kentucky
C
Caroline N. Leach
University of Kentucky
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References (27)
[3]
Lesion-specific coronary artery calcium score to predict stent underexpansion
Founder's Pitch
"AI-enhanced framework that generalizes coronary artery calcium scoring across different CT modalities for scalable cardiovascular screening."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
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4/4 signals
Series A Potential
3/4 signals
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Why It Matters
This research addresses a significant gap in cardiovascular risk screening by automating coronary artery calcium scoring in non-specialist settings. By enabling the use of non-gated CT scans, this method allows for more frequent and widespread screening without requiring additional imaging resources, thus optimizing healthcare resource utilization and potentially identifying at-risk patients sooner.
Product Angle
The framework could be productized as a cloud-based or on-premise software tool for healthcare providers, allowing integration with existing CT imaging devices and systems via industry-standard interfaces like OHIF and MONAI for seamless clinical deployment.
Disruption
This tool could replace or significantly augment current manual or semi-manual calcium scoring processes, which are often operator-dependent and time-consuming, thereby streamlining diagnosis workflows and increasing patient throughput.
Product Opportunity
The target is healthcare providers who want to improve cardiovascular disease risk assessment by leveraging CT imaging they already perform for other conditions. The market for cardiovascular diagnostics, especially in middle to large hospitals worldwide, is sizable, offering significant uptake potential.
Use Case Idea
An application for hospitals and clinics that incorporates the AI framework to process both gated and non-gated CT scans for automated coronary calcium scoring, enabling better cardiovascular risk assessment in routine clinical workflows.
Science
The framework uses a Vision Transformer model, CARD-ViT, pretrained on gated CT scan data to perform coronary artery calcium scoring. Through self-supervised learning with the DINO technique, it can generalize well from only gated CT training data to non-gated CT applications. This approach facilitates automated calcium detection and scoring without needing domain-specific annotations, overcoming significant challenges in cross-domain adaptation.
Method & Eval
The framework was evaluated using internal and external datasets. It achieved high segmentation and scoring accuracy on gated CT datasets, and demonstrated comparable performance to existing models trained specifically on non-gated datasets, proving the effectiveness of the cross-domain approach.
Caveats
Potential challenges include integration with various hospital IT systems, ensuring HIPAA compliance, and gaining trust among radiologists accustomed to established methods. False positives from improved models may also pose issues if not addressed effectively in clinical settings.
Author Intelligence
Mahmut S. Gokmen
University of Kentucky
Moneera N. Haque
University of Kentucky
Steve W. Leung
University of Kentucky
Caroline N. Leach
University of Kentucky
Seth Parker
University of Kentucky
Stephen B. Hobbs
University of Kentucky
Vincent L. Sorrell
University of Kentucky
W. Brent Seales
University of Kentucky
V. K. Cody Bumgardner
University of Kentucky