AI-powered analysis of stent healing streamlines assessment in heart patients

A research team from Helmholtz Munich, the Technical University of Munich (TUM) and the TUM University Hospital has developed DeepNeo, an AI-powered algorithm that automates the process of analyzing coronary stents after implantation.

The tool matches medical expert accuracy while significantly reducing assessment time. With strong validation in both human and animal models, DeepNeo has the potential to standardize monitoring after stent implantation and thus improve cardiovascular treatment outcomes. The research is published in Communications Medicine.

Each year, more than 3 million people worldwide are treated with stents to open blocked blood vessels caused by heart disease. However, monitoring the healing process after implantation remains a challenge.

If the tissue growing over the stent develops irregularly—either becoming too thick or forming deposits—it can lead to complications, such as re-narrowing or occlusion of the blood vessel. Currently, analyzing these healing patterns in intravascular optical coherence tomography (OCT) images is time-consuming and impractical for routine clinical practice.

A research team from Helmholtz Munich and the TUM University Hospital has now developed DeepNeo, an artificial intelligence (AI) algorithm that can automatically assess stent healing in OCT images. DeepNeo differentiates between different healing patterns with an accuracy comparable to clinical experts—but in a fraction of the time. The AI tool also provides precise measurements, e.g., regarding tissue thickness and stent coverage, offering valuable insights for patient management.

“With DeepNeo, we can achieve an automated, standardized, and highly accurate analysis of stent and vascular healing that was previously only possible through extensive manual effort,” says Valentin Koch, first author of the study introducing the algorithm. “DeepNeo is as good as a doctor, but much faster.”

To train DeepNeo, researchers used 1,148 OCT images from 92 patient scans, manually annotated to classify different types of tissue growth. They then tested the AI algorithm in an animal model, where it correctly identified unhealthy tissue in 87% of cases when compared to detailed laboratory analysis, the current gold standard. When analyzing human scans, DeepNeo also demonstrated high precision, closely matching expert assessments.

“DeepNeo demonstrates how machine learning can support clinicians in making quicker, more informed treatment decisions. The next step is now to effectively integrate AI algorithms like DeepNeo into clinical practice,” explains Dr. Carsten Marr, Director at the Institute of AI for Health at Helmholtz Munich.

His colleague Prof. Julia Schnabel, who leads the Institute of Machine Learning in Biomedical Imaging and is Professor of Computational Imaging and AI in Medicine at TUM, envisions DeepNeo as part of an AI-powered health care system that could offer unprecedented certainty for clinical decision-making.

The project has received a Helmholtz Innovation Grant, and a patent application has been filed. Ascenion, technology transfer partner in the life sciences, is supporting the DeepNeo team in identifying potential industry partners.

“DeepNeo facilitates and standardizes OCT imaging assessment after stent implantation and thus improves clinical decision-making,” say PD Dr. med. Philipp Nicol and Prof. Dr. med. Michael Joner, cardiologists at the TUM University Hospital, who led the project from the clinical side. “This has the potential to not only reduce health care costs but pave the way for more effective and personalized cardiovascular treatments.”

A research team from Helmholtz Munich, the Technical University of Munich (TUM) and the TUM University Hospital has developed DeepNeo, an AI-powered algorithm that automates the process of analyzing coronary stents after implantation.

The tool matches medical expert accuracy while significantly reducing assessment time. With strong validation in both human and animal models, DeepNeo has the potential to standardize monitoring after stent implantation and thus improve cardiovascular treatment outcomes. The research is published in Communications Medicine.

Each year, more than 3 million people worldwide are treated with stents to open blocked blood vessels caused by heart disease. However, monitoring the healing process after implantation remains a challenge.

If the tissue growing over the stent develops irregularly—either becoming too thick or forming deposits—it can lead to complications, such as re-narrowing or occlusion of the blood vessel. Currently, analyzing these healing patterns in intravascular optical coherence tomography (OCT) images is time-consuming and impractical for routine clinical practice.

A research team from Helmholtz Munich and the TUM University Hospital has now developed DeepNeo, an artificial intelligence (AI) algorithm that can automatically assess stent healing in OCT images. DeepNeo differentiates between different healing patterns with an accuracy comparable to clinical experts—but in a fraction of the time. The AI tool also provides precise measurements, e.g., regarding tissue thickness and stent coverage, offering valuable insights for patient management.

“With DeepNeo, we can achieve an automated, standardized, and highly accurate analysis of stent and vascular healing that was previously only possible through extensive manual effort,” says Valentin Koch, first author of the study introducing the algorithm. “DeepNeo is as good as a doctor, but much faster.”

To train DeepNeo, researchers used 1,148 OCT images from 92 patient scans, manually annotated to classify different types of tissue growth. They then tested the AI algorithm in an animal model, where it correctly identified unhealthy tissue in 87% of cases when compared to detailed laboratory analysis, the current gold standard. When analyzing human scans, DeepNeo also demonstrated high precision, closely matching expert assessments.

“DeepNeo demonstrates how machine learning can support clinicians in making quicker, more informed treatment decisions. The next step is now to effectively integrate AI algorithms like DeepNeo into clinical practice,” explains Dr. Carsten Marr, Director at the Institute of AI for Health at Helmholtz Munich.

His colleague Prof. Julia Schnabel, who leads the Institute of Machine Learning in Biomedical Imaging and is Professor of Computational Imaging and AI in Medicine at TUM, envisions DeepNeo as part of an AI-powered health care system that could offer unprecedented certainty for clinical decision-making.

The project has received a Helmholtz Innovation Grant, and a patent application has been filed. Ascenion, technology transfer partner in the life sciences, is supporting the DeepNeo team in identifying potential industry partners.

“DeepNeo facilitates and standardizes OCT imaging assessment after stent implantation and thus improves clinical decision-making,” say PD Dr. med. Philipp Nicol and Prof. Dr. med. Michael Joner, cardiologists at the TUM University Hospital, who led the project from the clinical side. “This has the potential to not only reduce health care costs but pave the way for more effective and personalized cardiovascular treatments.”