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Children’s Hospital of Philadelphia (CHOP) uses open-source, AI-assisted medical-imaging software to turn a child’s existing CT, MRI, or 3D-ultrasound scans into a patient-specific three-dimensional heart model. The workflow, built around Project MONAI and tools including MONAI Label and Auto3DSeg, can produce a model in seconds rather than the roughly four hours of skilled manual work previously required, according to NVIDIA’s September 15, 2026 report.
The model gives cardiologists, surgeons, and catheterization teams a detailed way to inspect unusual anatomy and rehearse possible approaches before a procedure. It can support device selection, repair planning, team communication, and family education. It is an aid to clinical judgment—not an autonomous diagnosis or treatment system.
Why patient-specific modeling matters in congenital heart disease
Congenital heart defects affect about 1% of live births, according to NVIDIA’s 2026 account of the CHOP work. The central challenge is that each child’s anatomy can differ substantially. A device designed for a typical structure, or a surgical plan based on a generic diagram, may not fit a particular heart.
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“You’ve got a one-of-a-kind kid and an off-the-shelf device,” CHOP cardiologist and researcher Dr. Matthew Jolley told NVIDIA. “Our job is to find what fits — and modeling lets us do that before anyone goes into the cath lab or operating room.”
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Three-dimensional modeling does not create new clinical information from nothing. It reorganizes information already present in scans into an anatomical representation that can be rotated, measured, shared, and examined from viewpoints that are difficult to obtain from two-dimensional slices alone.
How CHOP’s AI-assisted heart-modeling workflow works
The process combines medical images, automated or assisted segmentation, computer-aided design, and clinician review. The exact sequence can vary with the child’s anatomy and the intended procedure, but the reported workflow follows five stages.
1. The team starts with images the child already has
A care team supplies cardiac CT, cardiac MRI, or 3D-ultrasound data collected as part of the child’s evaluation. The modeling service is therefore connected to an existing diagnostic pathway rather than requiring a separate scan solely to make a model. The usable detail depends on the image quality, the structures captured, and the clinical question the team is trying to answer.
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2. AI-assisted segmentation identifies the anatomy
Segmentation separates the structures that matter from the surrounding image data. In this workflow, the targets can include cardiac chambers, vessels, valves, septal defects, and related structures. NVIDIA describes CHOP using MONAI Label and Auto3DSeg in this stage.
Project MONAI is an open-source medical-imaging AI project. In practical terms, its tools can help a specialist outline anatomy consistently and quickly, while still allowing the specialist to inspect and correct the result. Automated segmentation is not the same as accepting an algorithm’s first output without review: congenital anatomy can be unusually shaped, small, or intertwined with nearby structures.
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3. The service creates a digital heart model
Once the relevant structures have been segmented and checked, the software generates a patient-specific digital model. NVIDIA reports that the service can produce a model in seconds. That is a major change from the earlier process described in the published CHOP service review, where image segmentation and computer-aided-design modeling required a highly skilled user and could take about four hours of manual work.
4. Clinicians review the model in the format that fits the case
CHOP’s Cardiac Center 3D Imaging Review Suite supports several representations:
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- Digital models: anatomy can be viewed and manipulated on a workstation.
- 3D PDFs: a shareable digital representation can support review among members of the care team.
- Physical prints: a tangible model can help clinicians examine spatial relationships by hand.
- Virtual reality: an immersive view can let a team walk through complex anatomy before a procedure.
These are different ways to inspect the same patient-specific anatomy, not separate diagnoses. The appropriate output depends on whether the team needs a quick digital review, a physical object for planning, or an immersive view for discussion and simulation.
5. The model informs a procedure plan
Clinicians use the model to consider access routes, device dimensions and placement, repair strategies, and the relationships among chambers, vessels, valves, and defects. The final plan remains the responsibility of the treating team, which must reconcile the model with the original images, the child’s examination, physiology, and the risks of each option.
Where the model can help before treatment
Planning complex repairs
Congenital-heart procedures can involve multiple connected abnormalities. A three-dimensional view can make it easier to understand which structures are connected, where a defect sits in relation to a valve or vessel, and which route may provide the safest access. CHOP’s published service review found that complex biventricular repair was its most common indication, accounting for 30 cases, or 31% of the clinical modeling cases reported.
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Preparing for catheter procedures
Before a catheterization, the team can use the model to think through the path from the entry point to the target and to evaluate whether an available device is likely to fit the child’s anatomy. This does not guarantee successful placement, but it can move some of the spatial problem-solving out of the procedure room and into planning.
Understanding multiple ventricular septal defects
Repair of multiple ventricular septal defects was the second most common indication in the CHOP review, with 11 cases, or 12% of the clinical modeling cases. When several openings must be located and addressed, a model can help the team visualize their positions and their proximity to other structures.
Explaining anatomy to families and trainees
Models can also support education. A parent may understand a physical or virtual representation more readily than a stack of scan slices, while trainees can study unusual anatomy without relying solely on a textbook illustration. In CHOP’s 2018–2020 service data, 16 of 112 modeled cases were used for education rather than direct procedural planning.
What CHOP’s published service data show
Ghosh and colleagues’ 2022 review covered the service from 2018 through 2020. It recorded 112 individual 3D-modeling cases. Most were used clinically, and use increased each year.
| Measure | Reported result | What it represents |
|---|---|---|
| Total modeled cases | 112 | Individual cases in the 2018–2020 service review |
| Clinical procedural-planning cases | 96 | Models used to support procedural planning |
| Education cases | 16 | Models used for education |
| Annual cases in 2018 | 15 | Cases modeled during the first year covered |
| Annual cases in 2020 | 47 | Cases modeled during the last year covered |
| STAT category 3–5 use in 2018 | 8% | Share of the hospital’s highest-risk STAT category 3–5 cases that used modeling |
| STAT category 3–5 use in 2020 | 27% | Share of those highest-risk cases that used modeling |
| Most common indication | 30 cases (31%) | Complex biventricular repair |
| Second-most common indication | 11 cases (12%) | Repair of multiple ventricular septal defects |
The figures show adoption and clinical use, not proof that modeling alone caused better surgical outcomes. They also describe one hospital’s service during 2018–2020, so they should not be treated as a universal rate for every pediatric cardiac center.
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What “open source” changes in this setting
Using an open-source foundation can make it easier for a medical-imaging team to adapt workflows to local anatomy, imaging protocols, and review practices. In CHOP’s case, MONAI provides the framework around which segmentation and modeling components can be assembled, while MONAI Label and Auto3DSeg are described as part of the segmentation workflow.
Open source does not remove the need for governance. A hospital still has to decide which software versions to deploy, how to validate them on its own scans, who can approve a segmentation, how models are stored, and how changes are documented. The openness of a tool also does not by itself establish regulatory clearance for every use or prove that a model is accurate for every diagnosis.
Digital, printed, and virtual-reality models: choosing the representation
| Representation | Best suited to | Practical consideration |
|---|---|---|
| Digital 3D model | Rotating anatomy, inspecting relationships, and reviewing a plan on a computer | Easy to distribute within the clinical team, but requires a compatible viewing setup |
| 3D PDF | Sharing an interactive model for review and discussion | Useful for communication when an immersive system is unnecessary |
| Physical 3D print | Hands-on examination, teaching, and discussing spatial geometry around a planned repair | Requires printing time, materials, and a workflow for producing an accurate physical copy |
| Virtual reality | Immersive walkthroughs of complex anatomy and team planning | Requires VR equipment and a suitable review environment |
The representation should match the question. A surgeon may want an object to hold, a catheter team may prefer a manipulable digital view, and a teaching session may benefit from VR. None of these formats substitutes for checking the source scans.
Benefits that are documented—and benefits that remain unproven
Documented benefits
- Faster production: NVIDIA reports model generation in seconds, compared with roughly four hours of earlier skilled manual work.
- Improved visualization: A model presents patient-specific spatial relationships in digital, printed, or immersive form.
- Support for difficult planning: CHOP used most of its reviewed models for procedural planning, including complex biventricular repairs and multiple ventricular septal defects.
- Growing integration: Annual modeled cases rose from 15 in 2018 to 47 in 2020, and use among STAT category 3–5 cases rose from 8% to 27%.
- Team communication: A common 3D representation can help surgeons, cardiologists, interventionalists, trainees, and families discuss the same anatomy.
Claims the available evidence does not establish
- It does not establish a population-wide reduction in complications, mortality, procedure time, or repeat operations.
- It does not show that every child needs a 3D model or that a model is superior to conventional imaging for every case.
- It does not establish universal regulatory clearance for all software, model types, or clinical uses.
- It does not show autonomous treatment decisions. Clinicians remain responsible for validating the anatomy and choosing the intervention.
NVIDIA’s account includes an individual child whose repair succeeded after the model clarified a difficult ventricular septal defect. That is a clinically meaningful example, but it is an anecdote rather than a controlled outcomes study. A 2023 review of patient-specific 3D-printed models describes benefits for planning, simulation, education, and communication while characterizing the evidence base as still evolving.
Limits, risks, and operational requirements
Segmentation still needs expert oversight
The 2022 CHOP review describes segmentation and computer-aided-design modeling as a time-consuming process requiring a highly skilled user. AI can reduce the manual burden, but a specialist still needs to check whether the software has correctly separated chambers, vessels, valves, and defects. An error in a small vessel or a thin septal boundary could matter to a procedural plan.
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Image quality limits the model
A model cannot recover anatomy that the source scan does not capture clearly. Motion, artifacts, limited resolution, or incomplete coverage can make segmentation harder. The team may need to compare more than one imaging modality or decide that a particular structure is not reliable enough to include in a planning model.
Models are representations, not living physiology
A static model shows geometry. It does not automatically reproduce blood flow, tissue elasticity, changing pressure, or how anatomy will behave during anesthesia or intervention. Those clinical factors remain part of the team’s assessment.
Implementation takes people and process
A hospital needs trained reviewers, image-handling procedures, software validation, secure data storage, and a way to communicate model limitations. Printing and VR add equipment and scheduling requirements. The published sources do not provide a universal price or staffing formula, so institutions must determine those requirements locally.
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- Which scan or combination of scans was used to build the model?
- Which chambers, vessels, valves, and defects were segmented?
- Who reviewed and approved the segmentation?
- Is the model being used for procedural planning, education, or both?
- Which format—digital, 3D PDF, printed, or VR—will be most useful for this decision?
- What parts of the anatomy are uncertain because of image quality or resolution?
- How does the model change the proposed device, access route, or repair strategy?
- Will the team compare the model with the original CT, MRI, or ultrasound images before acting?
The practical takeaway
CHOP’s experience shows a credible use of open-source AI in pediatric cardiac care: software can accelerate the labor-intensive step of turning medical images into a precise, patient-specific 3D heart model, then give clinicians a clearer way to plan complex interventions. The strongest evidence so far is faster modeling, broader clinical use, and better visualization of unusual anatomy. Whether these tools improve outcomes across larger populations remains an open clinical question, and every model still requires expert review and judgment.
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