# Software-guided Operative Planning of MitraClip Placement

> **NIH NIH R44** · 3DT HOLDINGS, LLC · 2024 · $480,989

## Abstract

ABSTRACT
Mitral regurgitation (MR) is the most common type of valvular heart disease in patients over age 75. Almost half
of the patients identified with moderate-severe MR are not candidates for open-heart surgery due to frailty and
co-morbidities. MitraClip (MC) is a recent percutaneous approach whereby a clip is placed in the center of the
MR “jet” to reduce MR. Currently, when clinicians prepare to place the MC on the mitral valve (MV), they have
data on the degree of MR, and the size and shape of the MV and LV measured using real-time 3D
transesophageal echocardiography (RT3D-TEE). Myocardial and leaflet stresses, however, are not considered
in the current MC placement strategy. Additionally, mean left atrial pressure (MLAP) has been introduced to
assess long-term MC outcomes. The objective of this Fast-Track proposal is to develop and validate a machine
learning (ML)-based MC placement software tool (MCP-ST) for finding the optimal MC scenarios and real-time
predictions of MR, MLAP, MV leaflet stresses, and myocardial stresses, which are known to affect, respectively,
the safety of device placements and the cardiac function. Accordingly, Specific Aims are proposed: Phase I)
Generate a dataset of MR, MLAP, MV, and LV stress. AIM 1: Finite element (FE) structural simulation of
the MV + LV of patients from a retrospective data base. To add additional retrospective patient data to our
current dataset. Milestone 1: A dataset of MV geometrical parameters, MR, MLAP, MV stress, and LV stress
from over 5,000 FE models obtained from over 1,000 patient images. Timeframe: 6 months from the award date.
Phase II) Automating computing MR, MLAP, MV, and LV stress from DICOM data and testing the results
using animal experiments. To automate the prediction of MC therapy outcomes by approximating MR, MLAP,
MV, and LV stresses from echo images. Milestone 2: Manually process echo images to create a dataset of echo
images. Milestone 3: ML model development to process echo images and integrate this ML model into the ML
workflow that predicts MR, MLAP, MV, and LV stress. Timeframe: 12 months from the award date. Milestone 3:
Assessing the performance of the respective ML workflow in swine. Timeframe: 24 months from the award date.
AIM 2: Automation of echo image processing for ML predictions and Validation of Animal Studies. To
develop an ML platform that predicts MR, MLAP, MV, and LV stresses from echo images and to assess its
performance using swine experiments. Timeframe: 24 months from the award date. AIM 3: Prepare
submission package for software as a medical device. To submit a package to FDA that includes good
laboratory practices (GLP) animal study, verification and validation of software, as well as full quality
documentation. Milestone 4: Assess the performance of the respective ML workflow in swine. Timeframe: 30
months from the award date. Moreover, the proposed research provides a template for developing and validating
ML algorithms concerned with oth...

## Key facts

- **NIH application ID:** 10820659
- **Project number:** 1R44HL169034-01A1
- **Recipient organization:** 3DT HOLDINGS, LLC
- **Principal Investigator:** Julius Matteo Guccione
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $480,989
- **Award type:** 1
- **Project period:** 2024-09-20 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10820659

## Citation

> US National Institutes of Health, RePORTER application 10820659, Software-guided Operative Planning of MitraClip Placement (1R44HL169034-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10820659. Licensed CC0.

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