# Uncertainty-Informed Multimodal Infarct Age Prediction from Imaging and Clinical Data

Evariste Njomgue Fotso, Marta Nuñez-Garcia, Buntheng Ly, Hubert Cochet, Maxime Sermesant

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Evariste Njomgue Fotso, Marta Nuñez-Garcia, Buntheng Ly, Hubert Cochet, Maxime Sermesant. Uncertainty-Informed Multimodal Infarct Age Prediction from Imaging and Clinical Data. FIMH 2025 - Functional Imaging and Modeling of the Heart, Radomir Chabiniok; Maria Gusseva; Tarique Hussain; Hoang Nguyen; Vlad Zaha; Qing Zou, Jun 2025, Dallas (TX), United States. 10.1007/978-3-031-94562-5_6. hal-05126700

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# Uncertainty-Informed Multimodal Infarct Age Prediction from Imaging and Clinical Data

Evariste Njomgue Fotso<fnref n="1" />,<fnref n="2" /> Marta Nuñez-Garcia<fnref n="3" />, Buntheng Ly<fnref n="2" />, Hubert Cochet<fnref n="2" />, and Maxime Sermesant<fnref n="1" />,<fnref n="2" />

Contact: maxime.sermesant@inria.fr

**Abstract.** Accurately estimating the age of a myocardial infarction (MI) is crucial for determining clinical outcomes and guiding treatment strategies in post-MI patients, such as in arrhythmia risk stratification. This study addresses this challenge by proposing a novel multimodal regression framework for predicting infarct age. The framework integrates key post-MI biomarkers—including myocardial thickness, intramyocardial fat, calcification, and clinical data—as inputs for infarct age estimation. Leveraging computed tomography (CT) imaging, we quantify and analyze the spatial distribution of wall thinning, intramyocardial fat, and calcifications, which are hallmarks of post-MI structural remodeling. These biomarkers, combined with clinical parameters, are fed into a machine learning-based regression model optimized for temporal precision in infarct dating. We leverage multimodal data fusion using late fusion (decision-level) techniques, integrating information from imaging and clinical data through uncertainty quantification. This study evaluates the framework using regression metrics, demonstrating a significant improvement over traditional imputation methods. Additionally, the pathophysiological correlations between intramyocardial fat accumulation, calcification, myocardial remodeling, and clinical factors are explored across various infarct ages, providing valuable insights into the progression of post-MI remodeling. The results suggest that these multimodal inputs offer complementary information that enhances temporal resolution in infarct dating, paving the way for personalized treatment strategies and improved prognostic evaluations in patients with MI.

**Keywords:** multimodal deep learning · infarct age regression · medical imaging · intramyocardial fat, calcification, myocardial thickness

## 1 Introduction

MI remains a major global health burden, with long-term complications such as arrhythmias contributing significantly to morbidity and mortality. Accurate estimation of infarct age is crucial for understanding myocardial remodeling, predicting arrhythmia risk, and guiding clinical interventions. Post-MI remodeling

<footnote n="1">¹ Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France</footnote>

<footnote n="2">² IHU Liryc, Université de Bordeaux, Bordeaux, France</footnote>

<footnote n="3">³ Universidade De Santiago De Compostela</footnote>
involves complex structural and functional changes, including myocardial thinning, fatty infiltration, calcification, and electrical alterations [19]. This process unfolds in distinct phases, beginning with early ischemic injury and progressively leading to myocardial thinning, fatty infiltration, calcification, and electrical alterations over time [19–21]. Integrating these structural biomarkers with clinical data through advanced multimodal approaches offers a promising pathway for improved risk prediction and therapeutic strategies.

Myocardial thickness is a key marker of structural remodeling after MI. Initially, myocardial edema may cause transient thickening, but chronic infarcts typically exhibit significant thinning over time due to myocyte loss, extracellular matrix reorganization, and infarct expansion. This thinning compromises myocardial integrity, alters conduction dynamics, and increases arrhythmia risk [2]. It is often associated with reduced ejection fraction (EF), a crucial parameter for stratifying arrhythmia risk and assessing post-MI severity [3]. In parallel, fatty infiltration, another hallmark of chronic infarcts, contributes to electrical heterogeneity by replacing necrotic myocytes with adipose tissue, which disrupts signal propagation and promotes reentrant arrhythmias [5]. The extent of intramyocardial fat also serves as an indicator of infarct age, providing valuable insights into chronic remodeling [6].

Calcification, a marker of advanced scar maturation, further complicates post-MI remodeling by creating mechanically stiff, non-conductive regions that hinder electrical propagation. These calcified areas, often coexisting with myocardial thinning and fatty infiltration, compound the arrhythmogenic potential. Advances in cardiac CT now enable precise quantification of myocardial calcification, improving arrhythmia risk stratification [7]. Additionally, clinical factors such as age, sex, diabetes, hypertension, and dyslipidemia significantly influence remodeling. Older patients exhibit more pronounced myocardial thinning and scar maturation, while comorbidities accelerate adverse remodeling through oxidative stress and inflammation. Smoking and reduced EF further exacerbate these changes, highlighting the necessity of integrating clinical data with structural biomarkers for comprehensive risk assessment [8, 9].

Recent advancements in machine learning have transformed cardiovascular risk prediction by integrating diverse datasets, including imaging biomarkers and clinical parameters, resulting in more quantitative multimodal approaches. Multimodal data fusion techniques integrate information from diverse data sources at various levels, including early (data-level), intermediate (feature-level), and late (decision-level) fusion, incorporating uncertainty quantification (UQ) to enhance predictive confidence [11]. UQ is a methodological framework that assesses and models the confidence in predictions by distinguishing between aleatoric (data-related) and epistemic (model-related) uncertainties, enhancing the reliability and interpretability of machine learning models [23].

Late fusion strategies, which combine independent modality-specific models at the decision level, offer flexibility in handling missing data while preserving modality-specific feature representations. UQ in late fusion further improves predictive reliability by assessing confidence in model outputs, which is crucial for
clinical decision-making. These multimodal models have demonstrated the capability to handle high-dimensional data and generate explainable predictions, thereby enhancing clinical decision-making. Recent studies have demonstrated the effectiveness of incorporating UQ in multimodal learning for cardiovascular disease diagnosis. In particular, UQ-based fusion strategies have been successfully applied to myocardial infarction diagnosis using echocardiography and electrocardiograms, improving robustness in decision-making under uncertain conditions [24]. Additionally, late fusion strategies incorporating UQ have been successfully applied in medical imaging for segmentation tasks, enhancing robustness in multimodal data integration [22]. These advancements highlight the importance of integrating UQ into multimodal learning frameworks to improve interpretability and reliability in cardiovascular risk assessment.

Building on this progress, accurate infarct age estimation becomes particularly crucial in clinical scenarios such as silent MI — a known risk factor for sudden cardiac death — where delayed detection complicates timely intervention [4]. Furthermore, in cases where infarct age data is incomplete, robust regression models can provide reliable predictions, supporting more informed therapeutic decisions. We propose a multimodal infarct age regression model that integrates advanced imaging biomarkers with clinical data, offering a novel and effective solution for improving risk prediction and personalised post-MI management.

## 2 Method

The proposed approach for multimodal infarct age regression involves the quantification of intramyocardial fat, calcification, and myocardial thickness on mid-wall mesh nodes, followed by the application of a quantiles range decision fusion technique. The first part outlines the extraction and mapping of key imaging biomarkers onto the mid-wall mesh, enabling precise spatial representation of post-infarct remodeling. The second part details the fusion strategy based on quantiles range, designed to enhance the robustness of infarct age estimation by integrating heterogeneous biomarkers and clinical data.

### 2.1 Intramyocardial Fat, Calcification and Myocardial Thickness Quantification on Mid-wall Mesh Nodes

The 3D CT scan is processed into a graph input using an image segmentation pipeline involving short axis reorientation, left ventricular myocardium segmentation and thickness computation, LV mid-wall meshing, and average mesh fitting, following the technique described in [1].

Expanding upon this segmentation process, the 3D CT scans and the corresponding segmentations of the myocardium are used to quantify intramyocardial fat and calcification. Fat and calcification have respectively lower (darker) and higher (brighter) attenuation values in Hounsfield Scale than the healthy myocardium. Unfortunately, many post-MI patients have implantable cardioverter-defibrillators (ICD) or other devices (e.g. coronary stents, artificial valves) which
<figure><img src="image_2.png" /><figcaption>Fig. 1: Data processing pipeline.</figcaption></figure>

results in image artifacts that hinder accurate automated quantification. In this work, weighted distance maps are used to estimate the intensity of ICD artifacts at each location of the image. Then, optimal image-specific intensity thresholds for fat and calcification segmentation are estimated using Gaussian Mixture Models (GMM) in the areas free of ICD artifacts. Mitral annular calcification is a chronic process defined by the occurrence of calcium deposits in the fibrous ring of the mitral valve. Given that the aim of this study is to quantify only the calcified areas that are related to scar maturation (i.e. nearby the scarred area), an automated method was developed to discard mitral annular calcification. A region of interest of 10 mm from the base of the LV wall segmentation is computed, and calcification patches that overlap with this region by more than 80% are excluded from further analysis. The selection of these parameters was made on the basis of empirical evidence derived from the available dataset. Finally, a density-based denoising strategy is proposed to eliminate image noise, highly affecting fat quantification.

The computed intramyocardial fat and calcification values are subsequently mapped to the nearest mesh nodes (see fig. 1). Myocardial thickness is normalised by clipping values over 20 mm and subsequently dividing by 20. Intramyocardial fat and calcification values are normalised using the RobustScaler [16] method to account for variability in the quantified data across mesh nodes.

## 2.2 Quantiles Range Decision Fusion

State-of-the-art techniques for UQ, which aim to assess the reliability of model predictions and improve decision-making, include Gaussian process regression [12], Bayesian neural networks [13], and Monte Carlo methods [14]. While effective, these methods often require significant computational resources and extensive training. Inspired by these UQ methodologies, the proposed quantile ranges
decision approach offers a lightweight alternative. Unlike traditional UQ techniques, quantile-based methods require minimal training and efficiently capture data variability, making them more practical for real-time applications [15].

<figure><img src="image_3.png" /><figcaption>Fig. 2: Quantiles Range Decision Fusion: general setting. For each modality, a predictor regressor and a conditional quantile regressor are trained separately. Late fusion at the decision level is then applied, selecting the best infarct age prediction based on the smallest quantile range.</figcaption></figure>

In the proposed multimodal regression framework, for each modality, two distinct types of regressors are trained separately: a *predictor regressor* and a *conditional quantile regressor*. The predictor regressor outputs a point estimate $\hat{Y}$ of the target variable $Y$ and is trained using the Mean Absolute Error (MAE) loss, which is defined as:

$$ \mathcal{L}_{\text{MAE}} = \frac{1}{N} \sum_{i=1}^{N} |Y_i - \hat{Y}_i|, $$

where $N$ is the total number of training samples, $Y_i$ is the ground truth target value for sample $i$, and $\hat{Y}_i$ is the point prediction provided by the predictor regressor. The use of MAE ensures robust training by minimizing large deviations in the prediction.

The conditional quantile regressor outputs a set of quantiles $Q_q(Y|\mathbf{X}_i)$ for each sample $i$, where $q$ belongs to a predefined set of quantile levels $Q = \{0.05, 0.1, 0.5, 0.9, 0.95\}$. To train the conditional quantile regressor, the quantile loss function is used. This loss penalizes over- and under-predictions asymmetrically, ensuring that each predicted quantile represents the appropriate fraction
of the target distribution. For a specific quantile $q \in Q$, the quantile loss is:

$$ \mathcal{L}_q = \frac{1}{N} \sum_{i=1}^{N} \max(q \cdot (Y_i - Q_q(Y|\mathbf{X}_i)), (1-q) \cdot (Q_q(Y|\mathbf{X}_i) - Y_i)). $$

The overall loss for the conditional quantile regressor is computed as the mean of the individual quantile losses across all quantiles in $Q$:

$$ \mathcal{L}_{\text{Quantile}} = \frac{1}{|Q|} \sum_{q \in Q} \mathcal{L}_q, $$

where $|Q|$ denotes the number of quantile levels in the set $Q$.

By training the predictor regressor and the conditional quantile regressor separately, we ensure that the predictor regressor focuses on providing accurate point estimates, while the conditional quantile regressor independently models the uncertainty of the predictions across different quantiles. This dual-model approach enables precise temporal predictions and reliable uncertainty estimation.

In multimodal regression tasks, the objective is to predict a target variable $Y$ using multiple input modalities. For a dataset with $N$ samples, let

$$ \mathbf{X}_i = [\mathbf{X}_{i1}, \mathbf{X}_{i2}, \dots, \mathbf{X}_{im}] $$

represent the input data for sample $i$, where $m$ denotes the number of modalities. Each modality $j$ is associated with two key components: a prediction regressor $f_j(\mathbf{X}_{ij})$, which provides a point estimate for $Y$, and a conditional quantile regressor $Q_q(Y|\mathbf{X}_{ij})$, which estimates the quantiles of $Y$ for UQ.

The prediction regressor $f_j(\mathbf{X}_{ij})$ outputs a single-point prediction $\hat{Y}_{ij}$ for the target variable $Y$ based on the input modality $\mathbf{X}_{ij}$. Simultaneously, the conditional quantile regressor computes the 5th and 95th quantiles $Q_{0.05}(Y|\mathbf{X}_{ij})$ and $Q_{0.95}(Y|\mathbf{X}_{ij})$, respectively. These quantiles define the range within which 90% of the predicted conditional distribution of $Y$ is expected to lie, providing a measure of uncertainty for each modality's prediction. The quantile range $R(\mathbf{X}_{ij})$ for modality $j$ of sample $i$ is computed as:

$$ R(\mathbf{X}_{ij}) = Q_{0.95}(Y|\mathbf{X}_{ij}) - Q_{0.05}(Y|\mathbf{X}_{ij}). $$

This quantile range serves as an uncertainty metric, where smaller values indicate greater certainty in the modality-specific prediction.

To determine the best prediction for a given sample $i$, the quantile range $R(\mathbf{X}_{ij})$ is evaluated for all modalities $j \in \{1, \dots, m\}$. The modality $j^*$ with the smallest quantile range is identified as:

$$ j^* = \arg \min_{j \in \{1, \dots, m\}} R(\mathbf{X}_{ij}). $$

The corresponding best prediction for the target variable $Y$ is the point estimate produced by the prediction regressor $f_j^*(\mathbf{X}_{ij}^*)$ associated with the selected modality $j^*$:

$$ \hat{Y}_{i\text{best}} = f_j^*(\mathbf{X}_{ij}^*). $$
This framework ensures that the final prediction is derived from the modality with the highest certainty, as indicated by the smallest quantile range, while maintaining a clear distinction between the roles of the prediction regressor $f_j$ and the conditional quantile regressor $Q_q$.

By leveraging modality-specific regressors for both prediction and UQ, this approach balances accuracy with reliability, making it particularly valuable for high-stakes applications where uncertainty-aware decisions are critical.

# 3 Materials

## 3.1 Data Set and Features Extraction

A retrospective dataset of 600 myocardial infarction (MI) patients was collected at Bordeaux University Hospital between 2010 and 2020. The dataset includes one cardiac CT image per patient, acquired at least one month post-infarction, along with corresponding clinical descriptions. Imaging was performed during the arterial phase following the administration of an intravenous bolus of iodinated contrast agent at a minimum flow rate of 5 mL/s. The protocol employed prospective ECG gating, with image acquisition at mid-diastole.

Among the 600 collected cases, infarct age was missing in approximately 32% of cases. For this study, we utilized the 403 cases with available infarct age information to train our models. The missing values on infarct age are likely to be mostly due to silent MI, but cannot directly reflect silent MI prevalence due to confounding factors (e.g. missing data).

<table><caption>Table 1: Clinical Data Modality (N=403)</caption><thead><tr><th>Feature Name</th><th>Statistics: [min, max] mean or proportion</th></tr></thead><tbody><tr><td>Infarct Age (months)</td><td>[1, 528] 133.70±120.35</td></tr><tr><td>LVEF %</td><td>[14, 78] 44.84±13.48</td></tr><tr><td>Age (years)</td><td>[27, 96] 70.92±11.99</td></tr><tr><td>Smoking</td><td>194 (48%)</td></tr><tr><td>Dyslipidaemia</td><td>289 (72%)</td></tr><tr><td>Diabete</td><td>96 (24%)</td></tr><tr><td>Hypertension</td><td>252 (63%)</td></tr><tr><td>Sex</td><td>68 Females / 335 Males</td></tr></tbody></table>

**Description:** LVEF % - Left Ventricular Ejection Fraction, Age - Patient age at the scan date.

## 3.2 Experimental Setup

In our experimentation, the proposed Quantiles Decision model (E) is evaluated against four alternative model configurations: median regression (A), the clinical data model (B), the medical images model (C), and the early fusion model (D).
Median regression (A), commonly used in imputation settings, serves as a baseline. The clinical data model (B) utilizes a Random Forest regressor for comparative analysis. The medical images model (C) combines a graph convolutional network (GCN) [17] and a fully connected network (FCN), starting with a graph of 5000 nodes downsampled to 500 using index pooling [1]. Two GCN layers with filter sizes of 64 are stacked around the index pooling layer, and a final GCN layer with a filter size of 1 concludes the block. The FCN block has three layers with [64, 32, 1] units, using ELU for intermediate layers and ReLU for the output layer.

The early fusion model (D) combines features from medical images and clinical data through concatenation, with the resulting tensor input into a Random Forest regressor [16]. The GCN block is used to extract features from medical images. The Random Forest Quantile Regressor [16] is employed by models (B) and (D) to estimate the 5th and 95th prediction quantiles for each respective model. Predictions from Models B and C are integrated to build the Quantiles Decision model (E), which combines modality strengths. All models are trained using 10-fold cross-validation on datasets split into training (70%), validation (10%), and test (20%) sets.

## 4 Results

As shown in Table 2, model (E) achieves the lowest MAE among all models. In contrast, model (C) demonstrates the best performance in terms of calibration error (CE). To evaluate the impact of the chosen quantile range thresholds, a decision model based on the 10th and 90th quantiles (83.39±8.12 for MAE and 68.76±7.79 for CE) demonstrates comparable performance to model (E) on both metrics. Lastly, model (C) outperforms model (B) in both metrics.

<table><caption>Table 2: Model performance results. Best performance metrics in **bold**.</caption><thead><tr><td>Model</td><td>MAE</td><td>Calibration Error</td></tr></thead><tbody><tr><td>(A) Median Baseline Regressor</td><td>99.79: ±7.91</td><td>94.75: ±7.56</td></tr><tr><td>(B) Clinical Data Modality</td><td>87.40: ±6.43</td><td>72.52: ±7.99</td></tr><tr><td>(C) Medical Images Modality</td><td>86.30:±6.69</td><td><b>66.50: ±12.41</b></td></tr><tr><td>(D) Early Fusion</td><td>85.23: ±3.89</td><td>69.51: ±8.53</td></tr><tr><td>(E) Quantiles Decision: [5th ; 95th]</td><td><b>83.39: ±7.48</b></td><td>69.34: ±6.87</td></tr></tbody></table>

These results highlight the potential of modality fusion to leverage the complementary strengths of different modalities for the specific regression task. In fact a detailed analysis of Fig. 3, focusing on the critical post-MI period (up to 10 years), reveals that model (E) consistently outperforms model (D) across all quantile bins (Fig. 3a) and effectively integrates the most informative features from both modalities (models (B) and (C)) (Fig. 3b). Notably, model (E)
<figure><img src="image_4.png" /><figcaption>Fig. 3: Calibration plot on quantile bins.</figcaption></figure>

achieves its best calibration error between 1 and 10 years post-MI. In addition to minimizing prediction uncertainty, model (E) offers the advantage of handling cases with missing modalities, unlike the model (D), which requires all modalities to be present.

# 5 Discussion

The reduced performance of all compared models (see Fig. 3) during the first year post-MI may be attributed to the incomplete formation or low visibility of imaging biomarkers at this early stage. This finding highlights the need to explore alternative biomarkers that may be more relevant during the early post-MI phase to further enhance the performance of model (E). Beyond 10 years, the poor performance of model (E) may be in part due to an imbalance in the dataset across different infarct periods. Sample weighting can be applied to models (D), (B), and (C) to address the imbalance issue, reducing their confidence in this period and enabling a more appropriate comparison.

Moreover, myocardial wall thickness can be influenced by multiple factors, including scar transmurality and clinical conditions such as hypertension and diabetes. In this study, model performance improved when thickness was combined with other tissue characteristics, such as intramyocardial fat and calcification. Incorporating infarct transmurality as an additional feature could provide further insights and warrants future investigation.

While Cardiac Magnetic Resonance Imaging (CMRI) remains the gold standard for evaluating myocardial infarction due to its excellent contrast between scar and remote myocardium, the use of CT is justified by its greater accessibility, faster acquisition, and lower operational costs. Beyond its availability, cardiac CT offers superior quantification of intramyocardial fat and calcification, attributed to its high spatial resolution, standardized Hounsfield Unit (HU)-based density measurements, and enhanced sensitivity to calcified tissue. These advantages make cardiac CT a valuable modality for assessing infarct characteristics
in large-scale studies and routine clinical practice. Furthermore, advancements
in photon-counting CT technology are expected to enable more robust late en-
hancement imaging and improved coronary plaque analysis, potentially position-
ing CT as a comprehensive tool for risk stratification in both myocardial and
coronary artery diseases.

6 Conclusion

The proposed approach leverages advanced quantification of myocardial thickness, fat and calcification to estimate infarct age with minimal prediction uncertainty through the proposed Quantiles Decision model. The proposed model demonstrates optimal calibration error between 1 and 10 years post-MI, which is the most crucial period for chronic cardiac remodelling. Additionally, the model offers the advantage of remaining functional in cases of missing modalities, further enhancing its robustness and applicability in diverse clinical scenarios. Such tool could help clinicians gain deeper insights into the complex interplay of structural and systemic factors driving myocardial remodelling, thereby improving post-MI patient management.

7 Acknowledgments.

This work has been supported by the French government through France 2030 (MediTwin) and the European Union (Next Generation EU), the National Research Agency (ANR) Investments in the Future with 3IA Côte d'Azur and IA Cluster (ANR-19-3IA-0002 and ANR-23-IACL-0001), LIRYC (ANR-10-IAHU-04) and ChroniCardio - ANR-22-PESN-0015. The authors are grateful to the OPAL infrastructure from Université Côte d'Azur for providing resources and support.

8 Disclosure of Interests

The authors have no competing interests to declare that are relevant to the
content of this article.

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