ArticleBMC medical informatics and decision making2026
Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveCurrent multimodal models often experience performance degradation when faced with incomplete data and lack effective fusion strategies for diverse data types, such as imaging, clinical, and electrophysiological data. This study aims to develop a novel model to improve the accuracy of early diagnosis of coronary artery disease. MATERIALS AND
methodsClinical data, laboratory test results, coronary CTA images, and ECG data from consecutive patients who underwent coronary CTA examinations at the center between February 2022 and August 2023 were collected. Features were extracted using convolutional neural networks, and a multimodal prediction model was developed using a weighted fusion strategy. An internal validation was performed with a data split ratio of 7:1.5:1.5, and an independent external validation was conducted using an external dataset. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), and model interpretability was assessed using the SHAP algorithm. Diagnostic consistency was evaluated using the Kappa coefficient.
resultCompared to traditional diagnostic methods, the model has increased the diagnosis rate of coronary heart disease by 10.43% points in absolute terms and by 44.0% in relative terms. This improvement has greatly enhanced the ability to identify coronary heart disease at an early stage. For diagnosing cardiovascular diseases, the performance of the multimodal model is significantly superior to that of single imaging or clinical indicators. The area under the curve (AUC) was 0.89 in internal validation, and the model also demonstrated excellent performance in the external validation dataset, with an AUC of 0.83.
conclusionThis model, by integrating three types of data for the first time and tolerating up to 50% missing data, significantly improves the detection rate of coronary artery disease and enhances diagnostic sensitivity and accuracy, thereby providing robust support for early clinical intervention and precise treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.