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Open research questions in Cardiac Imaging and Diagnostics

52 unresolved questions extracted from the limitations and future-work sections of 314 Cardiac Imaging and Diagnostics papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Future studies should focus on the invasive validation of the BSE-2024 recommendations and the proposed comple- mentary algorithm in larger independent cohorts. Further research is also needed to define the role of emerging echo- cardiographic parameters in LVFP estimation.

    Invasive validation of BSE-2024 vs. BSE-2013 using LVEDP and LV pre-A pressure in patients undergoing cardiac catheterization: a multicenter study with a complementary algorithm · 2026 · DOI

  • Therefore, our cohort represents a highly selected, higher-risk subset of the ‘normal MPI’ population with persistent clini- cal suspicion, and our findings may not be generalizable to all patients with normal perfusion.

    Added Value of CZT SPECT Myocardial Blood Flow Quantification for Detecting Obstructive Coronary Artery Disease in Patients with Normal Perfusion Imaging · 2026 · DOI

  • However, the diagnostic integration of ECV and FAI-RCA for identifying VSA in patients with angina with non-obstructive coronary arteries (ANOCA) remains to be elucidated.

    Comprehensive assessment of vasospastic angina using coronary computed tomography angiography: synergistic value of the presence of myocardial bridge, perivascular inflammation, and myocardial extracellular volume fraction · 2026 · DOI

  • Future research will focus on improving risk prediction, distinguishing active calcification, and developing targeted therapies [49]. Current calcium scoring provides information about the amount of calcified plaque in the coronary arteries, but it cannot determine whether calcification is biologically stable or unstable [50]. Artificial intelligence is expected to improve image analysis by enabling more accurate measurements of calcium volume, mineral density, distribution, plaque morphology, and other imaging characteristics [51]. AI algorithms may also predict plaque progression and identify high-risk features from routine imaging [52]. Molecular research may also identify new therapeutic targets, particularly pathways involving vascular smooth transformation and other muscle cell osteogenic mechanisms that regulate the progression of coronary artery calcification [53]. Inhibitors of BMP signaling and modulators of phosphate metabolism are among the targets being investigated [54]. Anti-inflammatory and antioxidant therapies may reduce the inflammatory triggers of vascular calcification [55]. outcomes [5]. Additionally, Future clinical trials will need to demonstrate that therapies aimed at reducing calcium progression improve cardiovascular the combination of advanced imaging, biomarkers, and genetic profiling may enable personalized prevention and treatment strategies [6]. The integration of CAC scoring with emerging technologies will continue to improve the management of coronary artery calcification and reduce the burden of cardiovascular disease [7].

    Calcification of Coronary Arteries: From Visualization to Risk Prediction and Prevention. · 2026 · DOI

  • Moreover, the authors discuss the barriers that have limited their clinical application in routine workflows namely the limited data used for training, the differences in the CT imaging protocols and the anatomical complexity of aortic diseases and that have to be addressed so as to unlock their full potential in the treatment of aortic diseases.

    Editorial: Glimpse to the past – the evolution of the role of Imaging in cardio therapeutics · 2026 · DOI

  • 6 Future works Future research will focus on transitioning the current compu- tational framework into an automated clinical diagnostic of ather- osclerosis by the prediction of the FFR. Therefore, other complex models with more elements should be explored.

    Developing a non-invasive diagnostic framework for the fractional flow reserve quantification in left coronary arteries: validation with patient cases · 2026 · DOI

  • integrating plaque composition with stenosis assessment for better functional and prognostic interpretation. Emerging evidence supports CT-based plaque quantification enhanced by artificial intelligence for individualized risk stratification (19). Our results are consistent with this approach and highlight the diagnostic synergy between non-invasive and invasive imaging. Analysis of CAC scores showed that mixed plaques predominated at higher CAC categories, while noncalcified and calcific types persisted even at low or intermediate calcium levels. This illustrates CAC’s limitation as a sole marker of atherosclerotic activity.

    Coronary plaque characteristics on CT Angiography: Associations with risk factors and stenosis burden · 2026 · DOI

  • However, the clini- cal relevance of CLEAR motion for patient management or care has not been determined in this study, and further stud- ies using a large prospective cohort with ontological dis- eases are warranted. 001 improvements were not performed, and further investiga- tions with phantoms are warranted.

    Deep learning-based motion correction: cardiac motion artifact and image quality improvements on chest CT · 2026 · DOI

  • Abstract Purpose Quantitative Rubidium-82 ( 82 Rb) PET allows assessment of myocardial blood flow (MBF) and flow reserve (MFR), yet clinically applicable normal reference ranges remain incompletely defined across age and sex.

    Establishing normal myocardial blood flow and myocardial flow reserve values: a rubidium-82 positron emission tomography study · 2026 · DOI

  • Cardiovascular magnetic resonance (CMR) T1-mapping quantifies diffuse fibrosis non-invasively, but its prognostic value in CKD remains uncertain Aims To investigate associations between native myocardial T1, mortality and incident CV outcomes, in CKD using a virtual twin-matching framework within the UK Biobank imaging cohort.

    Myocardial fibrosis and tissue alterations predict cardiovascular outcomes in chronic kidney disease—a prospective virtual twin study design using large-scale population database · 2026 · DOI

  • However, widely used techniques such as Modified Look-Locker Inversion Recovery (MOLLI) remain limited by their proprietary nature andlimited accessibility, particularly in resource-constrained settings.

    A Siemens Single-Shot T1 Mapping Sequence as an Alternative to MOLLI · 2026 · DOI

  • This study has limitations to address. First, the sample size is limited, which prevents reliable assessments of variations in plaque metrics across a wider range of plaque volumes. This limitation is related to the scarcity of existing serial contrast studies within a short time range, as demonstrated by the need for pooled data from two institutions to achieve this sample size of 30 participants. This study demonstrates high reproducibility of serial plaque quantifications using side-by-side readings, but there is no comparator arm, which prevents any assessment of incremental benefit from this approach compared to other methods. We do, however, demonstrate the superior reproducibility of scan-specific thresholds compared to fixed thresholds. It should also be noted that the side-by-side plaque quantification approach is more time-consuming than solely quantifying plaque in the latest scan and comparing the findings to the previous scan report. However, the side-by-side practice is more in line with clinical practice, with AI-enabled plaque analysis allowing an acceptable workflow. This study investigated the reproducibility of plaque volumes across serial acquisitions on the same scanner using consistent scanning protocols. In real-world practice, this consistency may be difficult to achieve as older scanners are upgraded over time and patients change their geographical location, leading to serial scans at different sites with varying hardware and protocols. Hence, future scientific efforts should aim to improve the reproducibility of plaque volumes also across inconsistent scanning modes. Lenell et al.

    High scan-rescan repeatability of AI-enabled coronary plaque quantification from coronary CT angiography · 2026 · DOI

  • The red dotted line represents the mean difference; blue dotted lines indicate the 95% limits of agreement application remains limited by the time burden imposed by manual scoring [33].

    Quantification of Abdominal Aortic Calcification on CT: Clinical Validation for Assessment Of Cardiovascular Risk in Oncology · 2026 · DOI

  • diagnostic accuracy and clinical decision-making. Echocardiography, despite being widely used, is highly operator-dependent and shows interobserver variability, particularly in LVEF assessment8. Post- imaging and www.discoveriesjournals.org/discoveries 2 AI in Cardiovascular Imaging: State and Outlook scan processing and manual analysis of imaging datasets, particularly in cardiac MRI, remain time- consuming processes that can delay the diagnosis6. In a high-volume clinical setting such as reliance on manual emergency departments, interpretation of the imaging often contributes to delays in diagnosis and workflow bottlenecks9. These ongoing difficulties with conventional imaging draw attention to the need for technological innovations like artificial intelligence to improve diagnostic speed, standardization. Given the growing importance of Artificial Intelligence (AI) across cardiovascular imaging Modalities, a comprehensive review is needed not only of its current clinical applications but also of performance FDA-approved comparisons, technologies, and future directions. This review aims to synthesize recent developments and address the challenges real-world implementation. associated with 2. Overview of artificial intelligence in imaging on Artificial Intelligence refers to algorithmic systems capable of autonomous pattern recognition and decision-making complex, based multidimensional datasets. This usually entails using data such as medical records or information taken from photos to determine the best course of treatment, detect a new condition, or anticipate a likely diagnosis10. Recent advances in deep neural architectures, expanding biomedical datasets, and accessible computational frameworks have catalyzed AI’s integration into clinical imaging11. Machine learning (ML), from classical regressions and support vector machines to convolutional neural networks (CNNs), underpins most modern AI tools in imaging. A branch of ML called deep learning (DL) makes use of deep neural networks12,13. 2.1 Biobanks and Bioresources Big data includes genetic information, imaging data, medical health records, patient outcomes, and outcome data. Structured big data gathering includes “biobanks” and “bio-resources”14. An additional type is an atlas, which combines data to offer reference information on structural variation, like that found in the heart15. AI models perform better with larger data sets. To be appropriate for ML, many datasets must be restructured into features, which are discrete informative units like motion vectors, clinical reports, or imaging metrics like pixel brightness16. The effectiveness of computational methods in producing an AI will depend on the caliber, precision, and depth improper or of misclassified data is provided, it indicates that the dataset does not sufficiently reflect the real world for ML to produce a model10. the data. When features in 2.2 Computational approaches ML algorithms can be trained in two broad ways: learning. learning and unsupervised supervised Another method that utilises many models to increase forecast accuracy and robustness is ensemble learning.

    Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions · 2025 · DOI

  • ● Generalizability to the population as a whole: How can federated and transfer learning be utilized to achieve robust performance for individuals in a range of ethnic, age, and comorbid disease groups without compromising data sharing121-123? ● Trade-off between explainability and performance: How much transparency of the model (XAI techniques, CAM maps) is needed regulatory agency for clinician approval, and what implications does this have for clinical accuracy diagnostic implementation? trust and and www.discoveriesjournals.org/discoveries 19 AI in Cardiovascular Imaging: State and Outlook ● Integrating into the clinical workflow: What are the optimal AI-human interaction paradigms (e.g., real-time triage, HITL oversight, automated reporting) to maximize efficiency gains without trading off safety in high-acuity settings? ● Regulation and liability: What processes should be used to “oversee” dynamic “learning” algorithms in operation, to handle degradation, to ensure patient safety, and to apportion liability among and practitioners? the developers, organisations, economic ● Cost-effectiveness and access: What will be the of AI sustained implementation on health-system costs, and how can AI solutions be scaled resourceconstrained settings to mitigate inequalities?

    Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions · 2025 · DOI

  • Emerging trends in cardiovascular imaging Healthcare is becoming more personalized and precise due to the development of AI technologies and their integration into various fields of medicine, such as cardiovascular imaging. This will enhance patient outcomes by providing early diagnosis and prompt management of CVD120. Although AI models have numerous advantages, some limitations prevent them from being widely used in medicine. One of them is that most of the AI models are trained on data that is small, narrow, and not diverse. This could result in lower generalizability and over-fitting. To overcome this issue, Federated Learning (FL) or Transfer Learning methods can be used, which train the models with data from different sites without centralising it. This leads to a decreased risk of data leaks or unauthorized access as the data is not sent over to other networks121,122. Due to training with multiple datasets, the model becomes familiar with rare cases, which increases its sensitivity and results in decisions that are less biased. The performance of such models is similar to those trained on centrally hosted datasets and much better when compared to models trained with data from a single institution, according to the latest studies123. Another disadvantage of AI models is their “unexplainable” feature, also known as “blackboxes”. This means that how the AI reaches a specific conclusion or diagnosis is not known. The lack of transparency its working124,125. Explainable AI methods are being developed to resolve this issue126. Class Activation Mapping (CAM) is a technique that comes under Explainable AI. CAM can identify the input patterns within the deep neural network, which leads to the activation of certain outputs and shows the findings on the image. This helps the physicians who use AI to better understand the reasoning of the model and make informed decisions127. AI can also serve as a means of bringing together different imaging modalities, which can be extremely helpful, especially in the diagnosis of heterogeneous regarding questions raises diseases, for example, heart failure and atrial fibrillation128. The multimodality AI approach combines information, in the form of image, text, audio, video, and language, obtained from different imaging methods. Each modality offers important additional data, resulting in accurate outputs. A study was done to distinguish between the causes of left ventricular hypertrophy by merging data from ECG and echocardiography. The multimodal AI was found to have higher sensitivity and specificity compared to physicians129. Therefore, as AI technologies are evolving rapidly, it is changing how cardiovascular imaging is used, by improving image quality, reducing the time and workforce required for image analysis, and providing prognostic information10,130. 8.2 Wearable AI Devices timely detection ECG is an essential and most frequently used noninvasive test in cardiology. However, to record and interpret an ECG, the individual is required to visit a healthcare professional. This results in paroxysmal arrhythmias being undetected. The integration of ECG monitoring and AI-based ECG interpretation into wearable devices like smartwatches has led to widespread access to ECG131. Furthermore, there has been an increase in the detection of arrhythmias131,132. In case of AF, early detection is crucial to avoid progression and development of complications such as stroke. As most cases have either nonspecific symptoms or are asymptomatic, patients are unaware of their condition and hence do not seek medical care. Using wearable devices with AI technology, early and in improved treatment outcomes133.

    Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions · 2025 · DOI

  • Noninvasive FFR computed from CT (FFR(CT)) is a novel method for determining the physiologic significance of coronary artery disease (CAD), but its ability to identify ischemia has not been adequately examined to date.

    Diagnostic Accuracy of Fractional Flow Reserve From Anatomic CT Angiography · 2012 · DOI

  • In addition, the prescribed uncertainty distributions were based on available literature and should be further validated across broader patient populations and disease phenotypes.

    Effects of Pulsatile Flow on Fractional Flow Reserve Assessed Using a Reduced-Order Model · 2026 · DOI

  • Future studies incorporating follow-up data are warranted to determine the real-world impact of these technologies on patient management and healthcare costs.

    Comparative diagnostic performance and stability of deep learning- and CFD-based CT-FFR across vessels, cardiac phases, and centers · 2026 · DOI

  • As the population was almost exclusively Euro-Caucasian with body mass index in the normal to moderately overweight range, validation in more diverse cohorts is warranted.

    Establishing normal myocardial blood flow and myocardial flow reserve values: a rubidium-82 positron emission tomography study · 2026 · DOI

  • Furthermore, the correlation between these biomarkers and coronary artery calcification merits particular investigation, given contradictory reports in current literature.

    ASSOCIATION OF NOVEL SYSTEMIC INFLAMMATION INDICES AND CORONARY ARTERY DISEASE · 2025 · DOI

  • AIMS: The role of sex in this discrepancy remains uncertain; thus, we aimed to investigate its impact on the discordance between FFR and iFR/RFR.

    Influence of sex on the functional assessment of myocardial ischemia · 2023 · DOI

  • In particular, the potential effect of volumetric calcium content and the topographical distribution in the lesion segment on physiological outcome has not yet been investigated.

    Influence of Coronary Calcification Patterns on Hemodynamic Outcome of Coronary Stenoses and Remodelling · 2017 · DOI

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52 open questions have been extracted from the limitations and future-work passages of 314 Cardiac Imaging and Diagnostics papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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