Cardiac amyloidosis (CA) is an infiltrative myocardial disease that, despite increasing recognition, remains substantially underdiagnosed. Its clinical presentation and imaging phenotype overlap with more common cardiomyopathies such as hypertensive left ventricular hypertrophy or aortic stenosis, delaying definitive diagnosis. Differentiation between transthyretin (ATTR) and light chain (AL) amyloidosis requires integration of multiple modalities: electrocardiogram, echocardiography, cardiac magnetic resonance, bone scintigraphy with SPECT/CT, and often biopsy. In this context, artificial intelligence (AI) and machine learning have emerged as tools capable of extracting subtle patterns from heterogeneous data, optimizing detection, classification, quantification, and prognosis of CA.
Recent literature shows unequal maturity according to clinical task. Binary detection models —presence or absence of CA— on bone scintigraphy and SPECT/CT are approaching clinical translation, supported by large externally validated cohorts and robust discrimination metrics. AI-assisted quantification of myocardial tracer burden, moreover, correlates with clinical outcomes, allowing monitoring of progression and treatment response. However, more complex tasks —subtype classification (ATTR vs. AL), prognostic stratification, and treatment response monitoring— remain in early stages, limited by small cohorts, enriched retrospective designs, heterogeneous labels, and uncertain calibration in realistic prevalence settings. High discriminative power is not enough if the model is not calibrated or generalizable.
This maturity gradient reflects a structural challenge: multimodal data integration is not trivial. Current AI systems are often trained on a single data source (ECG, echocardiogram or scintigraphy), but CA diagnosis and management require combining electrocardiographic signals, echocardiographic parameters, late gadolinium enhancement patterns on MRI, SPECT/CT absorbance quantification, and serum biomarkers. True innovation lies in platforms that fuse these modalities in an orchestrated manner, offering clinicians a comprehensive dashboard. This is where companies like Q2BSTUDIO can bring their expertise in developing custom software applications that integrate AI, cloud computing and data security.
From a technical and business perspective, deploying AI models in healthcare requires a scalable and secure infrastructure. The cloud, whether AWS or Azure, provides elasticity to process large volumes of images and signals, as well as managed machine learning services (Amazon SageMaker, Azure Machine Learning) that streamline training and inference. Q2BSTUDIO, as a technology partner, offers cloud services on AWS and Azure that enable building robust data pipelines, from ingestion of DICOM images to production model orchestration. Cybersecurity is another critical pillar: patient data is protected by regulations such as HIPAA and GDPR, and any solution must incorporate encryption, access control and auditing. The company integrates cybersecurity and pentesting into its developments, ensuring that AI systems meet the highest protection standards.
Beyond detection and quantification, the future of AI in cardiac amyloidosis points to risk prediction and treatment personalization. Current prognostic models are mostly based on clinical scores (such as the Mayo score or the National Amyloidosis Centre stage), but incorporating imaging variables and biomarkers through deep learning techniques could significantly refine stratification. This requires training algorithms on multicenter prospective cohorts and validating their calibration in real populations. Moreover, monitoring treatment response —for example, after tafamidis or patisiran in ATTR, or bortezomib and dexamethasone in AL— needs objective quantitative indicators. Automated quantification of amyloid burden on SPECT/CT, combined with serum biomarker trends, can provide a dynamic panel accessible from a Business Intelligence dashboard.
BI and Power BI tools are essential to visualize these longitudinal data. A medical team can consult a dashboard showing myocardial uptake trends, troponin and natriuretic peptide values, and the patient's risk classification, all updated in real time. Q2BSTUDIO develops Business Intelligence and Power BI solutions tailored to the healthcare sector, enabling integration of data from multiple sources into interactive reports. Likewise, AI agents —autonomous systems that can execute tasks such as image review, preliminary report generation or differential diagnosis suggestion— are beginning to be applied in radiology and cardiology. These agents, based on large language models (LLM) or convolutional neural networks, can act as virtual assistants to cardiologists, reducing workload and speeding up decision flow.
A concrete example: an AI agent trained on thousands of bone scintigraphies can automatically detect abnormal myocardial uptake regions, quantify the burden and classify the pattern (diffuse vs. focal). Then, another agent specialized in clinical data can combine that information with age, sex, ECG and echocardiography to suggest the probability of ATTR vs. AL. Finally, a prognostic module based on recurrent neural networks predicts 1- and 5-year survival. All orchestrated on a cloud platform with security and data governance layers. Q2BSTUDIO offers artificial intelligence services and custom agent development, adapting these solutions to the specific needs of each hospital or research center.
The adoption of AI in clinical practice for cardiac amyloidosis depends not only on model accuracy, but also on physician trust and integration into existing workflows. Solutions must be explainable, auditable and easy to use. Therefore, user interface design and clinical experience are key aspects that Q2BSTUDIO addresses through agile methodologies and direct collaboration with specialists. The company also offers training programs and ongoing support to ensure a smooth transition to data-driven medicine.
In conclusion, the path from binary detection to multimodal integration in cardiac amyloidosis is paved with advances and challenges. AI has shown its potential in screening and quantification tasks, but more complex applications require careful orchestration of data, models and people. Companies like Q2BSTUDIO, with their ecosystem of process automation and cloud services, AI, cybersecurity and BI, are uniquely positioned to accelerate this transformation, offering custom platforms that turn the promise of artificial intelligence into a reliable and scalable clinical reality. Cardiac amyloidosis, with its multimodal complexity, thus becomes a perfect testbed to demonstrate how technology can save lives when integrated intelligently and securely.
