ReviewInternational journal of molecular sciences2023
Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.
Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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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.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- Multimodal artificial intelligence to predict cardiomyopathy development in hereditary transthyretin amyloidosis.Open heart · 2026Article
- Cardiovascular Comorbidities and Advanced Chronic Kidney Disease in Hospitalized Patients with Multiple Myeloma: A Single-Center Retrospective Cohort Study.Diseases (Basel, Switzerland) · 2026Article
- Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.Biomedical engineering online · 2026Article
- Review
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.International journal of molecular sciences · 2025Review
- Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2025Review
- Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review.Journal of the American Heart Association · 2025Review
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- DNA Methylation in Ph-Negative Myeloproliferative Neoplasms: Prognostic Role and Therapeutic Implications.Current issues in molecular biology · 2025Review
- A progressive attention-based cross-modal fusion network for cardiovascular disease detection using synchronized electrocardiogram and phonocardiogram signals.PeerJ. Computer science · 2025Article
- Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.European journal of nuclear medicine and molecular imaging · 2025Article
- Hematological Malignancies in Older Patients: Focus on the Potential Role of a Geriatric Assessment Management.Diagnostics (Basel, Switzerland) · 2024Review
- How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.Journal of cardiovascular development and disease · 2024Review
- Cardiac Amyloidosis in Venezuela: A Pending Issue.Current cardiology reviews · 2024Article
- Role of the mechanisms for antibody repertoire diversification in monoclonal light chain deposition disorders: when a friend becomes foe.Frontiers in immunology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from "raw data" without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.
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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.