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Updated: Sep 21, 2026
Genetic Analysis of Hereditary Transthyretin Ala97Ser Related Amyloidosis
Published on: June 9, 2018
Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine
Eyad Jamileh1, Ahmed T Elmewafy2, Muhammad Abdullah Qadeer2
1Furness General Hospital, University Hospitals of Morecambe Bay NHS Foundation Trust, Cumbria, England, UK.
Background:
Transthyretin amyloid cardiomyopathy (ATTR-CM) remains under-recognised, and earlier identification is increasingly important with disease-modifying therapy. Artificial intelligence (AI) applied to routine cardiovascular data may support screening, triage and diagnostic interpretation.
Methods:
MEDLINE, Embase, CINAHL, Web of Science and CENTRAL were searched from inception to August 2026 for studies evaluating AI/machine learning for ATTR-CM detection, screening or classification, including broader cardiac-amyloidosis models with separately extractable ATTR-specific performance and models detecting imaging phenotypes relevant to ATTR-CM screening. Risk of bias was assessed using an adapted QUADAS-2 framework, with AI-specific considerations informed by QUADAS-AI development work; selected TRIPOD + AI and CLAIM domains were assessed descriptively. Owing to substantial clinical and methodological heterogeneity, meta-analysis was not performed.
Results:
Twenty-eight primary reports were included, representing > 120,000 report-level participant entries, although overlapping cohorts precluded estimation of a unique-patient total. Performance varied by modality and task. AUCs ranged approximately 0.55-0.97 for ECG-containing models, 0.72-1.00 for echocardiography/POCUS and 0.70-0.85 for CT-based single-modality approaches; CMR differentiation of ATTR from AL achieved an AUC of 0.92. Nuclear-imaging models often showed high discrimination, including external-testing AUCs of 0.925-1.000, although several targeted tracer-uptake phenotypes rather than definitive ATTR-CM. Recurrent concerns included enriched populations, limited event counts, internal-only testing, threshold optimisation and incomplete calibration reporting.
Conclusions:
AI shows promise for ATTR-CM screening and automated interpretation, but reported discrimination should not be equated with real-world clinical performance. Prospective multicentre validation, calibration at realistic prevalence, transparent reporting and workflow-level evaluation are required before routine implementation.
