ArticleJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography2026
Diagnostic Performance Evaluation of Clinical and Artificial Intelligence Risk Models in Patients Referred for Cardiac Amyloidosis Testing.
Article in Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundTo improve screening for cardiac amyloidosis (CA), several models using artificial intelligence (AI) and conventional statistics have been developed. However, few data are available to compare the relative utility of these tools. In this study, models were compared to determine their potential roles in optimizing diagnostic algorithms.
methodsIn this retrospective cohort study at a tertiary medical center, patients referred for cardiac scintigraphy for the detection of transthyretin amyloid CA (ATTR-CA) who underwent electrocardiography (ECG) and transthoracic echocardiography within 6 months and had clinical characteristics for risk score calculation were included. The performance of previously developed and validated clinical and AI risk models for ATTR-CA, including the transthyretin amyloid cardiomyopathy (ATTR-CM) clinical score and AI models applied to ECG (AI-ECG) and echocardiography (AI-Echo), was compared in a population referred for cardiac scintigraphy. Previously defined thresholds were used for each model. As the ATTR-CM score was validated following the exclusion of monoclonal immunoglobulin light chain (AL) amyloidosis, 28 patients with AL amyloidosis were excluded. AL amyloidosis and ATTR-CA were defined per guideline criteria.
resultsAmong 598 patients (median age, 76 years; interquartile range, 67-82 years; 72.6% men), 181 (30%) had ATTR-CA. AI-Echo identified ATTR-CA with 86% sensitivity and 85% specificity, compared with 80% and 64% for AI-ECG and 86% and 69% for the ATTR-CM score. The area under the receiver operating characteristic curve was 0.93 (95% CI, 0.91-0.95) for AI-Echo, 0.79 (95% CI, 0.76-0.83) for AI-ECG, and 0.87 (95% CI, 0.84-0.90) for ATTR-CM score (P < .001). In this cohort, the use of AI-Echo could have avoided more unnecessary scintigraphy than AI-ECG or ATTR-CM score (45 vs 24 vs 37 per 100, respectively) at a threshold probability of 0.25 (1 case of ATTR-CA per 4 referrals for scintigraphy).
conclusionsWithin a population at high risk for CA, the AI-Echo model demonstrated superior diagnostic discrimination and clinical utility for the identification of ATTR-CA compared with the AI-ECG model and the ATTR-CM clinical score.
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.