ArticleAnnals of hematology2025
Development of an artificial intelligence-based early diagnostic system for light-chain amyloidosis.
Article in Annals of hematology, 2025. 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
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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.
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.
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Who cites it
1 citing paper in PubMed.
- The Current Role of Physiotherapy in Systemic Light-Chain (AL) Amyloidosis and Multiple Myeloma.Life (Basel, Switzerland) · 2026Review
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Authors and funding
19 authors.
Funding
Abstract
Light-chain (AL) amyloidosis is a rare disease and its early diagnosis remains challenging. This study aimed to develop an artificial intelligence (AI)-based diagnostic assistance system to improve the early diagnosis of AL amyloidosis and facilitate earlier and more precise disease management. Through cooperation with 18 hospitals in the Chinese Registration Network for Light-chain Amyloidosis (CRNLA), 1,355 patients with AL amyloidosis were registered and followed up from January 2010 to January 2022. Ten variables that are easily monitored in clinics, including age, cardiac troponin I (cTnl), N-terminal prohormone B-type natriuretic peptide (NT-ProBNP), creatinine (Crea), albumin (ALB), total bilirubin (Tbil), alkaline phosphatase (ALP), interventricular septum (IVS), left ventricular posterior wall (LVPW), and ejection fraction (EF), were selected. An early assistant diagnostic model of AL amyloidosis was established using gradient boosting decision tree (GBDT), support vector machine (SVM), random forest (RF), and ensemble learning models. A validation subset of challenging cases from the external validation set was used to compare the performance of the AI-based early diagnostic assistance system to that of physicians. All of the typical machine learning algorithms, namely the GBDT, SVM, RF, and ensemble learning models, performed well. The ensemble learning model was considered to have the best performance in the external validation set based on its highest F1 value (0.94) and an area under the receiver operating characteristic (ROC) curve of 0.9784 (95% confidence interval [CI]:0.963-0.987). Performance comparisons between the AI-based early diagnostic assistance model and physicians suggested that the diagnostic accuracy of the AI model (0.95,95%CI:0.92-0.98;P < 0.05) substantially exceeded that of the hematologists (0.73,95%CI:0.7-0.82) and other physicians (0.65,95%CI: 0.61-0.71). The model established by AI, which uses routine laboratory and echocardiography results, can predict the possibility of AL amyloidosis in various scenarios, which may support the early diagnosis of this rare disease.
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