Development of an artificial intelligence-based early diagnostic system for light-chain amyloidosis Article Swipe
Wei Yan
,
Jian Chen
,
Tao He
,
Haoze Xu
,
Jianmin Chen
,
Liu Xiaoyu
,
Liye Zhong
,
Yan Li
,
Chunyan Sun
,
Xiaobo Wang
,
Li Gao
,
Fengyan Jin
,
Bingzong Li
,
Chunrui Li
,
Yingyou Wen
,
Zhuo Chen
,
Boheng Liu
,
Jian Li
,
Huihan Wang
,
Wei Yan
,
Jian Chen
,
Tao He
,
Haoze Xu
,
Jianmin Chen
,
Liu Xiaoyu
,
Liye Zhong
,
Yan Li
,
Chunyan Sun
,
Xiaobo Wang
,
Li Gao
,
Fengyan Jin
,
Bingzong Li
,
Chunrui Li
,
Yingyou Wen
,
Zhuo Chen
,
Boheng Liu
,
Jian Li
,
Huihan Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s00277-025-06674-7
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s00277-025-06674-7
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.
Related Topics
Concepts
Medicine
Amyloidosis
Receiver operating characteristic
Artificial intelligence
Internal medicine
Ejection fraction
Random forest
Creatinine
Confidence interval
Brain natriuretic peptide
Diagnostic accuracy
Fabry disease
Machine learning
AL amyloidosis
Decision tree
Hepatology
Interventricular septum
Natriuretic peptide
Pathology
Troponin T
Cardiology
Troponin
Ensemble learning
Elevated alkaline phosphatase
Area under the curve
Overcrowding
Kidney disease
Autoencoder
Fractal analysis
Predictive value of tests
Amyloid (mycology)
Nephrology
Support vector machine
Disease
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00277-025-06674-7
- https://link.springer.com/content/pdf/10.1007/s00277-025-06674-7.pdf
- OA Status
- gold
- References
- 23
- OpenAlex ID
- https://openalex.org/W7105515043
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7105515043Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00277-025-06674-7Digital Object Identifier
- Title
-
Development of an artificial intelligence-based early diagnostic system for light-chain amyloidosisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-13Full publication date if available
- Authors
-
Wei Yan, Jian Chen, Tao He, Haoze Xu, Jianmin Chen, Liu Xiaoyu, Liye Zhong, Yan Li, Chunyan Sun, Xiaobo Wang, Li Gao, Fengyan Jin, Bingzong Li, Chunrui Li, Yingyou Wen, Zhuo Chen, Boheng Liu, Jian Li, Huihan Wang, Wei Yan, Jian Chen, Tao He, Haoze Xu, Jianmin Chen, Liu Xiaoyu, Liye Zhong, Yan Li, Chunyan Sun, Xiaobo Wang, Li Gao, Fengyan Jin, Bingzong Li, Chunrui Li, Yingyou Wen, Zhuo Chen, Boheng Liu, Jian Li, Huihan WangList of authors in order
- Landing page
-
https://doi.org/10.1007/s00277-025-06674-7Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00277-025-06674-7.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s00277-025-06674-7.pdfDirect OA link when available
- Concepts
-
Medicine, Amyloidosis, Receiver operating characteristic, Artificial intelligence, Internal medicine, Ejection fraction, Random forest, Creatinine, Confidence interval, Brain natriuretic peptide, Diagnostic accuracy, Fabry disease, Machine learning, AL amyloidosis, Decision tree, Hepatology, Interventricular septum, Natriuretic peptide, Pathology, Troponin T, Cardiology, Troponin, Ensemble learning, Elevated alkaline phosphatase, Area under the curve, Overcrowding, Kidney disease, Autoencoder, Fractal analysis, Predictive value of tests, Amyloid (mycology), Nephrology, Support vector machine, DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
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