<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Yan W</submitter><funding>Shingling Liao Ning Revitalization Talents Program</funding><funding>Key R&amp;D Program of Liaoning Province</funding><funding>Key R&amp;amp;D Program of Liaoning Province</funding><funding>Natural Science Foundation of Liaoning Province</funding><funding>National Natural Science Foundation of China</funding><funding>the 345 Talent Project in Shengjing Hospital of China Medical University</funding><pagination>6315-6326</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12764643</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>104(12)</volume><pubmed_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 p</pubmed_abstract><journal>Annals of hematology</journal><pubmed_title>Development of an artificial intelligence-based early diagnostic system for light-chain amyloidosis.</pubmed_title><pmcid>PMC12764643</pmcid><funding_grant_id>Nos. 61972079 and 61772126</funding_grant_id><funding_grant_id>2019JH2/10100027</funding_grant_id><funding_grant_id>2022-YGJC-61;2022-MS-219</funding_grant_id><funding_grant_id>XLYC1802100</funding_grant_id><pubmed_authors>Wen Y</pubmed_authors><pubmed_authors>Li J</pubmed_authors><pubmed_authors>Li C</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Sun C</pubmed_authors><pubmed_authors>Wang H</pubmed_authors><pubmed_authors>Xu H</pubmed_authors><pubmed_authors>Yan W</pubmed_authors><pubmed_authors>Liu X</pubmed_authors><pubmed_authors>Jin F</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>He T</pubmed_authors><pubmed_authors>Chen Z</pubmed_authors><pubmed_authors>Gao L</pubmed_authors><pubmed_authors>Liu B</pubmed_authors><pubmed_authors>Wang X</pubmed_authors><pubmed_authors>Li B</pubmed_authors><pubmed_authors>Zhong L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Development of an artificial intelligence-based early diagnostic system for light-chain amyloidosis.</name><description>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 p</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Dec</publication><modification>2026-05-29T03:14:32.108Z</modification><creation>2026-05-29T03:11:23.9Z</creation></dates><accession>S-EPMC12764643</accession><cross_references><pubmed>41225023</pubmed><doi>10.1007/s00277-025-06674-7</doi></cross_references></HashMap>