[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85763-en":3,"doc-seo-85763-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85763,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Artificial Intelligence Across the Cardiac Amyloidosis Diagnostic Pathway: From Single- Modality Detection to Multimodal Clinical Integration","Cardiac amyloidosis (CA) is increasingly recognized yet substantially underdiagnosed due to overlapping clinical and imaging phenotypes with more common cardiomyopathies. Accurate subtype assignment and management require integrating multimodal evidence to distinguish transthyretin from light-chain disease. This narrative review synthesizes machine-learning and deep-learning studies across tasks including screening, detection, quantification, prognosis, and treatment-response monitoring, highlighting differences in cohorts, reference standards, metrics, and thresholds.","Artificial Intelligence Across the Cardiac Amyloidosis Diagnostic Pathway: From SingleModality Detection to Multimodal Clinical Integration  \nDiana Shadibaeva1†, Rochak Dhakal1†, Kui Zhang2, Xiaofeng Yang3, Saurabh Malhotra4,5, Weihua Zhou1,6*  \n1 Department of Applied Computing, Michigan Technological University, Houghton, MI, USA  \n2 Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA  \n3 Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL  \n4 Division of Cardiology, Cook County Health and Hospitals System, Chicago, IL, USA  \n5 Division of Cardiology, Rush Medical College, Chicago, IL, USA  \n6 Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA  \n† Diana Shadibaeva and Rochak Dhakal contributed equally to this paper.  \n* Corresponding author:  \nWeihua Zhou, Ph.D.  \nDepartment of Applied Computing, Michigan Technological University,  \n[E-Mail: whzhou@mtu.edu](E-Mail: whzhou@mtu.edu)  \nAbstract - Cardiac amyloidosis (CA) is increasingly recognized but remains substantially underdiagnosed, because its clinical and imaging phenotype overlaps with more common cardiomyopathies. Definitive subtype assignment and management further require integration of multimodal evidence to distinguish transthyretin from light chain disease. Machine learning and deep learning have been applied across the diagnostic and management pathway. These applications span ECG, echocardiography, and health record-based case finding, as well as CMRand nuclear interpretation, including SPECT/CT biomarker quantification, prognostic modeling, and treatment response assessment. This narrative review synthesizes these studies by clinical tasks, namely screening, detection, quantification, prognosis, and treatment response monitoring, rather than by input modality. This task-based organization clarifies why apparently similar AI models require different cohorts, reference standards, evaluation metrics, and implementation thresholds. The evidence reveals a maturity gradient. Binary detection and AI assisted quantification on bone scintigraphy and SPECT/CT are closest to clinical translation. Detection is supported by large externally validated cohorts, and quantification by interpretable, outcome linked measurement of myocardial tracer burden. By contrast, subtype aware classification, prognostic risk stratification, and treatment response monitoring remain at an early stage. These tasks are limited by small cohorts, enriched retrospective designs , heterogeneous labels, incomplete external validation, and uncertain calibration in realistic prevalence settings. Across tasks, high discrimination alone is insufficient. Clinically useful AI must demonstrate reliable performance against relevant mimics, maintain performance across patient subgroups, avoid circular reference standards, and improve referral or management decisions within existing diagnostic workflows. We argue that the field should now move beyond additional single modality classifiers toward multimodal, subtype aware, and longitudinally validated systems that support, rather than replace, established diagnostic algorithms.  \nKeywords: Cardiac amyloidosis; machine learning; multimodal imaging; nuclear cardiology; quantitative imaging biomarkers.  \n1. Introduction  \nCardiac amyloidosis (CA) is a progressive infiltrative cardiomyopathy caused by extracellular deposition of misfolded amyloid fibrils within the myocardium, producing increased ventricular wall thickness, myocardial stiffness, diastolic dysfunction, restrictive physiology, arrhythmias, and eventual heart failure [1–3] . Most cases are caused by one of two major subtypes: transthyretinamyloidosis (ATTR) or immunoglobulin light-chain amyloidosis (AL) . These subtypes differ fundamentally in pathogenesis, prognosis, and treatment urgency [2–4] . ATTR arises from mi","cbCaitSDpg82hA6c","https://ap.wps.com/l/cbCaitSDpg82hA6c","pdf",1092662,1,37,"English","en",105,"# Introduction\n## Clinical context and underdiagnosis\n## Diagnostic workflow and remaining challenges\n## Machine learning and deep learning motivation\n# Diagnostic pathway and AI task organization\n## Screening and detection\n## Quantification, prognosis, and treatment response\n## Evidence maturity and translational readiness","[{\"question\":\"Why is cardiac amyloidosis frequently underdiagnosed?\",\"answer\":\"Its clinical and imaging phenotype overlaps with more common cardiomyopathies, and patients are often identified late after substantial myocardial involvement.\"},{\"question\":\"How do ATTR and AL subtypes differ in clinical implications?\",\"answer\":\"They differ in pathogenesis, prognosis, and treatment urgency; subtype identification determines whether hematology-directed therapy, ATTR-specific disease-modifying treatment, genetic testing, or tissue confirmation is needed.\"},{\"question\":\"Which AI tasks appear most ready for clinical translation?\",\"answer\":\"Binary detection and AI-assisted quantification on bone scintigraphy and SPECT/CT are closest to clinical integration, supported by large externally validated cohorts and interpretable, outcome-linked tracer-burden measurements.\"}]",1784206106,93,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"artificial-intelligence-across-the-cardiac-amyloidosis-diagnostic-pathway-from-single-modality-detection-to-multimodal-clinical-integration","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/artificial-intelligence-across-the-cardiac-amyloidosis-diagnostic-pathway-from-single-modality-detection-to-multimodal-clinical-integration/85763/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is cardiac amyloidosis frequently underdiagnosed?","Question",{"text":75,"@type":76},"Its clinical and imaging phenotype overlaps with more common cardiomyopathies, and patients are often identified late after substantial myocardial involvement.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do ATTR and AL subtypes differ in clinical implications?",{"text":80,"@type":76},"They differ in pathogenesis, prognosis, and treatment urgency; subtype identification determines whether hematology-directed therapy, ATTR-specific disease-modifying treatment, genetic testing, or tissue confirmation is needed.",{"name":82,"@type":73,"acceptedAnswer":83},"Which AI tasks appear most ready for clinical translation?",{"text":84,"@type":76},"Binary detection and AI-assisted quantification on bone scintigraphy and SPECT/CT are closest to clinical integration, supported by large externally validated cohorts and interpretable, outcome-linked tracer-burden measurements.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]