{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:33:43Z","timestamp":1791650023228,"version":"4.3.4"},"reference-count":180,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from \u201craw data\u201d without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.<\/jats:p>","DOI":"10.3390\/ijms24065680","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T04:13:20Z","timestamp":1678940000000},"page":"5680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6156-8239","authenticated-orcid":false,"given":"Alessandro","family":"Allegra","sequence":"first","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Mirabile","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Human Pathology in Adulthood and Childhood \u201cGaetano Barresi\u201d, University of Messina, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8335-5541","authenticated-orcid":false,"given":"Alessandro","family":"Tonacci","sequence":"additional","affiliation":[{"name":"Clinical Physiology Institute, National Research Council of Italy (IFC-CNR), 56124 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Genovese","sequence":"additional","affiliation":[{"name":"Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-7449","authenticated-orcid":false,"given":"Giovanni","family":"Pioggia","sequence":"additional","affiliation":[{"name":"Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastiano","family":"Gangemi","sequence":"additional","affiliation":[{"name":"Allergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1097\/00019052-199312000-00008","article-title":"Alzheimer\u2019s disease and Creutzfeldt-Jakob disease: Overlap of pathogenic mechanisms","volume":"6","author":"DeArmond","year":"1993","journal-title":"Curr. 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