{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T05:48:31Z","timestamp":1791611311364,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Artificial Organs"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The contemporary intensive care unit generates an overwhelming magnitude of high\u2010frequency, granular physiological data. However, conventional decision\u2010making for patients requiring temporary mechanical circulatory support (tMCS) remains tethered to reactive, threshold\u2010driven manual adjustments, underutilizing continuous data streams and failing to optimize the complex patient\u2013machine interaction. This review examines the integration of deep neural networks (DNNs) as a candidate strategy for advanced clinical decision support. By identifying a continuous \u201cdigital phenotype,\u201d hierarchical deep learning architectures signal a shift toward real\u2010time trajectory recognition in tMCS. We establish a four\u2010tier functional taxonomy\u2014prognosticative, descriptive, predictive, and prescriptive\u2014to categorize translational milestones from automated pump\u2010speed optimization to closed\u2010loop homeostatic regulation. Operational challenges are analyzed through the TEIP (Transparency, Explainability, Interpretability, and Performance) Core\u2010Lens Framework, navigating the dynamic equilibrium between computational density and clinical explainability. We further evaluate the evolving global regulatory landscape, advocating for a foundation on verified empirical safety and rigorous clinical trial validation over absolute mathematical transparency in high\u2010density architectures. Implementing a strategic roadmap including context\u2010specific validation, multimodal integration, and human\u2010centered design is quintessential to safely transform passive hardware into intelligent systems. As the field evolves, DNN technology offers a consequential opportunity to advance early cardiopulmonary failure recognition into a clinical reality.<\/jats:p>","DOI":"10.1111\/aor.70265","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:50:56Z","timestamp":1791607856000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Neural Network\u2013Driven Early Recognition in Cardiopulmonary Failure: A Paradigm Shift Underway in Temporary Mechanical Circulatory Support?"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4967-8707","authenticated-orcid":false,"given":"Monika","family":"Tukacs","sequence":"first","affiliation":[{"name":"ECMO Program for Adults and Pediatrics New York Presbyterian\/Weill Cornell Medicine  New York New York USA"},{"name":"Department of Respiratory Medicine Paracelsus Medical University Nuremberg  Nuremberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9985-4126","authenticated-orcid":false,"given":"Elena Giovanna","family":"Bignami","sequence":"additional","affiliation":[{"name":"Anesthesiology, Critical Care, and Pain Medicine Division, Department of Medicine and Surgery University of Parma  Parma Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5871-5186","authenticated-orcid":false,"given":"Patrick M.","family":"Wieruszewski","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology Mayo Clinic  Rochester Minnesota USA"},{"name":"Department of Pharmacy Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-9160","authenticated-orcid":false,"given":"Jacopo","family":"D\u2019Andria Ursoleo","sequence":"additional","affiliation":[{"name":"Department of Anesthesia and Intensive Care IRCCS San Raffaele Scientific Institute  Milan Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2903-092X","authenticated-orcid":false,"given":"Justyna","family":"Swol","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine Paracelsus Medical University Nuremberg  Nuremberg 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