Early Hemodynamic Prediction: A Critical Review and Clinical Insights
Year: 2026
Type: article
Abstract: Blood flow abnormalities are central to cardiovascular and cerebrovascular diseases, yet early detection remains challenging due to invasive or resource‑intensive traditional methods. Deep neural networks (DNNs) offer a potential non‑invasive alternative by inferring hemodynamic states from medical images and physiological signals. In this critical review, we examine DNN methods for early prediction of problematic hemodynamics—defined as detecting dysfunction before irreversible damage or clinical symptoms appear. We focus on architectures, data modalities, validation practices, and clinical applicability. Our combined database and manual search yielded 192 studies. Over 70% of relevant studies were identified through manual reference screening, not primary database queries—revealing severe terminological fragmentation. Among studies that specified their architecture, convolutional neural networks (CNNs) predominated (image‑based tasks). Recurrent networks, transformers, and physics‑informed models appeared less often despite their theoretical fit for temporal flow data. Most studies lacked external validation, and many failed to report architecture or key metrics. Physics‑informed networks offer interpretability promise, but clinical translation is hindered by heterogeneous datasets, inconsistent reporting, and black‑box models. Because no validated quality tool exists for DNN‑hemodynamic studies, we developed a custom eight‑domain checklist. The field has grown rapidly (most studies since 2020) but has major weaknesses: external validation was rare, prospective evaluations exceptional, and statistical rigor often inadequate. We conclude that DNNs hold promise for non‑invasive hemodynamic prediction, but clinical readiness is limited. Future research should prioritize multi‑center prospective studies, standardized reporting (e.g., TRIPOD‑AI), explainable AI, and open datasets. This review is a critical synthesis with transparent search limitations, not a full systematic review.
Institutions: Al-Azhar University, Menoufia University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7222834085
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