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April 8, 2026Cardiology in Review

Artificial Intelligence–Enabled Prediction of Reversible Versus Irreversible Chemotherapy-Induced Myocardial Injury: Toward Precision Cardio-Oncology

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Why the study?

Conventional surveillance using serial LVEF often detects cancer therapy-related cardiac dysfunction only after irreversible remodeling has occurred, leaving early differentiation of reversible myocardial dysfunction from permanent structural injury an unmet need.

Design

Narrative review

Key result

Artificial intelligence models show improved sensitivity over conventional surveillance for identifying subclinical chemotherapy-induced myocardial dysfunction and predicting its reversibility.

Authors

PAParth AdrejiyaNNNegarsadat NeshatAPAnsy Patel

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Overview

May aid differentiation of reversible from irreversible chemotherapy-induced injury; hypothesis-generating pending prospective validation.

Key Points

  • To explore artificial intelligence applications for predicting reversible and irreversible myocardial injury caused by chemotherapy in cancer patients.
  • Review of contemporary AI techniques in cardio-oncology
  • Integration of clinical data from electronic health records and biomarkers
  • Evaluation of machine learning models for sensitivity in identifying myocardial dysfunction
  • Proposal of a framework for assessing myocardial injury reversibility
  • Machine learning models improve sensitivity for detecting subclinical myocardial dysfunction
  • Differentiation of transient myocardial injury from permanent fibrosis is enhanced
  • Highlights the need for further validation and development for clinical applicability

Cite This Study

Adrejiya et al. (2026) studied this question. Artificial intelligence models show improved sensitivity over conventional surveillance for identifying subclinical chemotherapy-induced myocardial dysfunction and predicting its reversibility.

synapsesocial.com/papers/69d5f09e74eaea4b11a7a050https://doi.org/10.1097/crd.0000000000001268
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