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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: Jul 1, 2026

Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening
10:39

Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening

Published on: April 15, 2017

TPNET: A Time-Sensitive Small Sample Multimodal Network for Cardiotoxicity Risk Prediction.

Yuan He, Fengyun Zhang, Kaimiao Hu

    IEEE Journal of Biomedical and Health Informatics
    |March 19, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep learning model using Tissue Doppler Imaging (TDI) to predict cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients. The TPNET model shows high accuracy, aiding in early detection and understanding of cardiac risks.

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    Last Updated: Jul 1, 2026

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    14:03

    High-Throughput Cardiotoxicity Screening Using Mature Human Induced Pluripotent Stem Cell-Derived Cardiomyocyte Monolayers

    Published on: March 24, 2023

    Area of Science:

    • Cardiology
    • Oncology
    • Artificial Intelligence

    Background:

    • Cancer therapy-related cardiac dysfunction (CTRCD) is a significant concern, especially for breast cancer patients.
    • Monitoring cardiac health during cancer treatment is crucial.
    • Tissue Doppler Imaging (TDI) offers insights into left ventricle function.

    Purpose of the Study:

    • To develop and evaluate a deep learning model for predicting CTRCD using TDI and clinical data.
    • To assess the model's performance in identifying patients at risk of cardiac dysfunction over 24 months.
    • To identify key pathogenic signs and potential new causative agents of CTRCD.

    Main Methods:

    • Development of a temporal-multimodal pattern network for efficient training (TPNET) model.
    • Utilized TDI, functional, and clinical data from 270 patients.
    • Employed integrated gradients (IG) attribution for feature analysis.

    Main Results:

    • The TPNET model achieved an area under the curve (AUC) of 0.83 and a sensitivity of 0.88.
    • Demonstrated superior robustness compared to existing visual models.
    • Identified key pathogenic signs and potential novel causative agents for CTRCD.

    Conclusions:

    • The TPNET model shows significant potential for predicting CTRCD in breast cancer patients.
    • The model can aid in early detection and risk stratification.
    • Feature attribution analysis offers insights into CTRCD pathogenesis and may guide clinical application in preoperative settings.