Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Comparison Between Artificial Intelligence-Based Models and Traditional Risk Scores for Predicting Risks in Adult

Tobias K Fuchs¹, Cameron Jones¹, Michael Breiner²

¹Edward Via College of Osteopathic Medicine (VCOM), Clinical Sciences, Blacksburg, Virginia.

The Journal of Surgical Research

|June 17, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Why Total Artificial Hearts Fail: Hemocompatibility, Infection, and the Biological Limits of Durable Cardiac Replacement.

Cureus·2026

Same author

Magnesium(0) complexes and their reduction reactions with binary transition metal carbonyls.

Chemical science·2026

Same author

Early-activated extracellular matrix proteins shape the metabolic and spatial dynamics of the kidney fibrotic microenvironment.

Nature metabolism·2026

Same author

Hog1/p38 and ZAKα drive Shwachman-Diamond syndrome and provide targets to improve cell growth.

bioRxiv : the preprint server for biology·2026

Same author

Evaluation of a Community-Based Chronic Kidney Disease Screening Program at a Senior Living Community in Blacksburg, Virginia.

Cureus·2026

Same author

Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Human.

Diabetes & metabolism journal·2026

Same journal

A Mixed-Methods Study of the Variation in Routine Preoperative Clinic Utilization.

The Journal of surgical research·2026

Same journal

Impact of Social Support on Patient-Reported Outcomes in Cardiothoracic Surgery: A Systematic Review.

The Journal of surgical research·2026

Same journal

Association Between Daytime Liver Transplantation and Hospital Length of Stay and Graft and Patient Survival.

The Journal of surgical research·2026

Same journal

"Understanding the Effect of Transfusion Rates on Differential Modulation of Inflammatory Responses".

The Journal of surgical research·2026

Same journal

The Association Between the Risk Analysis Index of Frailty and Patient Safety Indicators: A Single-System Cohort Study.

The Journal of surgical research·2026

Same journal

Geographic Impact of Surgical Conferences on Travel Burden and Carbon Emissions: Multisociety Study.

The Journal of surgical research·2026

See all related articles

This summary is machine-generated.

Machine learning (ML) models show promise in improving cardiac surgery risk prediction compared to traditional scores. Further validation is needed to address limitations like data leakage and overfitting before widespread clinical use.

Area of Science:

Cardiovascular Surgery
Medical Informatics
Artificial Intelligence in Medicine

Background:

Traditional risk scores (e.g., EuroSCORE II) may not capture complex interactions in cardiac surgery.
Machine learning (ML) offers nonlinear modeling to potentially improve risk prediction.

Purpose of the Study:

Systematically review studies comparing ML models with traditional risk scores for perioperative mortality and adverse events in adult cardiac surgery.
Focus on literature published between 2020 and 2026.

Main Methods:

Systematic review following PRISMA 2020 guidelines.
Searched PubMed, Google Scholar, and Cochrane (Jan 2020 - Jan 2026).
Assessed bias using Prediction model Risk Of Bias ASsessment Tool+ artificial intelligence.

Keywords:

Artificial intelligence Cardiac surgery EuroSCORE Machine learning Risk prediction STS

Related Experiment Videos

Main Results:

Thirteen studies compared ML and traditional scores.
ML models demonstrated improved or comparable performance (AUC differences 0.006-0.42) and calibration.
Specific algorithms like extreme gradient boosting and random forest showed high AUC for various outcomes.

Conclusions:

ML models, particularly ensembles, may enhance cardiac surgery risk prediction.
Further prospective multicenter validation is crucial.
Address data leakage, overfitting, temporal drift, and algorithmic fairness for clinical integration.