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Updated: Jan 13, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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A Machine Learning Approach to Predicting Radiographic Outcomes of Nonsurgically Treated Distal Radius Fractures.

Eric R Taleghani¹, Ruihong Lyu, Taylor Shackleford

¹From the Department of Orthopaedic Surgery, University of Cincinnati (Taleghani, Shackleford, Rex, Hale, and Florczynski), and University of Cincinnati College of Engineering and Applied Science, Cincinnati, OH (Lyu and Talavage).

The Journal of the American Academy of Orthopaedic Surgeons

|January 6, 2026

Summary

This summary is machine-generated.

A machine learning model accurately predicts outcomes for distal radius fractures treated non-surgically. Key predictors include postreduction parameters, emphasizing the importance of high-quality closed reduction for successful fracture healing.

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Area of Science:

Orthopedics
Radiology
Machine Learning

Background:

Existing statistical models for distal radius fracture stability lack consistent reproducibility.
Machine learning (ML) offers a potential solution for predicting radiographic outcomes.

Purpose of the Study:

To develop and validate a machine learning model for predicting radiographic outcomes of non-surgically treated distal radius fractures.
To identify key radiographic and demographic predictors of treatment success.

Main Methods:

Retrospective review of adult patients with displaced distal radius fractures undergoing closed reduction.
Training five ML models using pre- and postreduction radiographic parameters and demographic data.
Developing a composite model using the top 10 predictive parameters based on Shapley values.

Main Results:

The composite ML model achieved 81% accuracy, 0.84 AUC, and 0.81 F1 score in predicting 6-week outcomes.
Postreduction palmar tilt, radial height, and Lindstrom score were most predictive of success.
Specific cutoffs for ulnar variance, dorsal tilt, and radial inclination predicted treatment failure.

Conclusions:

An ML model can accurately predict 6-week radiographic outcomes for non-surgically managed distal radius fractures.
High-quality closed reduction, indicated by postreduction parameters, is crucial for successful outcomes.
ML demonstrates significant potential as a predictive tool in orthopedic fracture management.