A mixed reality framework for interpretable and explainable joint replacement assessment

The patient-specific MR framework provides a reliable, quantitative, and interpretable method for assessing prosthetic joint performance after replacement, supporting its integration into postoperative workflows for improved surgical outcome assessment.

Authors

Institutions

Publication Details

Journal
International Journal of Medical Informatics
Published
2026-03-07
DOI
https://doi.org/10.1016/j.ijmedinf.2026.106380
Primary Topic
Total Knee Arthroplasty Outcomes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A mixed reality framework for interpretable and explainable joint replacement assessment

Chiara Innocente, Sandro Moos, Giorgia Marullo, Enrico Vezzetti et al.
International Journal of Medical Informatics
Total Knee Arthroplasty Outcomes
article

A mixed reality framework for interpretable and explainable joint replacement assessment

Chiara Innocente, Sandro Moos, Giorgia Marullo, Enrico Vezzetti, Luca Ulrich, Alessandro Aprato, Andrea Audisio, Alessandro Massè
article en

Abstract

The patient-specific MR framework provides a reliable, quantitative, and interpretable method for assessing prosthetic joint performance after replacement, supporting its integration into postoperative workflows for improved surgical outcome assessment.

International Journal of Medical InformaticsVol. 213
Politecnico di Torino (IT), University of Turin (IT)
Peace, Justice and strong institutions
Openalex Percentile: Top 30%
Total Knee Arthroplasty Outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.