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TRUVACE RECORD VERSION
record: TRV-2026-1778
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T01:11:01.146532Z
status: published
lens: p_space
sector: health
headline: Measuring Knee Alignment Following Distal Femur Fracture Surgery: A Novel Deep Learning Approach
dek: Objectives To evaluate whether a machine learning-based system can accurately measure coronal knee alignment parameters from routine postoperative radiographs following surgical fixation of distal femur fractures. Methods Design: Retrospective cohort study. Setting Single-center academic orthopedic trauma practice. Patient selection criteria Adult patients 50 years of age or older who underwent surgical fixation of low-energy OTA/AO 33A and 33C distal femur fractures (including periprosthetic fractures) between…
gain_title: (none)
problem_title: Measuring Knee Alignment Following Distal Femur Fracture Surgery: A Novel Deep Learning Approach: Pearson correlation coefficients ranged from 0.4 to 1.0, and Bland-Altman analysis demonstrated minimal systematic bias, with 95% of differences falling within a clinically acceptable range of ±5°.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Measuring Knee Alignment Following Distal Femur Fracture Surgery: A Novel Deep Learning Approach: Pearson correlation coefficients ranged from 0.4 to 1.0, and Bland-Altman analysis demonstrated minimal systematic bias, with 95% of differences falling within a clinically acceptable range of ±5°.
problem_evidence: (none)
quick_read: Objectives To evaluate whether a machine learning-based system can accurately measure coronal knee alignment parameters from routine postoperative radiographs following surgical fixation of distal femur fractures. Methods Design: Retrospective cohort study.

Outcome measures and comparisons A machine learning-based automated alignment assessment system was trained to identify standard radiographic landmarks, including the femoral intercondylar notch, femoral condyles, tibial spines, and tibial plateaus, and automatically calculate commonly used coronal alignment parameters including joint line convergence angle (JLCA), anatomic femorotibial angle (aFTA), anatomic lateral distal femoral angle (aLDFA), and anatomic medial proximal tibial angle (aMPTA). The cohort was 78.0% female with a mean age of 72.3 years (range: 50 to 97 years).
limitation: 
tag: Evidence-backed problem
key_points: Objectives To evaluate whether a machine learning-based system can accurately measure coronal knee alignment parameters from routine postoperative radiographs following surgical fixation of distal femur fractures. | Methods Design: Retrospective cohort study. | Setting Single-center academic orthopedic trauma practice.
rundown: Objectives To evaluate whether a machine learning-based system can accurately measure coronal knee alignment parameters from routine postoperative radiographs following surgical fixation of distal femur fractures. Methods Design: Retrospective cohort study.

Setting Single-center academic orthopedic trauma practice. Patient selection criteria Adult patients 50 years of age or older who underwent surgical fixation of low-energy OTA/AO 33A and 33C distal femur fractures (including periprosthetic fractures) between November 2020 and December 2024 were retrospectively identified.
sources:
- peer_reviewed | Journal of Orthopaedic Trauma | https://doi.org/10.1097/bot.0000000000003300 | 2026-10-07
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