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TRUVACE RECORD VERSION
record: TRV-2026-1176
version: 2
kind: revised
reason: Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
timestamp: 2026-11-04T09:50:24.026202Z
status: published
lens: g_space
sector: health
headline: Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort
dek: Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children. Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and D…
gain_title: In a single-center review of 2,508 patients, the AI's high-confidence fracture detections on X-rays were correct 99.1% of the time, and the authors judged overall performance good.
problem_title: (none)
trace_subject: (none)
gain_reading: In a single-center review of 2,508 patients, the AI's high-confidence fracture detections on X-rays were correct 99.1% of the time, and the authors judged overall performance good.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children.

The index test was the algorithm output (bounding boxes with confidence). Sensitivity, specificity, PPV and negative predictive value (NPV) were calculated with 95% confidence intervals; differences were tested using the McNemar and a score test and patient-clustered logistic regression.
limitation: 
tag: Evidence-backed gain
key_points: Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. | Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children. | Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and December 2024.
rundown: Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children.

Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and December 2024. The index test was the algorithm output (bounding boxes with confidence).
sources:
- peer_reviewed | Emergency Radiology | https://doi.org/10.1007/s10140-026-02546-3 | 2026-09-22
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