Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1280
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-05T06:54:47.758252Z
status: published
lens: trace
sector: health
headline: Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis
dek: Objective To evaluate the diagnostic accuracy of imaging artificial intelligence (AI) models for detecting avascular necrosis of the femoral head (AVNFH) and to examine whether diagnostic performance differs according to imaging modality, disease-stage focus, and validation strategy. Materials and methods In accordance with PRISMA-DTA, we conducted a systematic search in four main databases. Two independent reviewers evaluated the studies, extracted relevant data, and assessed the quality following the QUADAS-2…
gain_title: Imaging AI models achieved high pooled diagnostic accuracy for detecting avascular necrosis of the femoral head in research datasets.
problem_title: Risk of bias, heterogeneity, and limited external testing restrict confidence that reported accuracy will translate to routine clinical use.
trace_subject: diagnostic accuracy of imaging AI for detecting avascular necrosis of the femoral head
gain_reading: Imaging AI models achieved high pooled diagnostic accuracy for detecting avascular necrosis of the femoral head in research datasets.
gain_evidence: Imaging AI models showed promising accuracy for AVNFH detection in research datasets
problem_reading: Risk of bias, heterogeneity, and limited external testing restrict confidence that reported accuracy will translate to routine clinical use.
problem_evidence: risk of bias, between-study heterogeneity, and limited external testing restrict confidence in their clinical applicability
quick_read: A systematic review and meta-analysis of imaging AI for avascular necrosis of the femoral head pooled nine estimates and reported sensitivity of 0.888, specificity of 0.937, and SROC AUC of 0.967, with MRI-based models showing higher sensitivity than radiography-based models.

The findings suggest promising research-stage performance, but the authors emphasize that risk of bias, heterogeneity, and reliance on only five external test-set estimates limit confidence for clinical use, leaving prospective multicenter validation and demonstrated clinical benefit as unresolved steps.
limitation: Confidence in clinical applicability is limited by risk of bias, between-study heterogeneity, and sparse external testing, with only five external test-set estimates in primary analysis.
tag: Dual reading
key_points: Systematic review and meta-analysis followed PRISMA-DTA with QUADAS-2 quality assessment across four databases. | Primary analysis pooled nine estimates for binary AVNFH detection using bivariate random-effects model. | MRI-based estimates showed higher sensitivity than radiography-based estimates in study-level comparison (p = 0.027). | Only five external test-set estimates contributed to primary analysis, limiting precision of subgroup comparisons.
rundown: The review searched four databases under PRISMA-DTA, with two independent reviewers extracting data and assessing quality via QUADAS-2. Nine estimates were pooled for binary detection, yielding sensitivity 0.888 and specificity 0.937 with SROC AUC 0.967.

Subgroup analyses found MRI-based estimates had higher sensitivity than radiography (p = 0.027), while no significant differences were found by disease-stage focus or validation strategy. Authors noted prospective, multicenter validation and evaluation of clinical benefit are needed before routine implementation.
sources:
- peer_reviewed | Skeletal Radiology | https://doi.org/10.1007/s00256-026-05376-z | 2026-10-04
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

e5cea0f3f722c1a91d0c0569e047994adad60bb8a8f6fb7fafa348bbf66a4454

previous

0000000000000000000000000000000000000000000000000000000000000000