Evidence map›Paper›PMID 41843807›Full record

ArticleJournal of the American Academy of Orthopaedic Surgeons. Global research & reviews2026

Accuracy of Diagnosis and Anticipation of Future Treatment in Pediatric Peripelvic Infections: The Role of Multisurgeon Review and Implications for Future Machine Learning Algorithms.

Tyler M Staten, Jarrett Warden, Pamela J Lang, Joel Turtle, Kenneth J Noonan, Laura L Bellaire

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Article in Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Tyler M StatenFrom the Department of Orthopaedic Surgery, University of Utah, Salt Lake City, UT (Staten, Dr. Turtle, and Dr. Bellaire); the Department of Orthopaedic surgery, University of Michigan Health-West, Wyoming, MI (Dr. Warden); the Shriners Children's Twin Cities Hospital, Woodbury, MN (Dr. Lang); and the Department of Orthopaedics and Rehabilitation, University of Wisconsin, Madison WI (Noonan).ORCID 0009-0000-7709-6248
Jarrett Warden
Pamela J Lang
Joel Turtle
Kenneth J Noonan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMRI is commonly used to evaluate pelvic musculoskeletal infections. Limited "quick" MRI protocols enable timely imaging without intravenous contrast or sedation. This study examines the consistency of interpretation among pediatric orthopaedic surgeons when using quick versus full, contrast-enhanced MRI scans in cases of peripelvic musculoskeletal infection and explores these findings to inform future development and design of machine learning algorithms.

methodsTwenty-nine pediatric patients with full pelvis MRI with and without contrast and culture-positive peripelvic infection were retrospectively identified. Two deidentified files were created for each patient: one including all sequences and the other containing only the limited sequences included in our institution's quick MRI protocol. Three pediatric orthopaedic surgeons independently and sequentially evaluated the images, followed by group discussion to reach consensus on the primary diagnosis and management. Fleiss' Kappa (FK) statistic was calculated for each outcome.

resultsModerate agreement in primary diagnosis was observed among reviewers using quick MRI sequences (Kappa = 0.488), and substantial agreement was seen with full sequences (0.684; P = 0.003). Inter-rater agreement on treatment recommendations was poor with both quick (0.09) and full (0.233) MRI (P = 0.046). No difference was found in team consensus diagnosis and final diagnosis between quick (0.523) and full (0.569) MRI (P = 0.662). Poor agreement was found between team treatment recommendations and actual treatment for both quick (0.182) and full (0.07) MRI (P = 0.254).

conclusionIndependent evaluation of limited, quick MRI sequences by pediatric orthopaedic surgeons showed more variability in diagnosis and treatment compared with full MRI review. When reviewed collaboratively, the diagnostic accuracy of quick MRI approached that of full MRI. Future artificial intelligence-based imaging interpretation platforms will benefit from multi-institutional collaboration to improve training data quality; use of ensemble learning techniques to reflect the diversity of multispecialist approaches; and incorporation of relevant clinical data to properly identify, triage, and direct treatment of complex pediatric musculoskeletal conditions.

Indexed as

Machine LearningMagnetic Resonance ImagingPelvisAdolescentAlgorithmsChildChild, PreschoolFemaleHumansMaleRetrospective Studies

Identifiers

PMID41843807
PMCPMC12999165

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.