ArticleChildren (Basel, Switzerland)2024
Automated Measurements of Long Leg Radiographs in Pediatric Patients: A Pilot Study to Evaluate an Artificial Intelligence-Based Algorithm.
Article in Children (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Applications of Artificial Intelligence in Pediatric Orthopaedics.Indian journal of orthopaedics · 2026Review
- Prospective video-based analysis of coronal lower limb alignment may be as accurate as radiography in children.BMC musculoskeletal disorders · 2025Article
- Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
- Inflammation-induced leg length discrepancy in children: from molecular mechanisms to clinical implications.Frontiers in medicine · 2025Review
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4 authors.
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Abstract
backgroundAssessment of long leg radiographs (LLRs) in pediatric orthopedic patients is an important but time-consuming routine task for clinicians. The goal of this study was to evaluate the performance of artificial intelligence (AI)-based leg angle measurement assistant software (LAMA) in measuring LLRs in pediatric patients, compared to traditional manual measurements.
methodsEligible patients, aged 11 to 18 years old, referred for LLR between January and March 2022 were included. The study comprised 29 patients (58 legs, 377 measurements). The femur length, tibia length, full leg length (FLL), leg length discrepancy (LLD), hip-knee-ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), and mechanical medial proximal tibial angle (mMPTA) were measured automatically using LAMA and compared to manual measurements of a senior pediatric orthopedic surgeon and an advanced practitioner in radiography.
resultsCorrect landmark placement with AI was achieved in 76% of the cases for LLD measurements, 88% for FLL and femur length, 91% for mLDFA, 97% for HKA, 98% for mMPTA, and 100% for tibia length. Intraclass correlation coefficients (ICCs) indicated moderate to excellent agreement between AI and manual measurements, ranging from 0.73 (95% confidence interval (CI): 0.54 to 0.84) to 1.00 (95%CI: 1.00 to 1.00).
conclusionIn cases of correct landmark placement, AI-based algorithm measurements on LLRs of pediatric patients showed high agreement with manual measurements.
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