Abstract

Ultrasound imaging is a clinically feasible tool to assess femoral articular cartilage and may have utility in tracking early knee osteoarthritis development. Traditional assessment techniques focus on measurements at a single location, which can be challenging to adopt for novice raters. To introduce a novel semiautomated ultrasound segmentation technique and determine the intrarater and interrater reliability of average regional femoral articular cartilage thickness and echo intensity of a novice and expert rater. Descriptive observational study. Orthopedic clinic. Fifteen participants (mean [SD]; age 23.5 [4.6]y, height = 172.6 [9.3]cm, mass = 79.8 [15.7]kg) with a unilateral history of anterior cruciate ligament reconstruction participated. None. One rater captured anterior femoral cartilage images of the participants' contralateral knees using a transverse suprapatellar ultrasound assessment. The total femoral cartilage cross-sectional area of each image was segmented by a novice and expert rater. A novel custom program automatically separated the cartilage segmentations into medial, lateral, and intercondylar regions to determine the cross-sectional area and cartilage length. The average cartilage thickness in each region was calculated by dividing the cross-sectional area by the cartilage length. Echo intensity was calculated as the average gray-scale pixel value of each region. Two-way random effect intraclass correlations coefficient (ICC) for absolute agreement were used to determine the interrater reliability between a novice and expert rater, as well as the intrarater reliability of the novice rater. The novice rater demonstrated excellent intrarater (ICC [2,k] range = .993-.997) and interrater (ICC [2,k] range = .944-.991) reliability with the expert rater of all femoral articular cartilage average thickness and echo intensity regions. The novel semiautomated average cartilage thickness and echo-intensity assessment is efficient, systematic, and reliable between an expert and novice rater with minimal training.

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