Intra- and interobserver reliability analysis of pediatric lower limb parameters on digital long leg radiographs

Fully Automated Analysis of the Anatomic and Mechanical Axes From Pediatric Standing Lower Limb Radiographs Using Convolutional Neural Networks

BACKGROUND

Lower limb alignment is the quantification of a set of parameters that are commonly measured radiographically to test for and track a wide range of skeletal pathologies. Determining limb alignment is a commonly performed yet laborious task in the pediatric orthopaedic setting and is therefore an interesting goal for automation.

METHODS

We employ a machine learning approach using convolutional neural networks (CNNs) to segment pediatric weight-bearing lower limb radiographs. The results are then used with custom Matlab code to extract anatomic landmarks and to determine lower limb alignment parameters.

RESULTS

Measurements obtained from the automated workflow proposed here were compared with manual measurements performed by orthopaedic surgery fellows. Mechanical axis deviation was determined within a mean of 2.02 mm. Lateral distal femoral angle and medial proximal tibial angle were determined with a mean deviation of 1.73 and 2.90 degrees, respectively. The calculation speed for the full set of mechanical and anatomic axis parameters was found to be ~2 seconds per radiograph.

CONCLUSIONS

The CNN-based approach proposed in this work was shown to produce results comparable to orthopaedic surgery fellows at fast calculation speed. Although further work is needed to validate these results against radiographs and measurements from other centers, we see this as a promising start and a functional path that can be employed in further research.

CLINICAL RELEVANCE

CNNs are a promising approach to automating commonly performed, repetitive tasks, especially those pertaining to image processing. The time savings are particularly important in clinical research applications where large sets of radiographs are routinely available and require analysis. With further development of these algorithms, we anticipate significantly improved agreement with expert-measured results and the calculation speed.

Genu Valgum in Pediatric Patients Presenting With Patellofemoral Instability

BACKGROUND

Lower extremity valgus is a commonly described factor associated with patellofemoral instability (PFI) and, if identified before skeletal maturity, can be treated with guided growth. The prevalence of valgus alignment in the pediatric and adolescent PFI population is largely unknown.

PURPOSE

The aim of this study was to report the prevalence of valgus alignment in adolescent patients presenting with PFI; with secondary assessment of high-grade valgus (zone II or III), coronal asymmetry, and associations of these findings with body mass index (BMI).

STUDY DESIGN

A retrospective cohort study.

METHODS

A total of 279 consecutive patients (349 knees) with a diagnosis of PFI presenting to a single orthopedic pediatric sport medicine surgeon were identified. A retrospective chart review was performed to collect demographic and clinical data, chronologic and bone age, sex, BMI, mechanism of injury, and the presence of osteochondral fracture. Full-length standing hip-to-ankle alignment radiographs were graded for knee alignment mechanical zone utilizing standard linear femoral head center to talar center assessment. In addition, mechanical axis deviation, mechanical lateral distal femoral angle and medial proximal tibial angle (MPTA) were also calculated.

RESULTS

Mean patient age was 14.0±2.5 years. There were 162 (58.1%) females and mean BMI was 24.3±6.4. Seventy patients (25.1%) had bilateral PFI. Standing alignment radiographs were available for 81.4% of knees (n=284). Valgus alignment was present in 172 knees with PFI (60.6%). High-grade valgus, defined as zone 2 or greater, was present in 66 knees (23.3%). Overall, 48.9% had asymmetry of coronal alignment (n=139). The mean mechanical lateral distal femoral angle was 85.4±2.8 and the mean MPTA was 88.2±2.6. There was a greater MPTA in female patients (88.8±2.4 vs. 87.5±2.7, P <0.001). A higher BMI (24.87±6.95, P =0.03) was associated with valgus alignment.

CONCLUSIONS

There is a high (60%) prevalence of lower extremity valgus in adolescent patients presenting with PFI, with nearly 1 in 4 presenting with high-grade valgus. The treatment team should be aware of this association as it may be an important consideration in the pediatric and adolescent PFI populations.

LEVEL OF EVIDENCE

Level III.

Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

PURPOSE

The aim of this study was to investigate the performance of an artificial intelligence (AI)-based software for fully automated analysis of leg alignment pre- and postoperatively after high tibial osteotomy (HTO) on long-leg radiographs (LLRs).

METHODS

Long-leg radiographs of 95 patients with varus malalignment that underwent medial open-wedge HTO were analyzed pre- and postoperatively. Three investigators and an AI software using deep learning algorithms (LAMA™, ImageBiopsy Lab, Vienna, Austria) evaluated the hip-knee-ankle angle (HKA), mechanical axis deviation (MAD), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA). All measurements were performed twice and the performance of the AI software was compared with individual human readers using a Bayesian mixed model. In addition, the inter-observer intraclass correlation coefficient (ICC) for inter-observer reliability was evaluated by comparing measurements from manual readers. The intra-reader variability for manual measurements and the AI-based software was evaluated using the intra-observer ICC.

RESULTS

Initial varus malalignment was corrected to slight valgus alignment after HTO. Measured by the AI algorithm and manually HKA (5.36° ± 3.03° and 5.47° ± 2.90° to - 0.70 ± 2.34 and - 0.54 ± 2.31), MAD (19.38 mm ± 11.39 mm and 20.17 mm ± 10.99 mm to - 2.68 ± 8.75 and - 2.10 ± 8.61) and MPTA (86.29° ± 2.42° and 86.08° ± 2.34° to 91.6 ± 3.0 and 91.81 ± 2.54) changed significantly from pre- to postoperative, while JLCA and mLDFA were not altered. The fully automated AI-based analyses showed no significant differences for all measurements compared with manual reads neither in native preoperative radiographs nor postoperatively after HTO. Mean absolute differences between the AI-based software and mean manual observer measurements were 0.5° or less for all measurements. Inter-observer ICCs for manual measurements were good to excellent for all measurements, except for JLCA, which showed moderate inter-observer ICCs. Intra-observer ICCs for manual measurements were excellent for all measurements, except for JLCA and for MPTA postoperatively. For the AI-aided analyses, repeated measurements showed entirely consistent results for all measurements with an intra-observer ICC of 1.0.

CONCLUSIONS

The AI-based software can provide fully automated analyses of native long-leg radiographs in patients with varus malalignment and after HTO with great accuracy and reproducibility and could support clinical workflows.

LEVEL OF EVIDENCE

Diagnostic study, Level III.