Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia

Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study

Purpose

The study aims to identify differences in tibiofemoral joint morphology between responders (R group, no pain) to arthroscopic partial medial meniscectomy (APMM) versus medial postmeniscectomy syndrome patients (MPMS group, recurrent pain at 2 years postmeniscectomy) in a clinically neutrally aligned patient population. The second aim was to build a morphology-based predictive algorithm for response to treatment (RTT) in APMM.

Methods

Two patient groups were identified from a large multicentre database of meniscectomy patients at 2 years of follow-up: the R group included 120 patients with a KOOS pain score > 75, and the MPMS group included 120 patients with a KOOS pain score ≤ 75. Statistical shape models (SSMs) of distal femur, proximal tibia and tibiofemoral joint were used to compare knee morphology. Finally, a predictive model was developed to predict RTT, with the SSM-derived morphologic variables as predictors.

Results

No differences were found between the R and MPMS groups for patient age, sex, height, weight or cartilage status. Knees in the MPMS group were significantly smaller, had a wider femoral notch and a smaller medial femoral condyle. A morphology-based predictive model was able to predict MPMS at 2 years follow-up with a sensitivity of 74.9% (95% confidence interval [CI]: 74.4%-75.4%) and a specificity of 81.0% (95% CI: 80.6%-81.5%).

Conclusion

A smaller tibiofemoral joint, a wider intercondylar notch and smaller medial femoral condyle were observed shape variations related to medial postmeniscectomy syndrome. These promising results are a first step towards a knee morphology-based clinical decision support tool for meniscus treatment.

Study Design

Case-control study.

Level of Evidence

Level IIIb.

Inconsistent repeatability of the Dejour classification of trochlear dysplasia due to the variability of imaging modalities: a systematic review

PURPOSE

The purpose of this systematic review was to critically assess the quality of papers that report on the intra- and inter-observer repeatability of the Dejour classification for trochlear dysplasia, and to identify the possible causes for poor repeatability.

METHODS

Two authors independently conducted an electronic search (four databases) on 8 February 2023 for studies (English or French) that assessed trochlear dysplasia classifications on imaging of skeletally mature participants. Exclusion criteria were reviews of clinical studies, conference proceedings, or editorials. After title, abstract, and full-text screening, characteristics of eligible studies were tabulated (author, year, journal, study design, cohort characteristics, and intra- and/or inter-observer agreement coefficients). The methodological quality of studies was assessed using the Joanna Briggs Institute (JBI) checklist for analytical cross-sectional studies. Authors analysed three components of the included studies: (1) classifications based on true lateral radiographs and slice imaging; (2) dysplasia graded into Type A vs B vs C vs D and 3) coefficients of intra- and/or inter-observer agreement.

RESULTS

The electronic search returned 3,178 references, and after removal of duplicates and irrelevant studies, ten were eligible for data extraction. A second search (31 July 2023) yielded one additional study. Eight studies did not include lateral radiographs, two studies did not explicitly state if radiographs were true lateral views, and one used true lateral radiographs in isolation. Classification of trochlear dysplasia into A vs B vs C vs D using different imaging modalities resulted in moderate to near-perfect intra-observer agreement, and slight to near-perfect inter-observer agreement. Studies distinguished between moderate and severe dysplasia using a variety of combinations: A vs B/C/D, A/B vs C/D and A/C vs B/D.

CONCLUSION

This systematic review revealed that the Dejour classification remains the most widely used to assess trochlear dysplasia and that the majority of studies that assessed the reliability of the Dejour classification, reported moderate to near-perfect inter-observer agreement; however, pooling of results for comparison among the included studies was inappropriate due to substantial variation in imaging protocols and non-standardised criteria to distinguish severe from moderate dysplasia.

LEVEL OF EVIDENCE

Level IV.

TRIAL REGISTRY

The PROSPERO registration number is CRD42023386731.

Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements

BACKGROUND

Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging (MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.

AIM

To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.

METHODS

We searched 464 knee MRI cases between January 2019 and December 2020, including FTD (n = 202) and normal trochlea (n = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, etc.) were calculated.

RESULTS

The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96. All values were superior to junior doctors and intermediate doctors, similar to senior doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.

CONCLUSION

The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.

Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning

Currently, both manual and automatic evaluation technology can evaluate the translation quality of unreferenced English articles, playing a particular role in detecting translation results. Still, their deficiency is the lack of a close or noticeable relationship between evaluation time and evaluation theory. Thereupon, to realize the automatic Translation Quality Assessment (TQA) of unreferenced English articles, this paper proposes an automatic TQA model based on Sparse AutoEncoder (SAE) under the background of Deep Learning (DL). Meanwhile, the DL-based information extraction method employs AutoEncoder (AE) in the bilingual words’ unsupervised learning stage to reconstruct the translation language vector features. Then, it imports the translation information of unreferenced English articles into Bilingual words and optimizes the extraction effect of language vector features. Meantime, the translation language vector feature is introduced into the automatic DL-based TQA. The experimental findings corroborate that when the number of sentences increases, the number of actual translation errors and the evaluation scores of the proposed model increase, but the Bilingual Evaluation Understudy (BLEU) score is not significantly affected. When the number of sentences increases from 1,000 to 6,000, the BLEU increases from 96 to 98, which shows that the proposed model has good performance. Finally, the proposed model can realize the high-precision TQA of unreferenced English articles.

EquiSim: An Open-Source Articulatable Statistical Model of the Equine Distal Limb

Most digital models of the equine distal limb that are available in the community are static and/or subject specific; hence, they have limited applications in veterinary research. In this paper, we present an articulatable model of the entire equine distal limb based on statistical shape modeling. The model describes the inter-subject variability in bone geometry while maintaining proper jointspace distances to support model articulation toward different poses. Shape variation modes are explained in terms of common biometrics in order to ease model interpretation from a veterinary point of view. The model is publicly available through a graphical user interface (https://github.com/jvhoutte/equisim) in order to facilitate future digitalization in veterinary research, such as computer-aided designs, three-dimensional printing of bone implants, bone fracture risk assessment through finite element methods, and data registration and segmentation problems for clinical practices.

A landmark-based 3D analysis reveals a narrower tibial plateau and patella in trochlear dysplastic knees

PURPOSE

The trochlear dysplastic femur has a specific morphotype previously characterised by not only dysplastic features of the trochlea but also by specific features of the notch and posterior femur. In this study the morphology of the tibia and patella was investigated to gain further insight in the complete geometrical complexity of the trochlear dysplastic knee.

METHODS

Arthro-CT scan-based 3D models of 20 trochlear dysplastic and 20 normal knees were uniformly scaled and landmarks and landmark-based reference planes were created to quantify a series of morphometric characteristics of the tibia and patella.

RESULTS

In the mediolateral direction, the 3D-analysis revealed a 3% smaller medial tibial plateau (30.4 ± 1.6 mm vs 31.5 ± 1.6 mm), a 3% smaller overall width of the tibial plateau (73.6 ± 2.0 mm vs 75.7 ± 2.0 mm), a 16% smaller medial facet (17.3 ± 2.2 mm vs 20.1 ± 1.3 mm) and a 4% smaller overall width of the patella (41.7 ± 2.5 mm vs 43.4 ± 2.3 mm) in trochlear dysplastic knees. In the anteroposterior direction, the lateral tibial plateau of trochlear dysplastic knees was 5% larger (37.2 ± 2.3 mm vs 35.5 ± 3.1 mm). A correlation test between the width of the femur and the width of the tibia revealed that trochlear dysplastic knees show less correspondence between the femur and tibia compared to normal knees.

CONCLUSION

Significant differences in the morphology of the tibial plateau and patella were detected between trochlear dysplastic and normal knees. Both in the trochlear dysplastic tibial plateau and patella a narrower medial compartment leads to a significant smaller overall mediolateral width. These findings are important for the understanding of knee biomechanics and the design of total knee arthroplasty components.

LEVEL OF EVIDENCE

III.

Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called "modes of variation" (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p = 0.02) in correspondence to IER and MoV #5 in correspondence to HKA (p = 0.0001), FVV (p = 0.001), and TS (p = 0.02). We can argue that the SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict potential misalignment in the knee for a new patient by processing the bone shapes, removing the need for measuring clinical landmarks as the rotation centers and mechanical axes.

Autoencoder-Based Detection of Dynamic Allostery Triggered by Ligand Binding Based on Molecular Dynamics

Dynamic allostery on proteins, triggered by regulator binding or chemical modifications, transmits information from the binding site to distant regions, dramatically altering protein function. It is accompanied by subtle changes in side-chain conformations of the protein, indicating that the changes in dynamics, and not rigid or large conformational changes, are essential to understand regulation of protein function. Although a lot of experimental and theoretical studies have been dedicated to investigate this issue, the regulation mechanism of protein function is still being debated. Here, we propose an autoencoder-based method that can detect dynamic allostery. The method is based on the comparison of time fluctuations of protein structures, in the form of distance matrices, obtained from molecular dynamics simulations in ligand-bound and -unbound forms. Our method detected that the changes in dynamics by ligand binding in the PDZ2 domain led to the reorganization of correlative fluctuation motions among residue pairs, which revealed a different view of the correlated motions from the PCA and DCCM. In addition, other correlative motions were also found as a result of the dynamic perturbation from the ligand binding, which may lead to dynamic allostery. This autoencoder-based method would be usefully applied to the signal transduction and mutagenesis systems involved in protein functions and severe diseases.

Pair-wise vs group-wise registration in statistical shape model construction: representation of physiological and pathological variability of bony surface morphology

Statistical shape models (SSM) of bony surfaces have been widely proposed in orthopedics, especially for anatomical bone modeling, joint kinematic analysis, staging of morphological abnormality, and pre- and intra-operative shape reconstruction. In the SSM computation, reference shape selection, shape registration and point correspondence computation are fundamental aspects determining the quality (generality, specificity and compactness) of the SSM. Such procedures can be made critical by the presence of large morphological dissimilarities within the surfaces, not only because of anthropometrical variability but also mainly due to pathological abnormalities. In this work, we proposed a SW pipeline for SSM construction based on pair-wise (PW) shape registration, which requires the a-priori selection of the reference shape, and on a custom iterative point correspondence algorithm. We addressed large morphological deformations in five different bony surface sets, namely proximal femur, distal femur, patella, proximal fibula and proximal tibia, extracted from a retrospective patient dataset. The technique was compared to a method from the literature, based on group-wise (GW) shape registration. As a main finding, the proposed technique provided generalization and specificity median errors, for all the five bony regions, lower than 2 mm. The comparative analysis provided basically similar results. Particularly, for the distal femur that was the shape affected by the largest pathological deformations, the differences in generalization, specificity and compactness were lower than 0.5 mm, 0.5 mm, and 1%, respectively. We can argue the proposed pipeline, along with the robust correspondence algorithm, is able to compute high-quality SSM of bony shapes, even affected by large morphological variability.