Abstract. Introduction. Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA), a degenerative joint disease affecting over 300 million people worldwide. Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development. Objectives. This study aims to create a trustworthy and interpretable precision health tool that predicts rapid knee OA progression based on baseline patient characteristics using an advanced automated machine learning (autoML) framework, “Autoprognosis 2.0”. Methods. All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class
Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA). Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development. To create a reliable and interpretable precision health tool that predicts rapid knee OA progression over a 2-year period from baseline patient characteristics using an advanced automated machine learning (autoML) framework, “Autoprognosis 2.0”. All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class
Total knee and hip arthroplasty (TKA and THA) are the most commonly performed surgical procedures, the costs of which constitute a significant healthcare burden. Improving access to care for THA/TKA requires better efficiency. It is hypothesized that this may be possible through a two-stage approach that utilizes
Background. Although there are predictive equations that estimate the total fat mass obtained from multiple-site ultrasound (US) measurements, the predictive equation of total fat mass has not been investigated solely from abdominal subcutaneous fat thickness. Therefore, the aims of this study were; (1) to develop regression-based
Although 3D-printed porous dental implants may possess improved osseointegration potential, they must exhibit appropriate fatigue strength. Finite element analysis (FEA) has the potential to predict the fatigue life of implants and accelerate their development. This work aimed at developing and validating an FEA-based tool to predict the fatigue behavior of porous dental implants. Test samples mimicking dental implants were designed as 4.5 mm-diameter cylinders with a fully porous section around bone level. Three porosity levels (50%, 60% and 70%) and two unit cell types (Schwarz Primitive (SP) and Schwarz W (SW)) were combined to generate six designs that were split between calibration (60SP, 70SP, 60SW, 70SW) and validation (50SP, 50SW) sets. Twenty-eight samples per design were additively manufactured from titanium powder (Ti6Al4V). The samples were tested under bending compression loading (ISO 14801) monotonically (N=4/design) to determine ultimate load (F. ult. ) (Instron 5866) and cyclically at six load levels between 50% and 10% of F. ult. (N=4/design/load level) (DYNA5dent). Failure force results were fitted to F/F. ult. = a(N. f. ). b. (Eq1) with N. f. being the number of cycles to failure, to identify parameters a and b. The endurance limit (F. e. ) was evaluated at N. f. = 5M cycles. Finite element models were built to predict the yield load (F. yield. ) of each design. Combining a linear correlation between FEA-based F. yield. and experimental F. ult. with equation Eq1 enabled FEA-based
Background. There is a 20% dissatisfaction rate with knee replacements. Calls for tools that can pre-operatively identify patients at risk of being dissatisfied postoperatively have been widespread. However, it is unclear what sort of information patients would want from such a tool, how it would affect their decision making process, and at what part of the pathway such a tool should be used. Methods. Using focus groups involving 12 participants and in-depth interviews with 10 participants, we examined the effect outcome
During OA the homeostasis of healthy articular chondrocytes is dysregulated, which leads to a phenotypical transition of the cells, further influenced by external stimuli. Chondrocytes sense those stimuli, integrate them at the intracellular level and respond by modifying their secretory and molecular state. This process is controlled by a complex interplay of intracellular factors. Each factor is influenced by a myriad of feedback mechanisms, making the
Young patients receiving metallic bone implants after surgical resection of bone cancer require implants that last into adulthood, and ideally life-long. Porous implants with similar stiffness to bone can promote bone ingrowth and thus beneficial clinical outcomes. A mechanical remodelling stimulus, strain energy density (SED), is thought to be the primary control variable of the process of bone growth into porous implants. The sequential process of bone growth needs to be taken into account to develop an accurate and validated bone remodelling algorithm, which can be employed to improve porous implant design and achieve better clinical outcomes. A bone remodelling algorithm was developed, incorporating the concept of bone connectivity (sequential growth of bone from existing bone) to make the algorithm more physiologically relevant. The algorithm includes adaptive elastic modulus based on apparent bone density, using a node-based model to simulate local remodelling variations while alleviating numerical checkerboard problems. Strain energy density (SED) incorporating stress and strain effects in all directions was used as the primary stimulus for bone remodelling. The simulations were developed to run in MATLAB interfacing with the commercial FEA software ABAQUS and Python. The algorithm was applied to predict bone ingrowth into a porous implant for comparison against data from a sheep model.Abstract
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Quantitative ultrasound (QUS) is a promising tool to estimate bone structure characteristics and predict fragile fracture. The aim of this pilot cross-sectional study was to evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragile fractures retrospectively in postmenopausal women. RF signal and speed of sound (SOS) were obtained using an axial transmission QUS at one‐third distal radius for 246 postmenopausal women. Based on the involved RF signal, we conducted a MResNet, which combines multi-channel training with original ResNet, to classify the high risk of fragility fractures patients from all subjects. The bone mineral density (BMD) at lumber, hip and femoral neck acquired with DXA was recorded on the same day. The fracture history of all subjects in adulthood were collected. To assess the ability of the different methods in the discrimination of fragile fracture, the odds ratios (OR) calculated using binomial logistic regression analysis and the area under the receiver operator characteristic curves (AUC) were analyzed. Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was discriminant for all fragile fractures (OR = 2.64; AUC = 0.74), for Vertebral fracture (OR = 3.02; AUC = 0.77), for non-vertebral fracture (OR = 2.01; AUC = 0.69). MResNet showed comparable performance to that of BMD of hip and lumbar with all types of fractures, and significantly better performance than SOS all types of fractures.Methods
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A damaged vertebral body can exhibit accelerated ‘creep’ under constant load, leading to progressive vertebral deformity. However, the risk of this happening is not easy to predict in clinical practice. The present cadaveric study aimed to identify morphometric measurements in a damaged vertebral body that can predict a susceptibility to accelerated creep. Mechanical testing of 28 human spinal motion segments (three vertebrae and intervening soft tissues) showed how the rate of creep of a damaged vertebral body increases with increasing “damage intensity” in its trabecular bone. Damage intensity was calculated from vertebral body residual strain following initial compressive overload. The calculations used additional data from 27 small samples of vertebral trabecular bone, which examined the relationship between trabecular bone damage intensity and residual strain.Abstract
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The ability to predict which patients will improve following routine surgeries aimed at preventing the progression of osteoarthritis is needed to aid patients being stratified to receive the most appropriate treatment. This study aimed to investigate the potential of a panel of biomarkers for predicting (prior to treatment) the clinical outcome following treatment with microfracture or osteotomy. Proteins known to relate to OA severity, with predictive value in autologous cell implantation treatment or that had been identified in proteomic analyses (aggrecanase-1/ ADAMTS-4, cartilage oligomeric matrix protein (COMP), hyaluronic acid (HA), Lymphatic Vessel Endothelial Hyaluronan Receptor-1, matrix metalloproteinases-1 and −3, soluble CD14, S100 calcium binding protein A13 and 14-3-3 protein theta) were assessed in the synovial fluid (SF) of 19 and 13 patients prior to microfracture or osteotomy, respectively, using commercial immunoassays. Levels of COMP and HA were measured in the plasma of these patients. To find predictors of postoperative function, multiple linear regression analyses were performed.Abstract
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Neonatal motor development transitions from initially spontaneous to later increasingly complex voluntary movements. A delay in transitioning may indicate cerebral palsy (CP). The general movement optimality score (GMOS) evaluates infant movement variety and is used to diagnose CP, but depends on specialized physiotherapists, is time-consuming, and is subject to inter-observer differences. We hypothesised that an objective means of quantifying movements in young infants using motion tracking data may provide a more consistent early diagnosis of CP and reduce the burden on healthcare systems. This study assessed lower limb kinematic and muscle force variances during neonatal infant kicking movements, and determined that movement variances were associated with GMOS scores, and therefore CP. Electromagnetic motion tracking data (Polhemus) was collected from neonatal infants performing kicking movements (min 50° knee extension-flexion, <2 seconds) in the supine position over 7 minutes. Tracking data from lower limb anatomical landmarks (midfoot inferior, lateral malleolus, lateral knee epicondyle, ASIS, sacrum) were applied to subject-scaled musculoskeletal models (Gait2354_simbody, OpenSim). Inverse kinematics and static optimisation were applied to estimate lower limb kinematics (knee flexion, hip flexion, hip adduction) and muscle forces (quadriceps femoris, biceps femoris) for isolated kicks. Functional principal component analysis (fPCA) was carried out to reduce kicking kinematic and muscle force waveforms to PC scores capturing ‘modes’ of variance. GMOS scores (lower scores = reduced variety of movement) were collected in parallel with motion capture by a trained operator and specialised physiotherapist. Pearson's correlations were performed to assess if the standard deviation (SD) of kinematic and muscle force waveform PC scores, representing the intra-subject variance of movement or muscle activation, were associated with the GMOS scores.Abstract
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The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man. In this comparative study we implemented parametric and flexible parametric (FP) methods and random survival forests (RSF). The overall performance of the parametric models was compared using Akaike information criterion (AIC). The preferred parametric model and the RSF algorithm were further compared in terms of the Brier score, concordance index (C index) and calibration. The dataset contains 327 238 hip replacements for osteoarthritis carried out in England and Wales between 2003 and 2015. The AIC value for the FP model was the lowest. The averages of survival probability estimates were in good agreement with the observed values for the FP model and the RSF algorithm. The integrated Brier score of the FP model and the RSF approach over 10 years were similar: 0.011 (95% confidence interval: 0.011–0.011). The C index of the FP model at 10 years was 59.4% (95% confidence interval: 59.4%–59.4%). This was 56.2% (56.1%–56.3%) for the RSF method. The FP model outperformed other commonly used survival models across chosen validation criteria. However, it does not provide high discriminatory power at the individual level. Models with more comprehensive risk adjustment may provide additional insights for individual risk.
The management of displaced forearm diaphyseal fractures in adults is predominantly operative. Anatomical reduction is necessary to infer optimal motion and strength. The authors have observed an intraoperative technique where passive pronosupination is examined to assess quality of reduction as a surrogate marker for active movement. We aimed to assess the value of this technique, but intentionally malreducing a simulated diaphyseal fracture of a radius in a cadaveric model, and measuring the effect on pronosupination. A single cadaveric arm was prepared and pronation/supination was examined according to American Academy of Orthopaedic Surgeons guidance. A Henry approach was then performed and a transverse osteotomy achieved in the radial diaphysis. A volar locking plate was used to hold the radius in progressive amounts of translation and rotation, with pronosupaintion measured with a goniometer. The radius could be grossly malreduced with no effect on pronation and supination until the extremes of deformity. The forearm showed more tolerance with rotational malreduction than translation. Passive pronation was more sensitive for malreduction than supination. The use of passive pronosupination to assess quality of reduction is misleading.
We have evaluated the circulation of the femoral head after multiple pinning for femoral neck fractures by bone SPECT. Forty-four patients (33 women, 11 men, who had a mean age of 67 years) were enrolled prospectively. Early and late bone SPECT images were obtained on 2 to 13 days and 3 months after surgery and follow-up periods were over 12 months (average, 29 months).Introduction
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Abstract. Objectives. Knee alignment affects both the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ankle angle (HKA) measurement from radiographs could improve reliability and save time. Further, if the gold-standard HKA from full-limb radiographs could be accurately predicted from knee-only radiographs then the need for more expensive equipment and radiation exposure could be reduced. The aim of this research is to assess if deep learning methods can predict FTA and HKA angle from posteroanterior (PA) knee radiographs. Methods. Convolutional neural networks with densely connected final layers were trained to analyse PA knee radiographs from the Osteoarthritis Initiative (OAI) database with corresponding angle measurements. The FTA dataset with 6149 radiographs and HKA dataset with 2351 radiographs were split into training, validation and test datasets in a 70:15:15 ratio. Separate models were learnt for the
Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the
Currently implemented accuracy metrics in open-source libraries for segmentation by supervised machine learning are typically one-dimensional scores [1]. While extremely relevant to evaluate applicability in clinics, anatomical location of segmentation errors is often neglected. This study aims to include the three-dimensional (3D) spatial information in the development of a novel framework for segmentation accuracy evaluation and comparison between different methods. Predicted and ground truth (manually segmented) segmentation masks are meshed into 3D surfaces. A template mesh of the same anatomical structure is then registered to all ground truth 3D surfaces. This ensures all surface points on the ground truth meshes to be in the same anatomically homologous order. Next, point-wise surface deviations between the registered ground truth mesh and the meshed segmentation
The lateral wall thickness (LWT) in trochanteric femoral fractures is a known predictive factor for postoperative fracture stability. Currently, the AO/OTA classification uses a patient non-specific measure to assess the absolute LWT (aLWT) and distinguish stable A1.3 from unstable A2.1 fractures based on a threshold of 20.5 mm. This approach potentially results in interpatient deviations due to different bone morphologies and consequently variations in fracture stability. Therefore, the aim of this study was to explore whether a patient-specific measure for assessment of the relative LWT (rLWT) results in a more precise threshold for
The primary aim was to assess the reliability of ultrasound in the assessment of humeral shaft fracture healing. The secondary aim was to estimate the accuracy of ultrasound assessment in predicting humeral shaft nonunion. Twelve patients (mean age 54yrs [20–81], 58% [n=7/12] female) with a non-operatively managed humeral diaphyseal fracture were prospectively recruited and underwent ultrasound scanning at six and 12wks post-injury. Scans were reviewed by seven blinded observers to evaluate the presence of sonographic callus. Intra- and inter-observer reliability were determined using the weighted kappa and intraclass correlation coefficient (ICC). Accuracy of ultrasound assessment in nonunion