Use of Instant Messaging Applications by General Practitioners During Overseas Deployment: A Survey of the French Military Health Service.

INTRODUCTION: Instant messaging applications (MAs) represent a major component of modern telecommunications for data transmission. During overseas deployments, military doctors increasingly rely on MAs due to their availability and the urgent need to obtain advice from specialists for optimal patient management. In this study, we aimed to describe and analyze the context and usage characteristics of these MAs for transmitting medical data by military general practitioners (GPs) during overseas missions. MATERIALS AND METHODS: This observational study was conducted between June 2020 and December 2020, based on a survey sent to GPs from the French Military Health Service who had been deployed overseas in military operations between 2010 and 2020. RESULTS: We received 233 surveys of which 215 were analyzed. Among these, 141 military GPs used instant MAs to transmit medical data during deployment. Notably, WhatsApp was used by 97% of the participants. The military GPs mainly used these applications for the speed of exchanges (45%) and their ease of use (28%). The physician specialties predominantly involved in data sharing were trauma and orthopedic surgery (38%) and dermatology (31%). The correspondents were mainly military specialist physicians from French military teaching hospitals (85%). A response time of less than 1 h was reported in 78% of the cases. Additionally, 72 doctors (51%) undertook their last deployment in an isolated post. CONCLUSION: MAs were extensively utilized communication tools among GPs during their overseas deployments. Although the use of these applications seems essential in telemedicine, it raises several legal and ethical questions. Thus, we recommend employing these tools while ensuring medical and military confidentiality.

Assessment and comparison of image quality between two real-time sequences for dynamic MRI of distal joints at 3.0 Tesla.

BACKGROUND: Real-time sequences allow functional evaluation of various joint structures during a continuous motion and help understand the pathomechanics of underlying musculoskeletal diseases. PURPOSE: To assess and compare the image quality of the two most frequently used real-time sequences for joint dynamic magnetic resonance imaging (MRI), acquired during finger and ankle joint motion. MATERIAL AND METHODS: A real-time dynamic acquisition protocol, including radiofrequency (RF)-spoiled and balanced steady-state free precession (bSSFP) sequences, optimized for temporal resolution with similar spatial resolution, was performed using a 3.0-T MRI scanner on 10 fingers and 12 ankles from healthy individuals during active motion. Image quality criteria were evaluated on each time frame and compared between these two sequences. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were determined and compared from regions of interest placed on cortical bone, tendon, fat, and muscle. Visualization of anatomical structures and overall image quality appreciation were rated by two radiologists using a 0-10 grading scale. RESULTS: Mean CNR was significantly higher with bSSFP sequence compared to RF-spoiled sequence. The grading score was in the range of 5-9.3 and was significantly higher with RF-spoiled sequence for bone and joint evaluation and overall image appreciation on the two joints. The standard deviation for SNR, CNR, and grading score during motion was smaller with RF-spoiled sequence for both the joints. The inter-reader reliability was excellent (>0.75) for evaluating anatomical structures in both sequences. CONCLUSION: A RF-spoiled real-time sequence is recommended for the in vivo clinical evaluation of distal joints on a 3.0-T MRI scanner.

Dynamic multi feature-class Gaussian process models.

In model-based medical image analysis, three relevant features are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical properties. Often, these features are modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. However, analysing articulated objects in an image using independent single object models may lead to large uncertainties and impingement, especially around organ boundaries. Questions that come to mind are the feasibility of building a unique model that combines all three features of interest in the same statistical space, and what advantages can be gained for image analysis. This study presents a statistical modelling method for automatic analysis of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). The DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variations. Our method is defined in a continuous domain with a principled way to represent shape, pose and intensity feature-classes in a linear space, based on deformation fields. A deformation field-based metric is adapted in the method for modelling shape and intensity variation as well as for comparing rigid transformations (pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including marginalisation and regression. Furthermore, they allow for adding additional pose variability on top of those obtained from the image acquisition process; what we term as permutation modelling. For image analysis tasks using DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of features fully probabilistic. We validate the method using controlled synthetic data and we perform experiments on bone structures from CT images of the shoulder to illustrate the efficacy of the model for pose and shape prediction. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications in a multitude of scenarios including the management of musculoskeletal disorders, clinical decision making and image processing.

Comprehensive personalized ankle joint shape analysis of children with cerebral palsy from pediatric MRI.

Cerebral palsy, a common physical disability in childhood, often causes abnormal patterns of movement and posture. To better understand the pathology and improve rehabilitation of patients, a comprehensive bone shape analysis approach is proposed in this article. First, a group analysis is performed on a clinical MRI dataset using two state-of-the-art shape analysis methods: ShapeWorks and a voxel-based method relying on Advanced Normalization Tools (ANTs) registration. Second, an analysis of three bones of the ankle is done to provide a complete view of the ankle joint. Third, a bone shape analysis is carried out at subject level to highlight variability patterns for personnalized understanding of deformities.

Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network.

Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. To this end, we have conceived a novel optimization scheme for the segmentation network which comprises additional regularization terms to the loss function. In order to obtain globally consistent predictions, we incorporate a shape priors based regularization, derived from a non-linear shape representation learnt by an auto-encoder. Additionally, an adversarial regularization computed by a discriminator is integrated to encourage precise delineations. The proposed method is evaluated for the task of multi-bone segmentation on two scarce pediatric imaging datasets from ankle and shoulder joints, comprising pathological as well as healthy examinations. The proposed method performed either better or at par with previously proposed approaches for Dice, sensitivity, specificity, maximum symmetric surface distance, average symmetric surface distance, and relative absolute volume difference metrics. We illustrate that the proposed approach can be easily integrated into various bone segmentation strategies and can improve the prediction accuracy of models pre-trained on large non-medical images databases. The obtained results bring new perspectives for the management of pediatric musculoskeletal disorders.

Cross-Modality Image Adaptation Based on Volumetric Intensity Gaussian Process Models (VIGPM).

Image-based diagnosis routinely depends on more that one image modality for exploiting the complementary information they provide. However, it is not always possible to obtain images from a secondary modality for several reasons such as cost, degree of invasiveness and non-availability of scanners. Three-dimensional (3D) morphable models have made a significant contribution to the field of medical imaging for feature-based analysis. Here we extend their use to encode 3D volumetric imaging modalities. Specifically, we build a Gaussian Process (GP) over transformations establishing anatomical correspondence between training images within a modality. Given, two different modalities, the GP's eigenspace (latent space) can then be used to provide a parametric representation of each image modality, and we provide an operator for cross-domain translation between the two. We show that the latent space yields samples that are representative of the encoded modality. We also demonstrate that a 3D volumetric image can be efficiently encoded in latent space and transferred to synthesize the corresponding image in another modality. The framework called VIGPM can be extended by designing a fitting process to learn an observation in a given modality and performing cross-modality synthesis. Clinical Relevance- The proposed method provides a way to access a multi modality image from one modality. Both the source and synthetic modalities are in anatomical correspondence giving access to registered complementary information.

Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors.

Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations. In the medical image processing pipeline, semantic segmentation using deep learning algorithms enables an automatic generation of patient-specific three-dimensional anatomical models which are crucial for morphological evaluation. However, the scarcity of pediatric imaging resources may result in reduced accuracy and generalization performance of individual deep segmentation models. In this study, we propose to design a novel multi-task, multi-domain learning framework in which a single segmentation network is optimized over the union of multiple datasets arising from distinct parts of the anatomy. Unlike previous approaches, we simultaneously consider multiple intensity domains and segmentation tasks to overcome the inherent scarcity of pediatric data while leveraging shared features between imaging datasets. To further improve generalization capabilities, we employ a transfer learning scheme from natural image classification, along with a multi-scale contrastive regularization aimed at promoting domain-specific clusters in the shared representations, and multi-joint anatomical priors to enforce anatomically consistent predictions. We evaluate our contributions for performing bone segmentation using three scarce and pediatric imaging datasets of the ankle, knee, and shoulder joints. Our results demonstrate that the proposed approach outperforms individual, transfer, and shared segmentation schemes in Dice metric with statistically sufficient margins. The proposed model brings new perspectives towards intelligent use of imaging resources and better management of pediatric musculoskeletal disorders.

Optimal definitions for computing HKA angle in caos: an in-vitro comparison study.

The goal of this study was to assess and compare the precision and accuracy of nine and seven methods usually used in Computer Assisted Orthopedic Surgery (CAOS) to estimate respectively the Knee Center (KC) and the Frontal Plane (FP) for the determination of the HKA angle (HKAA). An in-vitro experiment has been realized on thirteen cadaveric lower limbs. A CAOS software application was developed and allowed the computation of the HKAA according to these nine KC and seven FP methods. The precision and the accuracy of the HKAA measurements were measured. The HKAA precision was highest when the FP is determined using the helical method. The HKAA accuracy was highest using the helical approach to determine the FP and either the notch or the tibial spines to determine the KC. This study shows that the helical approach to determine the FP and either the notch or the middle of tibia spines are the combinations that provide both a good enough accuracy and precision to estimate the HKA.

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry Through Statistical Learning.

OBJECTIVE: Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. METHODS: The proposed anatomically parameterized SSM (ANAT[Formula: see text]) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical space). This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT[Formula: see text]. We further impose orthogonality constraints to the anatomical parameterization (OC-ANAT[Formula: see text]) to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters within each (five for femoral and six for scapular bone). RESULTS: Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. CONCLUSION: This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs. SIGNIFICANCE: The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment.

A Musculoskeletal Model Customized for Sagittal and Frontal Knee Kinematics With Improved Knee Joint Stability.

Current lower limb musculoskeletal (MSK) models focus on sagittal plane kinematics. However, abnormal gait is typically associated with sagittal plane motions crossing into other planes, limiting the use of current MSK models. The purpose of this study was twofold, first, to extend the capability of a full-body MSK model from the literature to include frontal knee plane kinematics during healthy gait, and second, to propose and implement a realistic muscle discretization technique. Two MSK model constructs were derived-the first construct (Knee2_SM) allowed two degrees-of-freedom (sagittal and coronal) at the knee and the second construct (Knee2_MM) implemented multiline elements for all the lower limb muscles in conjunction with two knee degrees-of-freedom. Motion analysis data of normal gait cycle from 10 healthy adults were used to compare joint kinematics, muscle moment arms, muscle forces, and muscle activations, between new constructs and the original model. Knee varus-valgus trajectories were estimated with the mean peak values ranging from 9.49 deg valgus to 1.57 deg varus. Knee2_MM predicted a significant difference (p < 0.05) in moment arms and forces in those muscles responsible for medial-lateral stability of the knee. The simulated muscle activations generated by the Knee2_MM model matched more closely to the experimental electromyography (EMG) when qualitatively compared. This study enhances the capability of the sagittal plane full-body MSK model to incorporate knee varus-valgus motion while keeping the joint stability intact and improving muscle prediction.

Ultrasound Ulnar Nerve Measurement in a Healthy Population.

INTRODUCTION: Ulnar tunnel syndrome at the elbow is a common pathology. The ultrasound cross-sectional area is a well-known metric widely accepted in radiology for the description of nerve entrapment. However, the pathological cut-off value remains challenging. The objectives of this study were to (1) describe the ultrasound cross-sectional area measurement of the ulnar nerve at three locations, and (2) to evaluate the inter-observer reliability by two independent ultrasonographers. METHODS: One-hundred ulnar nerves of 50 asymptomatic individuals were scanned using B-mode and power Doppler ultrasonography. The ultrasound cross-sectional area measurements of the ulnar nerve were performed at three different levels: 2 cm proximal to the epicondyle, at the level of the epicondyle, and 2 cm distal to the epicondyle. RESULTS: In our healthy population, we found 21, 24 and 7% of ultrasound cross-sectional area ulnar nerve > 8 mm2, respectively, at three different levels of measurement and 4, 7, and 0% US-CSA ulnar nerve > 10 mm2. The intraclass correlation coefficient measured at three different site levels were good (0.7943, 0.7509) to moderate (0.5701). CONCLUSIONS: Almost one-quarter of our healthy population had an ultrasound cross-sectional area ulnar nerve more than 8 mm2 and few more than 10 mm2. A cut-off of ultrasound cross-sectional area ulnar nerve measurement more than 10 mm2 could be considered as pathological. No abnormal elbow ulnar nerve vascularization has been seen. This is the first step towards normal B-mode ulnar nerve values at the elbow to further detect pathological US findings as ulnar nerve entrapment.

3-D lower extremity bone morphology in ambulant children with cerebral palsy and its relation to gait.

Changes in lower-extremity bone morphology are potential mid- to long-term secondary consequences of cerebral palsy (CP), affecting activity. Little is known about the 3-D morphology of lower-extremity bones in children with CP and the association with gait deviations. The main aim of this study was to describe and compare 3-D lower-extremity bone morphology in ambulant children with unilateral or bilateral CP. Secondary aims were to determine whether certain bone parameters were related to the unilateral or bilateral CP and to quantify the association between bone parameters and gait deviations. Among 105 ambulant children with CP (aged 3 to 17 years), 48 had bilateral CP (Bilat-CP) and 57 had unilateral CP (Unilat-CP); the unaffected limb of children with Unilat-CP was used as control limbs. Fifteen bone parameters were calculated by EOS® biplanar radiography, and the Gait Deviation Index (GDI) was calculated by 3-D gait analysis. Data were compared by descriptive and comparative statistical analysis (Anova, principal component analysis [PCA] and focused-PCA). Mean (SD) neck shaft angle was significantly greater for Unilat-CP than control limbs (134.9° [5.9] vs. 131.3° [5]). Mean mechanical tibial angle was significantly smaller (85.8° [6.7] vs. 89° [4.6]) and mean femoral torsion was significantly greater (29.4° [1.6] vs. 19.1° [11.8]) for Bilat-CP than control limbs. On PCA of the main determinants of 3-D bone morphology, bone shape was more complex with Bilat-CP, with changes in all 3 dimensions of space, than Unilat-CP and control limbs. Few bone parameters were correlated with the GDI in any limbs. In ambulant children with CP, femoral and tibial growth are not affected by the condition. The unilateral or bilateral nature of CP must be considered during treatment to prevent bone deformities and bone morphology affecting gait quality.

Dynamic MRI for articulating joint evaluation on 1.5 T and 3.0 T scanners: setup, protocols, and real-time sequences.

Dynamic magnetic resonance imaging (MRI) is a non-invasive method that can be used to increase the understanding of the pathomechanics of joints. Various types of real-time gradient echo sequences used for dynamic MRI acquisition of joints include balanced steady-state free precession sequence, radiofrequency-spoiled sequence, and ultra-fast gradient echo sequence. Due to their short repetition time and echo time, these sequences provide high temporal resolution, a good signal-to-noise ratio and spatial resolution, and soft tissue contrast. The prerequisites of the evaluation of joints with dynamic MRI include suitable patient installation and optimal positioning of the joint in the coil to allow joint movement, sometimes with dedicated coil support. There are currently few recommendations in the literature regarding appropriate protocol, sequence standardizations, and diagnostic criteria for the use of real-time dynamic MRI to evaluate joints. This article summarizes the technical parameters of these sequences from various manufacturers on 1.5 T and 3.0 T MRI scanners. We have reviewed pertinent details of the patient and coil positioning for dynamic MRI of various joints. The indications and limitations of dynamic MRI of joints are discussed.

Clinical relevance of augmented statistical shape model of the scapula in the glenoid region.

OBJECTIVE: To illustrate (a) whether a statistical shape model (SSM) augmented with anatomical landmark set(s) performs better fitting and provides improved clinical relevance over non-augmented SSM and (b) which anatomical landmark set provides the best augmentation strategy for predicting the glenoid region of the scapula. METHODS: Scapula SSM was built using 27 dry bone CT scans and augmented with three anatomical landmark sets (16 landmarks each) resulting in three augmented SSMs (aSSMproposed, aSSMset1, aSSMset2). The non-augmented and three augmented SSMs were then used in a non-rigid registration (regression) algorithm to fit to six external scapular shapes. The prediction error by each type of SSM was evaluated in the glenoid region for the goodness of fit (mean error, root mean square error, Hausdorff distance and Dice similarity coefficient) and for four anatomical angles (critical shoulder angle, lateral acromion angle, glenoid inclination, glenopoar angle). RESULTS: Inter- and intra-observer reliability for landmark selection was moderate to excellent (ICC>0.74). Prediction error was significantly lower for SSMnon-augmented for mean (0.9 mm) and root mean square (1.15 mm) distances. Dice coefficient was significantly higher (0.78) for aSSMproposed compared to all other SSM types. Prediction error for anatomical angles was lowest using the aSSMproposed for critical shoulder angle (3.4°), glenoid inclination (2.6°), and lateral acromion angle (3.2°). CONCLUSION AND SIGNIFICANCE: The conventional SSM robustness criteria or better goodness of fit do not guarantee improved anatomical angle accuracy which may be crucial for certain clinical applications in pre-surgical planning. This study provides insights into how SSM augmented with region-specific anatomical landmarks can provide improved clinical relevance.

Statistical Shape Modeling Approach to Predict Missing Scapular Bone.

Prediction of complete and premorbid scapular anatomy is an important aspect of successful shoulder arthroplasty surgeries to treat glenohumeral arthritis and which remains elusive in the current literature. We proposed to build a statistical shape model (SSM) of the scapula and use it to build a framework to predict a complete scapular shape from virtually created scapular bone defects. The bone defects were synthetically created to imitate bone loss in the glenoid region and missing bony part in inferior and superior scapular regions. Sixty seven dry scapulae were used to build the SSM while ten external scapular shapes (not used in SSM building) were selected to map scapular shape variability using its anatomical classification. For each external scapula, four virtual bone defects were created in the superior, inferior and glenoid regions by manually removing a part of the original mesh. Using these defective shapes as prior knowledge, original shapes were reconstructed using scapula SSM and Gaussian process regression. Robustness of the scapula SSM was excellent (generality = 0.79 mm, specificity = 1.74 mm, first 15 principal modes of variations accounted for 95% variability). The validity and quality of the reconstruction of complete scapular bone were evaluated using two methods (1) mesh distances in terms of mean and RMS values and (2) four anatomical measures (three angles: glenoid version, glenoid inclination, and critical shoulder angle, and glenoid center location). The prediction error in the angle measures ranged from 1.0° to 2.2°. For mesh distances, highest mean and RMS error was 0.97 mm and 1.30 respectively. DICE similarity coefficient between the original and predicted shapes was excellent (≥ 0.81). This framework provided high reconstruction accuracy and can be effectively embedded in the pre-surgical planning of shoulder arthroplasty or in morphology-based shoulder biomechanics modeling pipelines.

Predicting epiphyseal screw length in anterior plating of distal radial fractures.

The purpose of this study was to determine whether optimal epiphyseal screw length could be predicted with reference to a given diaphyseal screw length when fixating a plate to the anterior surface of the distal radius. Computerized tomography scans of 40 wrists of 28 men and 12 women were semi-automatically segmented. A virtual anterior plate model was fixed to the distal radius. The mean maximal appropriate length of one diaphyseal screw and of the four distal epiphyseal screws were measured and linear regression analyses were performed. We found that the epiphyseal screw lengths were highly correlated to the diaphyseal screw length. Based on the data derived from measurements, we recommend epiphyseal screw lengths from ulnar to radial of 18, 18, 20 and 16 mm, respectively, if the diaphyseal screw is 14 mm or less. For diaphyseal screws longer than 14 mm we recommend epiphyseal screws of 20, 20, 22 and 18 mm. Using these recommended screw lengths as general guidelines may reduce the risk of intra-operative and postoperative extensor tendon injury.

In vivo ankle joint kinematics from dynamic magnetic resonance imaging using a registration-based framework.

In this paper, we propose a method for non-invasively measuring three-dimensional in vivo kinematics of the ankle joint from a dynamic MRI acquisition of a single range-of-motion cycle. The proposed approach relies on an intensity-based registration method to estimate motion from multi-plane dynamic MRI data. Our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the joint. First, the rigid motion of each ankle bone is estimated. Second, a four-dimensional (3D+time) high-resolution dynamic MRI sequence is estimated through the use of the log-euclidean framework for the computation of temporal dense deformation fields. This approach has been then applied and evaluated on in vivo dynamic MRI data acquired for a pilot study on six healthy pediatric cohort in order to establish in vivo normative joint biomechanics. Results demonstrate the robustness of the proposed pipeline and very promising high resolution visualization of the ankle joint.

Effects of gleno-humeral joint centre mislocation on gleno-humeral kinematics and kinetics.

The gleno-humeral (GH) rotation centre is typically estimated using predictive or functional methods, however these methods may lead to location errors. This study aimed at determining a location error threshold above which statistically significant changes in the values of kinematic and kinetic GH parameters occur. The secondary aims were to quantify the effects of the direction of mislocation (X, Y or Z axis) of the GH rotation centre on GH kinematic and kinetic parameters. Shoulder flexion and abduction movements of 11 healthy volunteers were recorded using a standard motion capture system (Vicon, Oxford Metrics Ltd, Oxford, UK), then GH kinematic and kinetic parameters were computed. The true position of the GH rotation centre was determined using a low dose x-ray scanner (EOS™ imaging, France) and this position was transferred to the motion data. GH angles and moments were re-computed for each position of the GH rotation centre after errors of up to ± 20 mm were added in increments of ± 5 mm to each axis. The three-dimensional error range was 5 mm to 34.65 mm. GH joint angle and moment values were significantly altered from 10 mm of three-dimensional error, and from 5 mm of error on individual axes. However, errors on the longitudinal and antero-posterior axes only caused very small alterations of GH joint angle and moment values respectively. Future research should develop methods of GH rotation centre estimation that produce three-dimensional location errors of less than 10 mm to reduce error propagation on GH kinematics and kinetics.