Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages

Simultaneous Measurement Imputation and Rehabilitation Outcome Prediction for Achilles Tendon Rupture

Achilles tendonbrott (Achilles Tendon Rupture, ATR) är en av de typiska mjukvävnadsskadorna. Rehabilitering efter sådana muskuloskeletala skador förblir en långvarig process med ett mycket variet resultat. Att kunna förutsäga rehabiliteringsresultat exakt är avgörande för beslutsfattande stöduppdrag. I detta arbete designar vi en probabilistisk modell för att förutse rehabiliteringsresultat för ATR med hjälp av en klinisk kohort med många saknade poster. Vår modell är tränad från början till slutet för att samtidigt förutsäga de saknade inmatningarna och rehabiliteringsresultat. Vi utvärderar vår modell och jämför med flera baslinjer, inklusive flerstegsmetoder. Experimentella resultat visar överlägsenheten hos vår modell över dessa flerstadiga tillvägagångssätt med olika dataimuleringsmetoder för ATR rehabiliterings utfalls prognos.Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such musculoskeletal injuries remains a prolonged process with a very variable outcome. Being able to predict the rehabilitation outcome accurately is crucial for treatment decision support. In this work, we design a probabilistic model to predict the rehabilitation outcome for ATR using a clinical cohort with numerous missing entries. Our model is trained end-to-end in order to simultaneously predict the missing entries and the rehabilitation outcome. We evaluate our model and compare with multiple baselines, including multi-stage methods. Experimental results demonstrate the superiority of our model over these baseline multi-stage approaches with various data imputation methods for ATR rehabilitation outcome prediction

Simultaneous Measurement Imputation and Rehabilitation Outcome Prediction for Achilles Tendon Rupture

Achilles tendonbrott (Achilles Tendon Rupture, ATR) är en av de typiska mjukvävnadsskadorna. Rehabilitering efter sådana muskuloskeletala skador förblir en långvarig process med ett mycket variet resultat. Att kunna förutsäga rehabiliteringsresultat exakt är avgörande för beslutsfattande stöduppdrag. I detta arbete designar vi en probabilistisk modell för att förutse rehabiliteringsresultat för ATR med hjälp av en klinisk kohort med många saknade poster. Vår modell är tränad från början till slutet för att samtidigt förutsäga de saknade inmatningarna och rehabiliteringsresultat. Vi utvärderar vår modell och jämför med flera baslinjer, inklusive flerstegsmetoder. Experimentella resultat visar överlägsenheten hos vår modell över dessa flerstadiga tillvägagångssätt med olika dataimuleringsmetoder för ATR rehabiliterings utfalls prognos.Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such musculoskeletal injuries remains a prolonged process with a very variable outcome. Being able to predict the rehabilitation outcome accurately is crucial for treatment decision support. In this work, we design a probabilistic model to predict the rehabilitation outcome for ATR using a clinical cohort with numerous missing entries. Our model is trained end-to-end in order to simultaneously predict the missing entries and the rehabilitation outcome. We evaluate our model and compare with multiple baselines, including multi-stage methods. Experimental results demonstrate the superiority of our model over these baseline multi-stage approaches with various data imputation methods for ATR rehabilitation outcome prediction

Simultaneous Measurement Imputation and Rehabilitation Outcome Prediction for Achilles Tendon Rupture

Achilles tendonbrott (Achilles Tendon Rupture, ATR) är en av de typiska mjukvävnadsskadorna. Rehabilitering efter sådana muskuloskeletala skador förblir en långvarig process med ett mycket variet resultat. Att kunna förutsäga rehabiliteringsresultat exakt är avgörande för beslutsfattande stöduppdrag. I detta arbete designar vi en probabilistisk modell för att förutse rehabiliteringsresultat för ATR med hjälp av en klinisk kohort med många saknade poster. Vår modell är tränad från början till slutet för att samtidigt förutsäga de saknade inmatningarna och rehabiliteringsresultat. Vi utvärderar vår modell och jämför med flera baslinjer, inklusive flerstegsmetoder. Experimentella resultat visar överlägsenheten hos vår modell över dessa flerstadiga tillvägagångssätt med olika dataimuleringsmetoder för ATR rehabiliterings utfalls prognos.Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such musculoskeletal injuries remains a prolonged process with a very variable outcome. Being able to predict the rehabilitation outcome accurately is crucial for treatment decision support. In this work, we design a probabilistic model to predict the rehabilitation outcome for ATR using a clinical cohort with numerous missing entries. Our model is trained end-to-end in order to simultaneously predict the missing entries and the rehabilitation outcome. We evaluate our model and compare with multiple baselines, including multi-stage methods. Experimental results demonstrate the superiority of our model over these baseline multi-stage approaches with various data imputation methods for ATR rehabilitation outcome prediction

Towards real-time dense 3D mapping with lightweight portable sensors

Simultaneous Localization And Mapping (SLAM) is the task of constructing a map of an unknown environment while simultaneously maintaining an estimate of the agent's localization within this map. SLAM exists in many variants and is used in many different contexts. Dense 3D SLAM is one of the most important variants, which aims to create dense 3D maps that can be used for applications in fields such as civil engineering, security, virtual reality or autonomous robotics. The current standard method for dense 3D SLAM uses scanning Light Detection And Ranging (LiDAR) devices and Inertial Measurement Units (IMU), typically mounted on wheeled rovers for increased stability. As a whole, this setup allows a great mapping accuracy but is heavy, hard to navigate in non-flat environments, power-hungry and expensive. In this work, we aim to perform dense 3D SLAM with lightweight, portable sensors. More specifically, we use a specific type of camera that perceives 3D information with the same principle as LiDAR devices but in a miniaturized, less power-hungry manner. This allows the camera to be carried by hand or worn (on a helmet, for example) but comes at the cost of a decreased sensing accuracy. Moreover, carrying the camera by hand or wearing it on a helmet instead of setting it up on a wheeled rover makes it much less stable, making it even harder to estimate in the localization step of the problem. Therefore, most of our efforts go into the adaptation of existing LiDAR-inertial SLAM methods for noisier measurement with a more complex motion model. To do so, we develop a probabilistic SLAM framework based on the Error-State Iterative Kalman Filter (ESIKF) and implement various techniques to make the execution faster and less error-prone. With our experiments, we show that our method achieves competitive accuracy in different environments

Evaluating the impact of head motion on monocular visual odometry with synthetic data

Monocular visual odometry is a core component of visual Simultaneous Localization and Mapping (SLAM). Nowadays, headsets with a forward-pointing camera abound for a wide range of use cases such as extreme sports, firefighting or military interventions. Many of these headsets do not feature additional sensors such as a stereo camera or an IMU, thus evaluating the accuracy and robustness of monocular odometry remains critical. In this paper, we develop a novel framework for procedural synthetic dataset generation and a dedicated motion model for headset-mounted cameras. With our method, we study the performance of the leading classes of monocular visual odometry algorithms, namely feature-based, direct and deep learning-based methods. Our experiments lead to the following conclusions: i) the performance deterioration on headset-mounted camera images is mostly caused by head rotations and not by translations caused by human walking style, ii) feature-based methods are more robust to fast head rotations compared to direct and deep learning-based methods, and iii) it is crucial to develop uncertainty metrics for deep learning-based odometry algorithms

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the AT Rrehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages.QC 20190912</p

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the AT Rrehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages.QC 20190912</p