Application of StyleGAN Architecture for Generating Venous Leg Ulcer Images.

Leg ulcers caused by impaired venous blood return are the most typical chronic wound form and have a significant negative impact on the lives of people living with these wounds. Thus, it is important to provide early assessment and appropriate treatment of the wounds to promote their healing in the normal trajectory. Gathering quality wound data is an important component of good clinical care, enabling monitoring of healing progress. This data can also be useful to train machine learning algorithms with a view to predicting healing. Unfortunately, a high volume of good-quality data is needed to create datasets of suitable volume from people with wounds. In order to improve the process of gathering venous leg ulcer (VLU) data we propose the generative adversarial network based on StyleGAN architecture to synthesize new images from original samples. We utilized a dataset that was manually collected as part of a longitudinal observational study of VLUs and successfully synthesized new samples. These synthesized samples were validated by two clinicians. In future work, we plan to further process these new samples to train a fully automated neural network for ulcer segmentation.

Human Factors in Sensory Feedback for Haptic Gloves.

Manual therapy training requires close proximity between the clinical teacher and students, which limits the training of people in remote and rural regions. Video-based online training can provide visual but not tactile information, which is also essential for manual therapies. This project describes the development and testing of an inexpensive sensor glove developed using commercially available sensors, suitable for monitoring the shape and force applied by the hand of a person delivering a spinal manipulation. Its focus was the development of software to provide the human user with tactile information that is usually acquired intuitively in face-to-face teaching. Though rigorous assessment of the glove's application showed errors at low levels of force in actual force measurement and interpretation by users, these errors were reduced at higher levels of force. Trainers of spinal manipulation reported the device to be very useful and suitable for the purpose. We conclude that this glove has the potential for being used for online training of students.Clinical Impact: The outcome of this study shows the feasibility of developing an inexpensive haptic glove using proprietary software for online training of students of manual therapy.

Tabular data augmentation for video-based detection of hypomimia in Parkinson's disease.

BACKGROUND AND OBJECTIVE: This paper presents a method for the computerized detection of hypomimia in people with Parkinson's disease (PD). It overcomes the difficulty of the small and unbalanced size of available datasets. METHODS: A public dataset consisting of features of the video recordings of people with PD with four facial expressions was used. Synthetic data was generated using a Conditional Generative Adversarial Network (CGAN) for training augmentation. After training the model, Test-Time Augmentation was performed. The classification was conducted using the original test set to prevent bias in the results. RESULTS: The employment of CGAN followed by Test-Time Augmentation led to an accuracy of classification of the videos of 83%, specificity of 82%, and sensitivity of 85% in the test set that the prevalence of PD was around 7% and where real data was used for testing. This is a significant improvement compared with other similar studies. The results show that while the technique was able to detect people with PD, there were a number of false positives. Hence this is suitable for applications such as population screening or assisting clinicians, but at this stage is not suitable for diagnosis. CONCLUSIONS: This work has the potential for assisting neurologists to perform online diagnose and monitoring their patients. However, it is essential to test this for different ethnicity and to test its repeatability.