Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Human Movement Disorders Analysis with Graph Neural Networks

Human movement disorders encompass a group of neurological conditions that cause abnormal movements. These disorders, even when subtle, may be symptomatic of a broad spectrum of medical issues, from neurological to musculoskeletal. Clinicians and researchers still encounter challenges in understanding the underlying pathologies. In light of this, medical professionals and associated researchers are increasingly looking towards the fast-evolving domain of computer vision in pursuit of precise and dependable automated diagnostic tools to support clinical diagnosis. To this end, this thesis explores the feasibility of the interpretable and accurate human movement disorders analysis system using graph neural networks. Cerebral Palsy (CP) and Parkinson’s Disease (PD) are two common neurological diseases associated with movement disorders that seriously affect patients’ quality of life. Specifically, CP is estimated to affect 2 in 1000 babies born in the UK each year, while PD affects an estimated 10 million people globally. Considering their clinical significance and properties, we develop and examine the state-of-the-art attention-informed Graph Neural Networks (GNN) for robust and interpretable CP prediction and PD diagnosis. We highlight the significant differences between the human body movement frequency of CP infants and healthy groups, and propose frequency attention-informed convolutional networks (GCNs) and spatial frequency attention based GCNs to predict CP with strong interpretability. To support the early diagnosis of PD, we propose novel video-based deep learning system, SPA-PTA, with a spatial pyramidal attention design based on clinical observations and mathematical theories. Our systems provide undiagnosed PD patients with low-cost, non-intrusive PT classification and tremor severity rating results as a PD warning sign with interpretable attention visualizations

Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC

Evaluating surgical skills from kinematic data using convolutional neural networks

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.Comment: Accepted at MICCAI 201

Applications of interpretability in deep learning models for ophthalmology

PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT FINDINGS: The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' 'interpretability' and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice