The first applications of artificial intelligence in healthcare were reported over three decades ago . However it is only in the last few years, as artificial intelligence has become embedded within multiple areas of life, that there has been an exponential growth of interest in whether it can assist in automated diagnosis and personalized patient management. Artificial intelligence includes computational techniques that'learn' from existing data to make future decisions. Deep learning is a method composed of many layers of highly interconnected processing elements, which are able to represent high levels of abstraction. The use of deep learning with imaging data is usually based on convolutional neural networks that mimic, to some extent, how the human ventral stream is structured . These techniques facilitate rapid analysis of massive amounts of data .
The electrical activity in the human heart can be measured as a sequence of amplitudes away from a baseline signal. The segmentation of these regions of ECG waveforms can provide the basis for measurements useful for assessing the overall health of the human heart and the presence of abnormalities . Manually annotating each region of the ECG signal can be a tedious and time-consuming task. Signal processing and deep learning methods potentially can help streamline and automate region-of-interest annotation. This example uses ECG signals from the publicly available QT Database  .
A new AI tool developed by researchers from Cedars-Sinai Medical Center can accurately measure plaque deposits in coronary arteries and predict a patient's risk of suffering a heart attack within five years. The tool needs further validation before being deployed in clinics, but promises to automatically achieve in seconds what has previously taken trained experts up to 30 minutes to deliver. A scan known as computed tomography angiography (CTA) is one of the best tools doctors currently have at their disposal to evaluate heart disease patients. CTA imaging of plaque deposits within coronary arteries has recently been found to be the best way to predict a patient's likelihood of heart attack in the near future. "Coronary plaque is often not measured because there is not a fully automated way to do it," said senior author of the new study, Damini Dey, from the Biomedical Imaging Research Institute at Cedars-Sinai. "When it is measured, it takes an expert at least 25 to 30 minutes, but now we can use this program to quantify plaque from CTA images in five to six seconds."
We present a novel approach to evaluate the performance of interpretability methods for time series classification, and propose a new strategy to assess the similarity between domain experts and machine data interpretation. The novel approach leverages a new family of synthetic datasets and introduces new interpretability evaluation metrics. The approach addresses several common issues encountered in the literature, and clearly depicts how well an interpretability method is capturing neural network's data usage, providing a systematic interpretability evaluation framework. The new methodology highlights the superiority of Shapley Value Sampling and Integrated Gradients for interpretability in time-series classification tasks.
ABSTRACT The Internet of Things (IoT) has received a lot of interest from individuals, businesses, governments, and universities for applications such as smart buildings, environmental monitoring, and health care, among others. Due to its high level and omnipresent monitoring, some sophisticated technologies, such as the Internet of Things (IoT), are becoming more accessible nowadays. Network communication between smart devices is facilitated by IoT from any location and at any time. IoT-based health monitoring systems are gaining popularity and acceptability as a means of continuously monitoring and detecting health anomalies from gathered data. A range of wearable remote electrocardiogram (ECG) monitoring devices have been created in response to the growing importance of remote electrocardiogram (ECG) monitoring. Electrocardiographic (ECG) signals are commonly used to identify cardiac problems. In this paper, a novel ECG monitoring approach based on IoT technology is suggested. This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL). In addition, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolution Neural Networks (CNNs)-based approaches for ECG signal categorization were tested in this study. Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions in order to classify cardiac problems efficiently.
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high. In clinical practice, the severeness of CAD is often assessed with a coronary CT angiography (CCTA) scan and manually graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical questions this score assesses are whether patients have CAD or not (rule-out) and whether they have severe CAD or not (hold-out). In this work, we reach new state-of-the-art performance for automatic CAD-RADS scoring. We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture. Furthermore, we introduce a novel task- and model-specific, heuristic coronary segment labeling, which subdivides coronary trees into consistent parts across patients. It is fast, robust, and easy to implement. We were able to raise the previously reported area under the receiver operating characteristic curve (AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the hold-out task respectively.
This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral changes are induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would provide guidelines for future research enabling effective detection of human stress conditions.
In 2021 the application of AI enabled advances in many areas of healthcare. We made significant progress in AI for drug discovery, medical imaging, diagnostics, pathology, and clinical trials. Important peer reviewed papers were published and dozens of partnerships were formed. Big Pharma companies and major tech companies became very active in the space. Record amounts of funding were raised, and a few companies even started human clinical trials. Microsoft and NVIDIA launched two of the world's most powerful supercomputers and Microsoft announced Azure OpenAI Service. In 2022 we expect these technologies to converge across the healthcare spectrum. This article summarizes milestones achieved in 2021. This is the first in a series of progress reports I'm writing on the sector that will be supplemented by industry performance data and metrics compiled in partnership with Alliance for Artificial Intelligence in Healthcare (AAIH) and other top tier resources.
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attempted ECG denoising, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis.