With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States
. Lung cancer screening using low-dose computed tomography has been shown to reduce ...mortality by 20-43% and is now included in US screening guidelines
. Existing challenges include inter-grader variability and high false-positive and false-negative rates
. We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful
. Despite the existence of screening programmes worldwide, the ...interpretation of mammograms is affected by high rates of false positives and false negatives
. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.
Ballistocardiography and Seismocardiography: A Review of Recent Advances Inan, Omer T.; Migeotte, Pierre-Francois; Kwang-Suk Park ...
IEEE journal of biomedical and health informatics,
2015-July, 2015-Jul, 2015-7-00, 20150701, 2015-07-01, Letnik:
19, Številka:
4
Journal Article, Publication
Recenzirano
Odprti dostop
In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and ...seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.
Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health ...disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear.
Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML.
A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase databases, as well as Google Scholar, identified 635 records, of which 12 studies were included.
Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8%) described a model in routine clinical use, 2 (17%) examined prospectively validated clinical models, and the remaining 9 (75%) described internally validated models. In addition, 8 (67%) studies concluded that racial bias was present, 2 (17%) concluded that it was not, and 2 (17%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42%), accuracy (4/12, 25%), and disparate impact (2/12, 17%). All 8 (67%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors' chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them.
The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.
Abstract
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR ...findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For training and tuning the system, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7–28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. Lastly, to facilitate the continued development of AI models for CXR, we release our collected labels for the publicly available dataset.
We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram ...(SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.
Cardiac MRI (CMR) techniques offer non-invasive visualizations of cardiac morphology and function. However, imaging can be time-consuming and complex. Seismocardiography (SCG) measures physical ...vibrations transmitted through the chest from the beating heart and pulsatile blood flow. SCG signals can be acquired quickly and easily, with inexpensive electronics. This study investigates relationships between CMR metrics of function and SCG signal features. Same-day CMR and SCG data were collected from 28 healthy adults and 6 subjects with aortic valve disease history. Correlation testing and statistical median/decile calculations were performed with data from the healthy cohort. MR-quantified flow and function parameters in the healthy cohort correlated with particular SCG energy levels, such as peak aortic velocity with low-frequency SCG (coefficient 0.43, significance 0.02) and peak flow with high-frequency SCG (coefficient 0.40, significance 0.03). Valve disease-induced flow abnormalities in patients were visualized with MRI, and corresponding abnormalities in SCG signals were identified. This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects. Additionally, through comparison to normative ranges from healthy subjects, it observed correspondences between pathological flow and abnormal SCG. This may support development of an easy clinical test used to identify potential aortic flow abnormalities.
Background Patients with congenital heart disease (CHD) are at risk for the development of low cardiac output and other physiologic derangements, which could be detected early through continuous ...stroke volume (SV) measurement. Unfortunately, existing SV measurement methods are limited in the clinic because of their invasiveness (eg, thermodilution), location (eg, cardiac magnetic resonance imaging), or unreliability (eg, bioimpedance). Multimodal wearable sensing, leveraging the seismocardiogram, a sternal vibration signal associated with cardiomechanical activity, offers a means to monitoring SV conveniently, affordably, and continuously. However, it has not been evaluated in a population with significant anatomical and physiological differences (ie, children with CHD) or compared against a true gold standard (ie, cardiac magnetic resonance). Here, we present the feasibility of wearable estimation of SV in a diverse CHD population (N=45 patients). Methods and Results We used our chest-worn wearable biosensor to measure baseline ECG and seismocardiogram signals from patients with CHD before and after their routine cardiovascular magnetic resonance imaging, and derived features from the measured signals, predominantly systolic time intervals, to estimate SV using ridge regression. Wearable signal features achieved acceptable SV estimation (28% error with respect to cardiovascular magnetic resonance imaging) in a held-out test set, per cardiac output measurement guidelines, with a root-mean-square error of 11.48 mL and
of 0.76. Additionally, we observed that using a combination of electrical and cardiomechanical features surpassed the performance of either modality alone. Conclusions A convenient wearable biosensor that estimates SV enables remote monitoring of cardiac function and may potentially help identify decompensation in patients with CHD.
PDF
