6 research outputs found
Spintronic Circuits: The Building Blocks of Spin-Based Computation
In the most general situation, binary computation is implemented by means of microscopic logical gates known as transistors. According to Moore’s Law, the size of transistors will half every two years, and as these transistors reach their fundamental size limit, the quantum effects of the electrons passing through the transistors will be observed. Due to the inherent randomness of these quantum fluctuations, the basic binary logic will become uncontrollable. This project describes the basic principle governing quantum spin-based computing devices, which may provide an alternative to the conventional solid-state computing devices and circumvent the technological limitations of the current implementation of binary logic
External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection.
BACKGROUND: Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the first device to move into the realm of assisted diagnosis for intracranial hemorrhage (ICH) and its subtypes.
METHODS: A single-center retrospective dataset of 402 head noncontrast CT scans (NCCT) with an intracranial hemorrhage were retrospectively collected from January 2012 to July 2020; an additional 108 NCCT scans with no intracranial hemorrhage findings were also included. The presence of an ICH and its subtype were determined from the International Classification of Diseases-10 code associated with the scan and validated by an expert panel. We used the Caire ICH vR1 to analyze these scans, and we evaluated its performance in terms of accuracy, sensitivity, and specificity.
RESULTS: We found the Caire ICH system to have an accuracy of 98.05% (95% confidence interval [CI]: 96.44%-99.06%), a sensitivity of 97.52% (95% CI: 95.50%-98.81%), and a specificity of 100% (95% CI: 96.67%-100.00%) in the detection of ICH. Experts reviewed the 10 incorrectly classified scans.
CONCLUSIONS: The Caire ICH vR1 algorithm was highly accurate, sensitive, and specific in detecting the presence or absence of an ICH and its subtypes in NCCTs. This work suggests that the Caire ICH device has potential to minimize clinical errors in ICH diagnosis that could improve patient outcomes and current workflows as both a point-of-care tool for diagnostics and as a safety net for radiologists
Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage.
BACKGROUND: Intracranial hemorrhage (ICH) requires emergent medical treatment for positive outcomes. While previous artificial intelligence (AI) solutions achieved rapid diagnostics, none were shown to improve the performance of radiologists in detecting ICHs. Here, we show that the Caire ICH artificial intelligence system enhances a radiologist\u27s ICH diagnosis performance.
METHODS: A dataset of non-contrast-enhanced axial cranial computed tomography (CT) scans (n=532) were labeled for the presence or absence of an ICH. If an ICH was detected, its ICH subtype was identified. After a washout period, the three radiologists reviewed the same dataset with the assistance of the Caire ICH system. Performance was measured with respect to reader agreement, accuracy, sensitivity, and specificity when compared to the ground truth, defined as reader consensus.
RESULTS: Caire ICH improved the inter-reader agreement on average by 5.76% in a dataset with an ICH prevalence of 74.3%. Further, radiologists using Caire ICH detected an average of 18 more ICHs and significantly increased their accuracy by 6.15%, their sensitivity by 4.6%, and their specificity by 10.62%. The Caire ICH system also improved the radiologist\u27s ability to accurately identify the ICH subtypes present.
CONCLUSION: The Caire ICH device significantly improves the performance of a cohort of radiologists. Such a device has the potential to be a tool that can improve patient outcomes and reduce misdiagnosis of ICH
Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data
Video capsule endoscopy (VCE) is an innovation that has revolutionized care within the field of gastroenterology, but the time needed to read the studies generated has often been cited as an area for improvement. With the aid of artificial intelligence, various fields have been able to improve the efficiency of their core processes by reducing the burden of irrelevant stimuli on their human elements. In this study, we have created and trained a convolutional neural network (CNN) capable of significantly reducing capsule endoscopy reading times by eliminating normal parts of the video while retaining abnormal ones. Our model, a variation of ResNet50, was able to reduce VCE video length by 47% on average and capture abnormal segments on VCE with 100% accuracy on three VCE videos as confirmed by the reading physician. The ability to successfully pre-process VCE footage as we have demonstrated will greatly increase the practicality of VCE technology without the expense of hundreds of hours of physician annotated videos