Background: Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify tra...
详细信息
Background: Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asymptomatic. However, current ST-segment monitoring software is vastly underutilized due to false positive alarms, with resultant alarm fatigue. Current ST algorithms may contribute to alarm fatigue because;(I) they are not designed with a delay (minutes), rather alarm to brief spikes (i.e., turning, heart rate changes), and (2) alarm to changes in a single ECG lead, rather than contiguous leads. Purpose: This study was designed to determine sensitivity, and specificity, of ST algorithms when accounting for;ST magnitude (100 mu V vs 200 mu V), duration, and changes in contiguous ECG leads (i.e., aVL, I, aVR, II, aVF, III;V1, V2, V3, V4, V5, V6, V6, I). Methods: This was a secondary analysis from the COMPARE Study, which assessed occurrence rates for transient myocardial ischemia in hospitalized patients with suspected ACS using 12-lead Holter. Transient myocardial ischemia was identified from Holter using >100 mu V ST-segment up arrow or down arrow, in >1 ECG lead, > I min. algorithms tested against Holter transient myocardial ischemia were done using the University of California San Francisco (UCSF) ECG algorithm and included: (1)100 mu V vs 200 mu V any lead during a 5-min ST average;(2)100 mu V vs 200 mu V any lead >5 min, (3) 100 mu V vs 200 mu V any lead during a 5-min ST average in contiguous leads, and (4) 100 mu V vs 200 mu V > 5 min in contiguous leads (Table below). Results: In 361 patients;mean age 63 + 12 years, 63% male, 56% prior CAD, 43 (11%) had transient myocardial ischemia. Of the 43 patients with transient myocardial ischemia, 17 (40%) had ST-segment elevation events, and 26 (60%) ST-segment depression events. A higher proportion of patients with ST segment depression has missed ischemic events. [GRAPHICS]
An automatic programming system using object-oriented programming techniques is described. This system is intended for use as a design tool for signal processing software. The system starts with a graphical descriptio...
详细信息
An automatic programming system using object-oriented programming techniques is described. This system is intended for use as a design tool for signal processing software. The system starts with a graphical description of an algorithm, supports interactive editing and performance analysis, and produces as output source code which implements the algorithm.
The editorial introduces JMI Issue 3 Volume 11, looks ahead to SPIE Medical Imaging, and highlights the journal’s policy on conference article submission.
The editorial introduces JMI Issue 3 Volume 11, looks ahead to SPIE Medical Imaging, and highlights the journal’s policy on conference article submission.
Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving ...
详细信息
Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction-glance duration, glance history, and glance location-on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior-1.5th power of duration and duration weighted by glance location-produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support realtime distraction mitigation systems.
As artificial intelligence (Al) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there ar...
详细信息
As artificial intelligence (Al) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I consider a specific one: a potential misalignment of the self-interests of radiologists and Al developers with the best interests of the patients. Radiologists know that supporting research into Al and advocating for its adoption in clinical settings could diminish their employment opportunities and reduce respect for their profession. This provides an incentive to oppose Al in various ways. Al developers have an incentive to hype their discoveries to gain attention. This could provide short-term personal gains, however, it could also create a distrust toward the field if it became apparent that the state of the art was far from where it was promised to be. The future research and clinical implementation of Al in radiology will be partially determined by radiologist and Al researchers. Therefore, it is very important that we recognize our own personal motivations and biases and act responsibly to ensure the highest benefit of the Al transformation to the patients.
Objective: This study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs)...
详细信息
Objective: This study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs);and 2) assess their impact on relative differences across sex, race, ethnicity, and language *** and Methods: The study used EHR data from a tertiary academic medical center. A baseline rule-base algorithm, relying on structured family history data (structured data;SD), was enhanced using a natural language processing (NLP) component and a relaxed criteria algorithm (partial match [PM]). The identification rates and differences were analyzed considering sex, race, ethnicity, and language ***: Among 120,007 patients aged 25-60, detection rate differences were found across all groups using the SD (all P < 0.001). Both enhancements increased identification rates;NLP led to a 1.9 % increase and the relaxed criteria algorithm (PM) led to an 18.5 % increase (both P < 0.001). Combining SD with NLP and PM yielded a 20.4 % increase (P < 0.001). Similar increases were observed within subgroups. Relative differences persisted across most categories for the enhanced algorithms, with disproportionately higher identification of patients who are White, Female, non-Hispanic, and whose preferred language is ***: algorithm enhancements increased identification rates for patients eligible for genetic testing of hereditary cancer syndromes, regardless of sex, race, ethnicity, and language preference. However, differences in identification rates persisted, emphasizing the need for additional strategies to reduce disparities such as addressing underlying biases in EHR family health information and selectively applying algorithm enhancements for disadvantaged populations. Systematic assessment of differences in algorithm performance across population subgroups should be incorporated into algorithm development processes.
暂无评论