Publications by authors named "J Tooley"
Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.
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- This study investigates whether simpler models using standard ECG measurements can effectively detect left ventricular systolic dysfunction (LVSD) compared to complex deep learning methods.
- Analyzing a dataset of nearly 41,000 ECGs, researchers found that a random forest model and a logistic regression model both achieved high accuracy in detecting LVSD, with performance comparable todeep learning models.
- The findings suggest that simpler ECG models are not only effective but also easier to implement and interpret in clinical settings, making them potentially more suitable for widespread use.
CRISPR/Cas9 gene editing represents an exciting avenue to study genes of unknown function and can be combined with genetically encoded tools such as fluorescent proteins, channelrhodopsins, DREADDs, and various biosensors to more deeply probe the function of these genes in different cell types. However, current strategies to also manipulate or visualize edited cells are challenging due to the large size of Cas9 proteins and the limited packaging capacity of adeno-associated viruses (AAVs). To overcome these constraints, we developed an alternative gene editing strategy using a single AAV vector and mouse lines that express Cre-dependent Cas9 to achieve efficient cell-type specific editing across the nervous system.
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- * A hypertension management platform was developed through careful analysis of clinician workflows and needs, incorporating input from 5 Stanford clinicians and a team of 15 specialists across various fields.
- * The platform aims to enhance chronic disease management by integrating digital health tools, offering a model that can be adapted for other cardiovascular digital health solutions.
Article Synopsis
- Existing preoperative risk assessments are inadequate at predicting postoperative mortality, but deep-learning analysis of ECGs can highlight hidden risk factors.
- A deep-learning algorithm was developed using ECG data from nearly 46,000 patients to more accurately forecast postoperative mortality, and its performance was compared to the Revised Cardiac Risk Index (RCRI).
- In testing, the algorithm achieved an AUC of 0.83, significantly outperforming the RCRI score (AUC of 0.67), indicating its effectiveness across multiple health-care systems.