Publications by authors named "Sulaiman Somani"
Article Synopsis
- 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.
Article Synopsis
- Segmenting CT is essential for clinical practices like personalized cardiac ablation, but traditional machine learning methods often require large labeled datasets which are difficult to gather.
- The article introduces the DOKEN algorithm, which uses domain knowledge to automatically label a small training set, enabling high-performance ML segmentation without the need for extensive data.
- In tests, the DOKEN-enhanced nnU-Net model showed impressive segmentation results, achieving a high Dice score of 96.7% and demonstrating performance comparable to expert manual segmentation, thus validating its efficacy in real-world applications.
Article Synopsis
- - Globally, obesity is on the rise, leading to serious health issues, including heart disease, and is a significant financial burden on healthcare systems, costing over $200 billion a year.
- - This study utilized advanced AI to analyze over 390,000 Reddit discussions about GLP-1 receptor agonists (GLP-1 RAs), highlighting a wide interest in topics like weight loss results, side effects, accessibility, and psychological benefits.
- - The analysis revealed that public sentiment around GLP-1 RAs is mostly neutral to positive, suggesting these findings could help in monitoring side effects not seen in trials and addressing drug shortages.
Article Synopsis
- This review highlights how Artificial Intelligence (AI) improves the assessment of atherosclerotic cardiovascular disease (ASCVD) risk, opportunistic screening, and guideline adherence by analyzing both unstructured clinical and patient-generated data.
- Recent findings indicate that AI models outperform traditional risk scores in evaluating individual ASCVD risk and can automatically detect risk markers, like coronary artery calcium (CAC), using various imaging techniques.
- AI applications are valuable for preventing and managing ASCVD, and they can enhance patient education, but successful integration into clinical practice requires careful regulation and structured clinical pathways.
Article Synopsis
- - Coronary artery calcium (CAC) testing is important for assessing the risk of atherosclerotic cardiovascular disease (ASCVD), but public perception of CAC and its implications for heart health decision-making are not well understood.
- - Researchers utilized an AI model to analyze 5,606 discussions on Reddit about CAC, identifying 91 topics categorized into 14 main themes, including the influence of CAC on treatment choices and concerns over testing risks.
- - Sentiment analysis of these discussions showed that nearly half expressed neutral or negative feelings towards CAC testing, highlighting a need for better communication and education to improve public understanding and shared decision-making in cardiovascular health.
Article Synopsis
- Segmentation of cardiac CT is crucial for procedures like cardiac ablation, but traditional machine learning methods require extensive labeled data, which is hard to gather.
- The researchers developed a "virtual dissection" model that uses simple geometric shapes to represent atrial anatomy, enabling effective segmentation with minimal training data in a sample of just 6 digital hearts.
- Their results showed high accuracy in segmenting atrial structures across multiple datasets, significantly reducing segmentation time in live patients while maintaining accuracy comparable to expert assessments.
Advances in cardiac pacing with leadless pacemakers and conduction system pacing.
Curr Opin Cardiol
January 2024
Article Synopsis
- Recent advancements in cardiac pacing include conduction system pacing (CSP) and leadless pacemakers (LLPMs), which offer benefits like reduced complications and better heart function compared to traditional methods.
- CSP, particularly in the left bundle branch, is gaining popularity due to its advantages in improving heart mechanics, although challenges with lead placement and maintenance still exist.
- LLPMs, such as Aveir and Micra, are gaining traction for their effectiveness and compatibility with CSP, indicating a promising future for improved patient care in cardiac pacing technology.
Article Synopsis
- This study evaluated a new technique for detecting left ventricular hypertrophy (LVH) using ECGs, comparing it to existing methods to determine effectiveness.
- The retrospective analysis involved over 53,000 ECG and echocardiogram pairs, revealing that the new technique, called Witteles-Somani (WS), shows comparable performance to others like Sokolow-Lyon and Cornell.
- Results indicated that patients identified with LVH using the WS method faced a significantly increased risk of cardiovascular issues, including heart failure and mortality.
Article Synopsis
- Statins are effective in reducing cardiovascular events but are not widely used, which raises questions about public perceptions, particularly on social media platforms like Reddit.
- The study analyzed over a decade of discussions about statins on Reddit, employing AI to categorize topics and measure sentiment around the posts.
- A total of 10,233 discussions were identified, revealing six main themes related to statins, including concerns about side effects, hesitancy in usage, dietary influences, and perceived biases from the pharmaceutical industry.
Article Synopsis
- Electrocardiogram (ECG) deep learning research aims to enhance cardiovascular patient outcomes by using CNNs, but traditional methods can lead to redundancy and inaccuracies in predictions.
- The study introduced a sub-waveform representation focusing on the rhythmic patterns of ECG data to improve prediction accuracy without altering the CNN architecture.
- Results from analyzing 92,446 patients showed significant performance improvements (2% increase in area under the receiver operating characteristic curve and 10% in precision-recall), alongside better prediction reliability and reduced uncertainties, indicating potential benefits for future cardiovascular AI technologies.
Article Synopsis
- A lot of clinical research relies on observational data from various sources like medical records and trials because randomized controlled trials can be expensive and sometimes unfeasible.
- Using advanced techniques like deep autoencoders to project complex medical data into a simplified form can enhance the matching of treatment groups, improving the analysis of confounding variables.
- The results show that this method provides better matching than traditional methods and can perform comparably to expert-designed models, making it valuable for analyzing complex clinical data situations.
Article Synopsis
- - The study explores the effectiveness of deep learning models utilizing electrocardiogram (ECG) data to improve the specificity of screening for pulmonary embolism (PE), addressing the issue of overusing computed tomography pulmonary angiograms (CTPAs).
- - Researchers built a cohort of over 21,000 patients and developed three predictive models: one based on ECG data, one on electronic health records (EHR), and a Fusion model combining both, finding the Fusion model significantly outperformed the others in PE detection accuracy.
- - The findings suggest that integrating ECG waveforms with clinical data can enhance the specificity and overall performance in detecting PE, offering a potential improvement over traditional clinical risk scoring methods.
Article Synopsis
- Deep learning models in healthcare need large, balanced datasets to work effectively, but COVID-19 data is often imbalanced, presenting a challenge for model training.! -
- Traditional cross-entropy loss (CEL) can struggle with imbalanced data, but the study shows that using contrastive loss (CL) enhances the performance of CEL, particularly with COVID-19 electronic health records.! -
- The research demonstrates that CL models consistently perform better than CEL models in predicting patient outcomes like mortality and ICU transfers, achieving notable improvements in precision and recall metrics.!
Objectives: This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.
Background: Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only.
Article Synopsis
- AI has the potential to enhance the patient-physician relationship in primary care, but biases in these systems can negatively affect vulnerable populations.
- The scoping review aims to assess how much AI in primary care addresses bias against these groups and how developers manage such biases.
- The review will identify the current state of healthcare equity in AI systems, focusing on their inclusivity of vulnerable patients, documentation of bias, and methods for mitigating harmful biases.
Article Synopsis
- The study addresses the challenge of classifying hospital admissions for acute myocardial infarction (AMI) using electronic health records (EHRs) and presents a novel method for identifying STEMI encounters.
- Researchers developed and validated algorithms that use multi-modal data, including discharge summaries and cardiac catheterization details, from a large database of EHRs involving millions of patients.
- The results showed that while using discharge summaries alone captures many STEMIs, combining these summaries with specific ICD codes and cardiac catheterization information significantly increases detection precision.
Article Synopsis
- * The proposed solution introduces a heterogeneous graph model (HGM) that incorporates relational learning to better predict mortality in COVID-19 ICU patients by utilizing large EHR datasets from multiple hospitals.
- * Experimental results indicate that the HGM model, using a unique Skip-Gram relational learning strategy, significantly outperforms traditional models in accuracy and recall, achieving higher area under the receiver operating characteristic curve (auROC) across different prediction time frames.
Article Synopsis
- Deep learning has become crucial in analyzing large healthcare datasets for disease classification, predictions, and decision-making in the past decade.
- Public ECG datasets have been around since the 1980s, mainly for specific cardiology issues, while private institutions now offer significantly larger databases for deep learning applications.
- This review aims to educate clinicians on deep learning basics, its current uses in ECG analysis, as well as its limitations and potential future developments.
Article Synopsis
- Machine Learning models need large and balanced datasets to effectively analyze healthcare data, but COVID-19-related data is often unbalanced, especially within electronic health records (EHR).
- Traditional methods like cross-entropy loss struggle with classification accuracy under these conditions, leading researchers to explore contrastive loss as a potential solution.
- This study, using EHR data from five hospitals, demonstrates that contrastive loss significantly improves model performance for predicting COVID-19 patient outcomes compared to cross-entropy loss, particularly when dealing with severe class imbalance.
Article Synopsis
- Machine learning models require large datasets, often limited by data silos across healthcare institutions, particularly in COVID-19 research focused on single hospitals.
- The study utilized federated learning to predict 7-day mortality in hospitalized COVID-19 patients, using data from five hospitals within the Mount Sinai Health System without aggregating sensitive patient data.
- Results showed that the LASSO model performed better at three hospitals and the multilayer perceptron (MLP) model outperformed at all five, indicating that federated learning can create effective predictive models while protecting patient privacy.
Article Synopsis
- The study aimed to analyze clinical characteristics and outcomes of hospitalized COVID-19 patients, comparing those who died in the hospital to those who were discharged alive.
- Data was collected from five hospitals in the Mount Sinai Health System for patients confirmed with COVID-19 between February and April 2020, focusing on demographics, clinical features, and mortality rates.
- Results showed that nearly half of the 2199 hospitalized patients were discharged, with a 29% overall mortality rate, higher rates of pre-existing conditions and lower lymphocyte percentages observed in patients who died compared to those who recovered.
Article Synopsis
- - In April 2020, the FDA shortened the deferral period for blood donations from men who have sex with men (MSM) from one year to three months due to COVID-19-related blood shortages, highlighting the need for immediate policy reconsideration.
- - The longstanding restrictions on MSM blood donors have been eased due to advancements in HIV testing and treatment, but ethical concerns remain regarding the current deferral policy and its impact on marginalized groups.
- - The proposal suggests an individual risk-based screening method that would eliminate exclusion based on gender identity or sexual orientation, promoting inclusivity in blood donation while maintaining safety standards.
Article Synopsis
- The COVID-19 pandemic has led to significant illness and mortality worldwide, highlighting the need for better resource allocation and risk identification for patients.
- This study aimed to analyze electronic health records from COVID-19 patients in the Mount Sinai Health System to develop machine learning models predicting hospital outcomes based on patient characteristics at admission.
- Using the XGBoost algorithm, the study found strong predictive performance for in-hospital mortality and critical events, with high accuracy scores across various time frames and effective model validation across multiple hospitals.