Publications by authors named "T Mansi"

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings.

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Article Synopsis
  • - Ultrasound is a valuable imaging technique but its quality is highly dependent on the operator's skill, which is hard to train due to various factors like artifacts and patient differences. Automating image acquisition could enhance consistency and quality but involves collecting a lot of data which isn't typically saved.
  • - The authors introduce a new method to create a large dataset of ultrasound images using data from other imaging modalities, optimized representation, and advanced simulation techniques. This approach allows them to produce patient-specific images to feed into machine learning algorithms.
  • - The validation of this new method shows that it can successfully generate accurate ultrasound images, which can be used to train AI models for navigating and classifying echocardiography views, resulting in improved performance even with
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Radiofrequency (RF) ablation is a minimally invasive therapy for atrial fibrillation. Conventional RF procedures lack intraoperative monitoring of ablation-induced necrosis, complicating assessment of completeness. While spectroscopic photoacoustic (sPA) imaging shows promise in distinguishing ablated tissue, multi-spectral imaging is challenging in vivo due to low imaging quality caused by motion.

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Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients.

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