A University of Pittsburgh researcher is developing artificial intelligence (AI) software to transform computed tomographic pulmonary angiography (CTPA) into non-contrast CT (NCCT) scans. This software will then be used to train an AI algorithm to detect pulmonary embolisms (PE) on routinely acquired NCCT scans. This novel software and AI algorithm could substantially improve the detection of life-threatening PE, offering a more accessible, efficient, and potentially safer method of diagnosis.
University of Pittsburgh researchers have developed system techniques integrating artificial intelligence (AI) and smartphone sensing, namely PTEase, that can be used as a pulmonary telemedicine device by patients to accurately evaluate pulmonary disease conditions and provide clinically relevant information out of clinic. The development of PTEase and the associated mouthpiece uses acoustic signals transmitted from the smartphone and the associated detected reflections along with AI to accurately determine the CSA of a patient’s airway segments.
University of Pittsburgh researchers have designed a closed-loop multimodal artificial intelligence (AI) assisted neuromodulation device to treat heart failure (HF). Assisted by artificial intelligence, this device aims to restore healthy autonomic balance in patients with HF with a view to halting progression of HF and improving the outcomes and quality of life for patients within this currently unmet clinical need. AI based on feedback from biomarkers, including ECG and blood pressure will be acquired and analyzed in real time, and will personalize the SCS and VNS parameters to provide autonomic balance, thereby reducing the risk of worsening HF or fatal ventricular arrhythmias.
University of Pittsburgh researchers are developing a novel artificial intelligence (AI) assisted non-invasive diagnostic tool to diagnose the severity of deep vein thrombosis (DVT) and predict those most at risk of developing post thrombotic syndrome (PTS). This novel approach will use non-invasive analysis to measure mechanical changes in the vein wall after DVT and use AI to predict a patient’s risk of developing PTS. All mechanical and clinical information will be used to develop a machine learning model based on the data collected from patients to produce an AI tool capable of classifying the severity of DVT and PTS.
University of Pittsburgh researchers have developed system techniques integrating artificial intelligence (AI) and smartphone sensing, namely PTEase, that can be used as a pulmonary telemedicine device by patients to accurately evaluate pulmonary disease conditions and provide clinically relevant information out of clinic. The development of PTEase and the associated mouthpiece uses acoustic signals transmitted from the smartphone and the associated detected reflections along with AI to accurately determine the CSA of a patient’s airway segments.
Utilizing advances in artificial intelligence (AI) and machine learning (ML), it is possible to predict changes in aneurysms or cancerous tumors over time, allowing clinicians better understanding of the risks to patients and thereby taking interventions where required. This novel approach uses AI to predict future changes in growth shape and morphology, and to determine the historical changes allowing clinicians to accurately predict risk of rupture or metastasis.
It is increasingly used in patient management, clinical research, and the development of artificial intelligence (AI) tools to support clinical decision making. Novel software has been developed to collect data from existing EHR databases for use in Azure Healthcare API with the potential to improve healthcare efficiency, clinical research, and AI tool development using large existing databases. • AI tool development using Azure Healthcare API.
This technology comprises a specialized codebook designed to annotate clinical data associated with an ICU rounding checklist based on the ABCEDF bundle.
A model processes transcribed ICU rounding discussions by initially using a multinomial logistic regression classifier that categorizes each utterance into one of nine predefined groups.
• Artificial intelligence (AI) in medical diagnostics. Novel AI software has also been developed to facilitate beamforming following the drastic reduction in the number of receiving sensor elements to enhance images collected.
Previous research and development have led to an AI-based risk prediction tool for aneurysm rupture using millions of distinct data points to create medical images.
The invention involves a multi-layered AI approach that includes AI software to automatically detect and draw regions of interest (ROI) around organs in each image frame, computational refinement layers to minimize artifacts such as patient motion and optimize AI output, and automated calculation of ejection fractions after excluding bad data. This technology leverages an advanced AI algorithm to detect and track organs of interest and calculate ejection fractions frame-by-frame, enhancing workflow efficiency and patient safety.
