This paper introduces Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised contrastive pretraining strategy using both CT scans and EHR data to improve feature alignment between modalities for better PE diagnosis. PECon pulls sample features of the same class together while pushing away features of other classes. The approach achieves state-of-the-art results on the RadFusion dataset, with an F1-score of 0.913 and AUROC of 0.943.
The paper introduces MedPromptX, a clinical decision support system using multimodal large language models (MLLMs), few-shot prompting (FP), and visual grounding (VG) for chest X-ray diagnosis, integrating imagery with EHR data. MedPromptX refines few-shot data dynamically for real-time adjustment to new patient scenarios and narrows the search area in X-ray images. The study introduces MedPromptX-VQA, a new visual question answering dataset, and demonstrates state-of-the-art performance with an 11% improvement in F1-score compared to baselines.
MBZUAI and Sheikh Shakbout Medical City researchers developed PECon, a deep learning method for pulmonary embolism detection using CT scans and electronic health records. PECon uses neural networks and contrastive learning to encode and align image and text data. The method aims to improve diagnosis accuracy and speed, potentially saving lives. Why it matters: This research demonstrates AI's potential to enhance medical diagnostics in the UAE, addressing a critical healthcare challenge.
A KAUST team led by Xin Gao developed an AI model for COVID-19 detection from CT scans, addressing limitations of existing methods. The model incorporates a novel embedding strategy, a CT scan simulator, and a 2.5D deep-learning algorithm. Tested at King Faisal Specialist Hospital, the model demonstrated high accuracy in detecting COVID-19 cases. Why it matters: This research provides a valuable tool for rapid and accurate COVID-19 diagnosis in the region, especially in early-stage infections, improving healthcare outcomes.
MBZUAI graduate Ahmed Sharshar developed a computer vision application that assesses lung health from a video of a person breathing, estimating Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). The model achieved up to 100% accuracy using thermal video data from 60 participants. Sharshar aims to create lightweight models applicable in developing countries without high-end GPUs. Why it matters: This research showcases the potential of AI to democratize healthcare access through non-invasive, accessible diagnostic tools.
A KAUST Rapid Research Response Team (R3T) is collaborating with healthcare stakeholders to combat COVID-19. Xin Gao and his Structural and Functional Bioinformatics (SFB) Group are developing an AI-based diagnosis pipeline from CT scans of COVID-19 patients. The AI pipeline aims to address the high false negative rates associated with nucleic acid detection. Why it matters: This research could improve COVID-19 diagnostics and potentially inform understanding of viral pathogenesis.
MBZUAI doctoral student Mai A. Shaaban and colleagues developed MedPromptX, a system that analyzes chest X-rays and patient data to aid lung disease diagnoses. MedPromptX uses multimodal large language models with visual grounding and few-shot prompting, trained on a new dataset of 6,000 patient records (MedPromptX-VQA) derived from MIMIC-IV and MIMIC-CXR. The system addresses the challenge of incomplete electronic health records by leveraging the knowledge embedded in large language models to interpret lab results. Why it matters: This research advances AI-driven medical diagnostics by integrating diverse data sources and addressing data gaps, potentially leading to quicker and more accurate diagnoses.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.