Latest AI and machine learning research in pulmonology for healthcare professionals.
Tuberculosis (TB) is a infectious global health challenge. Chest X-rays are a standard method for TB screening, yet many countries face a critical shortage of radiologists capable of interpreting these images. Machine learning offers an alternative, as it can automate tasks such as disease diagnosis, and report generation. However, traditional approaches rely on task-specific models, which canno...
Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - particularly granulomas, making it challenging to clinically differentiate them. Our research leverages 3D CTE scans, computer vision, and machine learning to improve this differentiation to avoid harmful treatment mismanagement such as unnecessary a...
Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with the highest burden in low- and middle-income countries. In...
AI models for lung cancer screening are limited by data scarcity, impacting generalizability and clinical applicability. Generative models address t...
The lungs are the essential organs of respiration, and this system is significant in the carbon dioxide and exchange between oxygen that occurs in h...
Vision-language models have proven to be of great benefit for medical image analysis since they learn rich semantics from both images and reports. P...
Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibros...
Detection and classification of pulmonary nodules is a challenge in medical image analysis due to the variety of shapes and sizes of nodules and the...
Medical imaging spans diverse tasks and modalities which play a pivotal role in disease diagnosis, treatment planning, and monitoring. This study pr...
Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list o...
Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we int...
Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models us...
Lung cancer is the second most common cancer and the leading cause of cancer-related deaths worldwide. Survival largely depends on tumor stage at di...
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the p...
We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI. Cirrhosis is the severe sca...
In critical care settings, where precise and timely interventions are crucial for health outcomes, evaluating disparities in patient outcomes is ess...
Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accur...
With the rapid development of artificial intelligence (AI), AI-assisted medical imaging analysis demonstrates remarkable performance in early lung c...
Until now, in the wake of the COVID-19 pandemic in 2019, lung diseases, especially diseases such as lung cancer and chronic obstructive pulmonary di...
Patient-specific modeling is a valuable tool in cardiovascular disease research, offering insights beyond what current clinical equipment can measur...