Precise localization of perfusion deficits in diffusion-weighted MRI (DWI) is critical for acute ischemic stroke management. However, existing deep learning methods typically produce discrete binary masks, failing to capture the continuous nature of ischemic…
Continue Reading
Artificial Intelligence has revolutionised critical care for common conditions. Yet, rare conditions in the intensive care unit (ICU), including recognised rare diseases and low-prevalence conditions in the ICU, remain underserved due to data scarcity…
Continue Reading
Vascular invasion assessment is critical for surgical planning in pancreatic ductal adenocarcinoma (PDAC). Current CT-based assessments often rely on radiologists’ subjective 2D interpretations, which may not capture the continuous, three-dimensional tumor–vessel interactions and multiple…
Continue Reading
Congenital and acquired heart disease affects approximately 1% of children worldwide, and right ventricular (RV) dysfunction is a common and complex manifestation in conditions such as congenital heart disease, pulmonary hypertension, and prematurity. Accurate…
Continue Reading
Remote photoplethysmography (rPPG) offers a non-invasive means of estimating heart rate in telemedicine settings. Yet its reliability remains uncertain due to the limited diversity and ecological validity of existing benchmark datasets. In this work,…
Continue Reading
Accurate detection of lung cancer from Computed Tomography (CT) scans is vital for improving patient survival but remains challenging for deep learning models, which struggle with scale variations of pulmonary nodules and the complex…
Continue Reading
Lung cancer remains the top cause of cancer death, demanding consistent decisions. This clinically oriented review synthesizes computer-aided diagnosis across classical imaging, machine learning, and deep learning, emphasizing bedside-proven advances: multimodal CT/PET–clinical fusion; small-data…
Continue Reading
Delirium affects 50% of hospitalized older adults and 80% of ICU patients, yet detection rates are as low as 20–30% with traditional methods. Natural language processing (NLP) offers potential for automated detection from clinical…
Continue Reading
Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural…
Continue Reading
We developed a deep learning model trained on over two million ultrasound images from 78,531 pregnancies from Australia, India, and the UK to estimate gestational age (GA) directly from any fetal ultrasound image, regardless…
Continue Reading
