This paper introduces a method for automatically designing convolutional neural network (CNN) architectures tailored for diabetic retinopathy (DR) diagnosis using fundus images. The approach uses k-medoid clustering, PCA, and inter/intra-class variations to optimize CNN depth and width. Validated on datasets including a local Saudi dataset and Kaggle benchmarks, the custom-designed models outperform pre-trained CNNs with fewer parameters.
KAUST and Saudi healthtech company amplifAI health have signed an MoU to develop a new disease detection system. The system will combine amplifAI's AI technology with KAUST's HyplexTM hyperspectral imaging, initially for diabetic foot complications. Clinical trials are planned, with aims to reduce amputations and save Saudi Arabia over 2 billion Riyals annually. Why it matters: This partnership showcases the potential of combining Saudi AI and advanced imaging technologies to address pressing healthcare challenges in the region, particularly diabetes.
MBZUAI researchers co-led a study published in Nature demonstrating that GluFormer, an AI foundation model trained on continuous glucose monitoring (CGM) data, more accurately predicts long-term diabetes and cardiovascular risk than current clinical standards. GluFormer, built on a transformer architecture and trained using NVIDIA AI infrastructure on over 10 million CGM measurements, forecasts individual health risks using short-term glucose dynamics. In a 12-year follow-up, the model captured 66% of new-onset diabetes cases and 69% of cardiovascular-death events in its highest-risk group, outperforming established CGM-derived metrics across 19 external cohorts. Why it matters: The development of GluFormer represents a significant advancement in personalized healthcare, enabling proactive and individualized health strategies through the analysis of dynamic glucose data.
This paper introduces a domain generalization (DG) method for Diabetic Retinopathy (DR) classification that maximizes mutual information using a large pretrained model. The method aims to address the challenge of domain shift in medical imaging caused by variations in data acquisition. Experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art techniques, achieving a 5.25% improvement in average accuracy.
KAUST's Laboratory of Stem Cells and Diseases, led by Assistant Professor Antonio Adamo, uses induced pluripotent stem cells (iPSCs) to model diseases like diabetes. The lab employs a reprogramming technique to revert patient fibroblasts into iPSCs, enabling the study of disease progression in vitro. Adamo's research focuses on enzymes and disregulated transcriptional/epigenetic mechanisms to understand disease onset. Why it matters: This research contributes to regenerative medicine and offers insights into metabolic diseases relevant to the GCC region.
KAUST postdoctoral fellow Elisabetta Fiacco won the Best Poster Prize at the Spetses Summer School 2018 on Chromatin and Metabolism for her research on the role of carbohydrate-responsive element binding protein (ChRBP) during the onset of type 2 diabetes. Fiacco's research at KAUST, under Assistant Professor Antonio Adamo, uses human induced pluripotent stem cells to understand the epigenetic and transcriptional mechanisms dysregulated in type 2 diabetes. She also gave a 15-minute oral presentation on her work at the event, which gathered over 80 participants from top global universities. Why it matters: This recognition highlights KAUST's contribution to cutting-edge research in regenerative medicine and the study of metabolic disorders prevalent in the region.
Researchers from KAUST, King Faisal Specialist Hospital, and collaborators have developed a new method to predict cardiometabolic disease risk in underrepresented ethnic populations using genetic information and public databases. The study focused on Arab communities and created a framework to determine polygenic scores for more accurate heart disease prediction. The framework was validated using records of over 5,000 Arab patients, demonstrating that genetic risk complements conventional risk factors. Why it matters: This research addresses a critical gap in genomic data for non-European populations, potentially leading to more effective and personalized healthcare strategies in the Arab world and beyond.
KAUST researchers are studying ancient supervolcanoes, like the Toba eruption 75,000 years ago, to understand current and future climate conditions. Volcanic eruptions serve as natural experiments that push the climate system to its limits, helping scientists understand climate's physical mechanisms. Research shows that volcanic eruptions delayed global warming by about 30% starting from 1850. Why it matters: Understanding the impact of volcanic activity on climate change can improve predictions of future global warming, particularly in regions like the Middle East which are strongly affected by volcanic events.