Skip to main content

Research Intelligence

ScienceView
Artificial Intelligence

AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.

| Source: Nature medicine

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using multimodal data with color fundus photographs and optical coherence tomography scans from 1,843 genetically confirmed patients (3,376 eyes) across China, South Korea and Poland. The top-5 prediction accuracy was 0.904 (95% confidence interval (CI): 0.896-0.912) and 0.856 (95% CI: 0.850-0.863) for internal and external validation, respectively. We conducted a randomized controlled trial with 300 participants with suspected IRD randomized 1:1 to either Retina4IRD-assisted specialist arm or specialist-only arm. Of these, 295 participants (median age 33 years, 114 (38.6%) females) with available next-generation sequencing reports were included in the final analysis. The primary outcome was met: top-5 genetic accuracy was significantly higher in the Retina4IRD-assisted specialist arm versus the specialist-only arm (88.5% versus 67.3%, P < 0.001). For secondary endpoints, top-1 to top-4 accuracies all favored the Retina4IRD-assisted specialist arm, with top-1 accuracy of 37.8% versus 22.4% and top-4 accuracy of 81.8% versus 53.1%, respectively. Post hoc analyses demonstrated that, with Retina4IRD assistance, clinicians made better management decisions, and the composite downstream management score indicated significantly higher scores relative to the control group (37.7 versus 28.5, P < 0.001). Our study shows that Retina4IRD is a CDSS tool prior to genetic testing and aligns with clinical workflow for patients with suspected IRDs. ClinicalTrials.gov identifier: NCT06839170 .

Read the original source →

Related Topics

Related Stories

Artificial Intelligence

Protein resource allocation across subcellular compartments revealed by large-scale absolute quantitative proteomics in yeast.

Subcellular compartmentalization within complex membrane-bound organelles is a hallmark of eukaryal life, enabling metabolic specialization through spatial segregation. Here, we compiled a dataset encompassing 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning, quantitative correlations among protein abundance, resource allocation, and cellular phenotypes wer

Continue reading
Artificial Intelligence

Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta.

Built environments provide durable evidence for how ancient settlements were organized, expanded, and transformed over time. Yet quantitative approaches to documenting architectural variability are generally labor-intensive, limited to individual structures, or dependent on aggregate volumetric estimates. We present a scalable, machine-learning-based framework that shifts the unit of analysis to the individual building stone and combines two complementary datasets: low-altitude drone imagery of

Continue reading
Artificial Intelligence

Data-driven discovery and rapid, direct synthesis of MXenes.

MXenes are a rapidly expanding family of two-dimensional transition metal carbides and nitrides, yet scalable synthesis routes remain limited and historical reports suggest a broader, largely unexplored chemical space. We used machine learning-assisted database mining to uncover hidden multilayer MXenes. Screening reveals 33 previously synthesized but unrecognized MXene candidates. Guided by these findings, we synthesized seven multilayer MXenes using a rapid, scalable self-propagating high-temp

Continue reading
Artificial Intelligence

Intrinsic gestational timing governs human cerebellar development after preterm birth.

Intrinsic programs shape brain maturation; however, which of these programs are perturbed by prematurity as well as the molecular pathways involved remain unknown. The human cerebellum serves as a paradigm of extrauterine development-its accelerated growth and circuit formation align with the third trimester, a period disrupted by preterm delivery. Using multimodal datasets encompassing in vivo neuroimaging and neurodevelopmental outcomes with postmortem spatial-transcriptomic and histopathologi

Continue reading
Artificial Intelligence

CX3CL1 (fractalkine)-CX3CR1 interface drives CD8⁺ T cell-induced neurodegeneration in human brain organoids.

Immune-neuron interactions play a critical role in neuroinflammatory and neurodegenerative diseases, such as Alzheimer's disease (AD), but how CD8⁺ T cells contribute to brain pathology remains poorly understood. Amyloid-β (Aβ), a hallmark of AD, is known to trigger neuronal stress and inflammation. Here, we established a human cortical organoid (hCO) model to investigate CD8⁺ T cell responses to Aβ-induced neurodegenerative stress. Aβ exposure increased neu

Continue reading