Alpenglow's Summit AI Unlocks Disease Insights in Full 3D

SEATTLE, WA – May 05, 2026 – Alpenglow Biosciences today announced the launch of Summit AI, a multimodal artificial intelligence platform poised to shift the paradigm of tissue analysis from flat, two-dimensional slices to comprehensive 3D volumes. By converting complex imaging data into quantitative spatial readouts, the platform aims to accelerate drug development and uncover biological insights previously hidden within the intricate architecture of human tissue.

Initially focused on inflammatory skin disease, Summit AI is designed to help researchers and pharmaceutical teams interpret tissue organization, cellular relationships, and disease states with a level of depth and context that has long eluded conventional methods. This move into the third dimension represents a significant step forward in the field of digital pathology and spatial biology.

A New Dimension in Tissue Analysis

For over a century, pathology has relied on analyzing thin, two-dimensional sections of tissue mounted on glass slides. While foundational to modern medicine, this method inherently suffers from limitations. Analyzing a 2D slice to understand a 3D structure is like trying to comprehend an entire building by looking at a single, isolated floor plan. Complex, meandering structures like neural networks or blood vessels can appear disconnected, and critical but rare cellular events can be missed entirely due to sampling bias.

Alpenglow's approach, powered by its Aurora™ 3D spatial biology platform, digitizes intact tissue samples without slicing them. Summit AI then takes over, analyzing these whole tissue volumes. Instead of a fragmented picture, it provides a complete, quantitative map of the tissue's microenvironment. The platform integrates this 3D data with other information, such as 2D readouts and spatial transcriptomics, creating a rich, multimodal view of the biology at play.

This holistic analysis allows for a more accurate understanding of tissue architecture. By preserving the full biological context, researchers can see precisely how different cells and structures are organized and interact within their native environment, a crucial factor in understanding both healthy function and disease progression.

Revolutionizing Drug Development for Skin Disease

The platform's initial focus is inflammatory skin disease, a field where full biopsy context is particularly critical. Conditions like atopic dermatitis and hidradenitis suppurativa involve complex interactions between immune cells, nerves, and structural components across multiple skin layers. Conventional 2D sections often fail to capture the full extent of this interplay.

For example, research has shown that the density of neural networks in skin biopsies can vary dramatically from one 2D slice to the next. A 3D analysis, however, provides a stable, accurate measurement of nerve volume across the entire sample. Summit AI is engineered to perform this type of analysis, enabling researchers to better study neuroimmune relationships and structural remodeling.

By generating more reliable and comparable biological measurements, the platform can significantly impact drug development workflows. It promises to accelerate the identification and validation of new biomarkers, improve the stratification of patients for clinical trials, and help scientists identify more effective drug targets. The goal is to move from a process of inference and approximation to one of direct, quantitative measurement, ultimately reducing the time and cost of bringing new therapies to market.

Building Trust in Medical AI

As artificial intelligence becomes more prevalent in healthcare, questions of reliability and transparency are paramount. Alpenglow is addressing this head-on with Summit AI. The platform is powered by an AI foundation model trained on a vast dataset comprising thousands of human tissue samples, all generated through Alpenglow's own 3D imaging technology. This provides the model with a deep and consistent understanding of complex biological structures.

Crucially, Summit AI includes built-in uncertainty metrics. This feature allows the AI to flag regions of an image or dataset where its confidence in the analysis is low. This doesn't represent a failure of the system but rather a key feature for building trust. It intelligently guides human experts to the areas that require review, creating a collaborative workflow between the pathologist and the AI. This allows research teams to prioritize high-confidence signals for downstream analysis while ensuring that ambiguous findings receive the necessary expert scrutiny.

Navigating a Competitive Spatial Biology Market

Alpenglow enters a dynamic and competitive spatial biology market. Companies like Akoya Biosciences and Lunaphore have made significant strides in high-plex immunofluorescence on 2D slides, while digital pathology leaders like Visiopharm offer powerful AI for analyzing 2D whole-slide images. However, many of these solutions still operate within the 2D or pseudo-3D (reconstructed from serial sections) domain.

Summit AI’s key differentiator is its foundation in true, whole-volume 3D imaging and analysis. While emerging competitors like Stellaromics are also entering the 3D spatial transcriptomics space, Alpenglow’s integrated system—combining its Aurora™ imaging hardware with the Summit AI analysis platform—offers a unified solution designed to move from intact tissue to quantitative insight.

The platform's outputs are built for direct biological interpretation, providing spatial representations of tissue organization, integrated disease signatures, and cohort-level comparisons. This comprehensive approach is designed to support the entire translational research pipeline, from basic discovery to late-stage clinical development.

“Researchers and drug development teams need more than isolated readouts or fragmented workflows,” stated Nicholas Reder, MD, MPH, CEO and Founder of Alpenglow Biosciences, in the company's announcement. “They need a way to move from image review to quantitative interpretation across the full sample, while preserving the biological context that matters.”