Join the MICCAI Society Startup Village Virtual Pitch Competition

Convea is a deep-tech spin-out from the University of Lübeck that brings state-of-the-art, foundation-model–driven medical image registration (ConvexAdam) into clinical workflows, with an initial focus on adaptive radiotherapy and longitudinal image analysis. Their universal, OEM-ready registration platform directly addresses a long-standing MICCAI challenge—robust, clinically deployable registration across anatomies and modalities—by translating years of MICCAI-related research and benchmark-winning methods into a scalable product

IgenziAI provides an AI-powered maintenance intelligence platform that keeps ultrasound, X-ray, CT, and MRI systems operational through a multilingual technical assistant, predictive maintenance, and device-log analytics, reducing downtime especially in resource-constrained settings. By ensuring imaging infrastructure remains available, it tackles a critical but underexplored MICCAI-adjacent problem: the reliability and sustainability of the imaging systems on which all medical image computing and AI methods depend.

Belle.ai develops an AI-powered dermatology platform that supports clinicians and researchers with image-based decision support, longitudinal skin-disease monitoring, and EMR-integrated workflows, built on strong expertise in medical image analysis and multimodal AI. The company is already closely embedded in the MICCAI community, with recent MICCAI and workshop papers, and exemplifies the translation of computer vision and medical imaging research into clinically deployed, trustworthy decision-support tools.

Coral Castle Laboratories builds advanced imaging phantoms and physics-informed reconstruction software (“Smearlets”) to characterize imaging systems and enhance radiographic image quality and contrast without hardware changes, starting from LINAC imaging. Their work sits at the interface of computational imaging and medical image computing, offering MICCAI researchers better-quantified imaging systems and improved input data for downstream reconstruction, analysis, and low-dose imaging research

CUBE.AI is an agentic AI platform that automates the full radiology research workflow—from cohort selection and segmentation to modeling and report drafting—so clinicians can run end-to-end imaging studies without coding skills. Closely aligned with MICCAI’s focus on reproducible pipelines and translational impact, it operationalizes segmentation, radiomics, and analysis inside PACS-integrated workflows, helping bridge the gap between published methods and practical radiology research and deployment.

Innoxlabs is developing MedCoder-X, a multimodal AI system that reads clinical notes, imaging and pathology reports, and lab results to generate specific, auditable ICD codes with an explanation of each decision. By fusing text and imaging through cross-attention and concept normalization (UCCR), it tackles a MICCAI-relevant multimodal reasoning problem and links imaging AI directly to downstream documentation, reimbursement, and population-health data quality.

NeuroPath offers an end-to-end neuroimaging pipeline that converts routine T1-weighted MRI into quantitative, explainable biomarkers for dementia diagnosis and monitoring via automated segmentation, radiomics, normative modeling, and graph-based prediction. Rooted in MICCAI 2025 research, it directly addresses core MICCAI themes in quantitative neuroimaging, trustworthy AI, and clinically deployable image-based biomarkers for neurodegenerative disease.

PraxiaTech is building a unified multimodal neurodiagnostic ecosystem that combines MRI/EEG-based AI4NEURO, a multilingual screening app (ManasMitra), and AR/VR cognitive assessment to support early detection and longitudinal monitoring of neurodegenerative disorders such as Alzheimer’s disease. This tightly integrated, explainable multimodal platform aligns with MICCAI topics in neuroimaging, multimodal learning, digital biomarkers, and translational, patient-centric AI for neurological care.

SurgExplore develops an AI-powered Software as a Medical Device that uses surgical video and EHR data to optimize cataract surgery workflows across training, pre-operative planning, and post-operative documentation, with demonstrated potential to reduce serious complications and deliver strong financial ROI for hospitals. Built on a substantial MICCAI publication track record in surgical video analysis and datasets like Cataract-1K, the platform showcases how MICCAI research in computer-assisted interventions can be translated into real-world, scalable surgical AI solutions.