Imaging finding intelligence
Detection, segmentation, classification, and Malignancy Index across lesions, with longitudinal matching across priors.
A living record, not a static archive
Hiveomics brings studies, priors, clinical history, and guideline logic together in a living longitudinal record that helps clinicians translate change over time into informed, guideline-based action.
Nexus platform
Nexus keeps each lesion connected to its studies, priors, measurements, Malignancy Index, lesion history, and guideline context so clinicians can review change over time in one place.
Nexus identifies clinically relevant lesions on each study and attaches them to the patient's oncology record for clinician review.
Measurements, interval change, radiomic context, and Malignancy Index become structured signals for oncology review.
The same lesion stays connected across priors, follow-ups, and treatment intervals, turning separate scans into one longitudinal history.
Guideline logic and lesion history come together as cited, reviewable next steps for clinician confirmation.
Where the technology applies
The same finding intelligence and longitudinal record extend across anatomy, modality, and the document trail that surrounds every patient.
Detection, segmentation, classification, and Malignancy Index across lesions, with longitudinal matching across priors.
Lymph nodes (axillary, mediastinal, abdominal, inguinal), liver and kidney lesions, abdominal organs — with measurements and structured outputs.
117 organ segmentations and automated clinical measurements that put any finding in anatomical context.
PET-CT fusion with metabolic measurements (SUV-max, SUV-mean) bound to the same finding as the structural read.
Scanned reports turned into structured FHIR with LOINC, SNOMED, and ICD-10 codes — prior history becomes searchable, not text. Runs on local LLMs, no external API calls.
Clinical guidelines formalized into reasoning that proposes next steps with citations the clinician can confirm.
Digital Twin
Hiveomics extracts clinical semantics from unstructured, cluttered data and organizes them into an evolving patient-level graph — imaging, history, pathology, genomics, labs, follow-up, all in one updating model of the patient.
The platform around it
Nexus is built for real clinical deployment, integration, and governance. Here's what's under the hood.
Compose AI algorithm pipelines as directed acyclic graphs with a visual editor. No code required to build complex processing flows.
Volume rendering with segmentation overlays, mask toggles, and opacity controls. Review AI findings directly inside the platform.
Connect to existing radiology infrastructure via DICOM Query/Retrieve (C-FIND, C-MOVE). No rip-and-replace.
Fully containerized and air-gapped capable. Zero external dependencies. Patient data never leaves the facility.
Multiple organizations and sites with role-based access control, department-level isolation, and centralized administration.
Immutable action history across the platform. Designed for regulatory requirements from the ground up.
Built for the team
Each role on the team gets a different cut of the same record — the read in context, the routed case already characterized, the follow-up that closes.
Each finding arrives with its priors, measurements, and trend lines already in view, so attention goes to the case, not to assembling it.
Routed cases arrive already assembled — lesion, priors, staging, and metabolic context — so the visit is about the decision, not the chart hunt.
A defensible benign-versus-malignant read before picking up a scalpel, with the reasoning behind it shown, not hidden.
Nobody drops out of the loop — overdue cases surface on their own, unreachable patients leave a record instead of a hole, and the cohort stays accountable.
AI deployment you can defend: concordance over time, an audit trail on every action, and governance under your control.
A platform that fits your infrastructure instead of fighting it — on-site, air-gapped, PACS-native, with no external API calls.
A structured, longitudinal patient dataset you couldn't build by hand — measurements, radiomics, and time-aligned priors across the cohort.