{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_columbo","slug":"columbo","name":"CoLumbo","type":"product","url":"https://columbo.me","page_url":"https://unfragile.ai/columbo","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_columbo__cap_0","uri":"capability://image.visual.spinal.mri.pathology.detection.and.flagging","name":"spinal mri pathology detection and flagging","description":"Processes DICOM-formatted spinal MRI scans through a deep learning CNN model trained on large annotated spine imaging datasets to automatically detect and spatially localize common pathologies (disc herniation, stenosis, spondylolisthesis, fractures). The system generates confidence scores per finding and flags high-confidence anomalies for radiologist review, reducing manual scan review time by filtering normal or low-risk studies. Architecture likely uses multi-slice 3D convolution with attention mechanisms to capture anatomical context across vertebral levels.","intents":["Reduce time spent on routine spinal MRI interpretation by auto-flagging abnormal cases","Improve consistency of pathology detection across multiple radiologists and reading sessions","Prioritize urgent findings (fractures, severe stenosis) for expedited radiologist triage","Generate structured preliminary reports that radiologists can validate and refine"],"best_for":["Radiology departments with high-volume spinal MRI workflows (50+ studies/day)","Spine specialist clinics seeking to reduce radiologist fatigue and read times","Hospital systems implementing AI-assisted diagnostic workflows with human-in-the-loop oversight"],"limitations":["Model accuracy and sensitivity/specificity metrics not publicly disclosed — regulatory clearance status unclear, limiting clinical confidence","Likely requires high-quality DICOM input with standard imaging protocols; non-standard sequences or poor image quality may degrade detection performance","No evidence of multi-institutional validation studies published in peer-reviewed literature","Requires radiologist oversight for final diagnosis — cannot replace human interpretation, only augment it"],"requires":["PACS system compatible with DICOM export/import (HL7 or DICOM API integration)","Spinal MRI studies in standard DICOM format (T1, T2, STIR sequences)","Licensed radiologist or spine specialist to validate AI-generated findings","Likely requires FDA 510(k) clearance or equivalent regulatory approval for clinical deployment"],"input_types":["DICOM image series (multi-slice 3D volumes)","Spinal MRI protocols (T1-weighted, T2-weighted, STIR)"],"output_types":["Structured pathology report (JSON or HL7)","Confidence scores per finding (0-1 scale)","Spatial coordinates/segmentation masks for flagged lesions","Priority triage classification (normal, low-risk, high-risk)"],"categories":["image-visual","medical-imaging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_columbo__cap_1","uri":"capability://automation.workflow.pacs.integrated.automated.reporting.workflow","name":"pacs-integrated automated reporting workflow","description":"Integrates with hospital PACS systems via DICOM API or HL7 messaging to automatically retrieve spinal MRI studies, process them through the detection model, and generate structured preliminary reports that populate radiology information systems (RIS). The system likely uses a message queue (e.g., AMQP, Kafka) to handle asynchronous processing of high-volume studies and maintains audit logs for regulatory compliance. Reports are formatted as HL7 or FHIR-compliant structured data that radiologists can import, review, and electronically sign.","intents":["Eliminate manual data entry and report templating by auto-generating structured preliminary reports","Reduce turnaround time from scan acquisition to radiologist review by 30-40%","Maintain full audit trail and regulatory compliance (HIPAA, FDA 21 CFR Part 11) for medical device workflows","Enable seamless integration into existing hospital IT infrastructure without custom middleware"],"best_for":["Large hospital systems with mature PACS infrastructure and HL7/DICOM API support","Radiology departments with 100+ daily spinal MRI studies seeking workflow automation","Healthcare IT teams implementing FDA-regulated AI-assisted diagnostic systems"],"limitations":["PACS integration complexity varies by vendor (GE, Philips, Siemens) — may require custom adapters or middleware","Asynchronous processing introduces latency (likely 2-5 minutes per study) — not suitable for real-time emergency triage","Requires robust error handling and fallback mechanisms if AI model fails or produces low-confidence results","Audit logging and compliance overhead adds operational complexity and storage requirements"],"requires":["PACS system with DICOM API or HL7 v2/FHIR messaging capability","RIS (Radiology Information System) with structured report import functionality","Network connectivity and firewall rules for secure DICOM/HL7 communication","HIPAA-compliant infrastructure (encryption, access controls, audit logging)","Likely requires IT governance approval and regulatory compliance review before deployment"],"input_types":["DICOM study metadata (patient ID, study date, modality)","HL7 order messages from RIS"],"output_types":["HL7 v2 or FHIR-formatted diagnostic reports","Structured data (JSON) with findings, confidence scores, and recommendations","Audit logs (timestamps, user actions, model outputs)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_columbo__cap_2","uri":"capability://safety.moderation.radiologist.assisted.finding.validation.and.report.refinement","name":"radiologist-assisted finding validation and report refinement","description":"Provides a web or desktop interface where radiologists review AI-generated findings, adjust confidence thresholds, add clinical context, and electronically sign final reports. The system tracks radiologist edits and model predictions side-by-side, enabling feedback loops to retrain or fine-tune the model on institutional data. Implements role-based access control (radiologist, attending, administrator) and maintains immutable audit trails for regulatory compliance. Likely uses a collaborative annotation UI with keyboard shortcuts and voice dictation for efficient report finalization.","intents":["Validate AI-generated findings and correct false positives/negatives before report finalization","Add clinical context, differential diagnoses, and recommendations that the AI model cannot generate","Collect ground-truth labels from radiologist reviews to improve model accuracy over time","Maintain full regulatory compliance and liability protection through documented human oversight"],"best_for":["Radiologists and spine specialists who need to review and validate AI findings before clinical use","Radiology departments seeking to improve model performance through continuous feedback and retraining","Healthcare organizations requiring documented human-in-the-loop oversight for FDA-regulated AI systems"],"limitations":["Radiologist review time may offset some of the AI efficiency gains if interface is poorly designed or requires excessive clicking","Feedback loop for model retraining requires careful data governance to avoid bias or overfitting to institutional preferences","No evidence of published studies on radiologist acceptance, usability, or actual time savings in clinical practice","Requires radiologist training and change management to adopt new workflow"],"requires":["Web browser or desktop application with DICOM viewer capability","User authentication and role-based access control (RBAC) system","Database to store radiologist edits, model predictions, and audit logs","Likely requires HIPAA-compliant hosting and data encryption"],"input_types":["DICOM images and AI-generated findings (JSON)","Radiologist annotations and edits (text, confidence adjustments)"],"output_types":["Finalized diagnostic report (HL7, PDF, or structured text)","Audit log with timestamps and user actions","Feedback data for model retraining (ground-truth labels)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_columbo__cap_3","uri":"capability://planning.reasoning.multi.pathology.confidence.scoring.and.risk.stratification","name":"multi-pathology confidence scoring and risk stratification","description":"Generates per-finding confidence scores (0-1 scale) for multiple spinal pathologies (disc herniation, stenosis, spondylolisthesis, fractures, etc.) and aggregates them into a study-level risk stratification (normal, low-risk, moderate-risk, high-risk). The scoring likely uses Bayesian uncertainty quantification or ensemble methods (multiple model predictions) to estimate model confidence rather than raw softmax probabilities. High-risk studies are automatically prioritized for radiologist review, enabling triage-based workflow optimization.","intents":["Automatically prioritize urgent findings (fractures, severe stenosis) for expedited radiologist review","Quantify model uncertainty to help radiologists distinguish high-confidence from borderline findings","Enable risk-based workflow optimization (normal studies reviewed faster, high-risk studies get senior radiologist review)","Provide transparency into model decision-making for regulatory and clinical validation"],"best_for":["High-volume radiology departments seeking to optimize radiologist allocation based on study complexity","Spine specialists who need to triage urgent findings (fractures, cord compression) for immediate attention","Healthcare organizations implementing risk-stratified diagnostic workflows"],"limitations":["Confidence scores are model-dependent and may not correlate with actual diagnostic accuracy — miscalibrated models can produce overconfident predictions","Risk stratification thresholds are likely institution-specific and require validation on local data","No published evidence of how well confidence scores predict radiologist agreement or clinical outcomes","Ensemble methods add computational overhead and latency"],"requires":["Deep learning model with uncertainty quantification (Bayesian, ensemble, or Monte Carlo dropout)","Calibration dataset to validate confidence score accuracy on institutional data","Threshold tuning based on clinical requirements (sensitivity vs. specificity tradeoffs)"],"input_types":["DICOM spinal MRI studies","Model predictions (logits or probabilities for each pathology class)"],"output_types":["Per-finding confidence scores (0-1)","Study-level risk classification (normal, low, moderate, high)","Uncertainty estimates (confidence intervals or Bayesian credible intervals)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_columbo__cap_4","uri":"capability://image.visual.anatomical.landmark.detection.and.localization","name":"anatomical landmark detection and localization","description":"Automatically identifies and localizes vertebral levels (C1-L5), intervertebral discs, spinal cord, and nerve roots in 3D space using semantic segmentation or keypoint detection networks. This enables spatial grounding of pathology findings (e.g., 'L4-L5 disc herniation' rather than generic 'disc herniation') and supports automated measurement of stenosis severity or disc height. Architecture likely uses U-Net or similar encoder-decoder networks with 3D convolutions to preserve volumetric context.","intents":["Automatically localize findings to specific vertebral levels and anatomical structures","Enable quantitative measurements (stenosis degree, disc height, canal diameter) for objective severity assessment","Generate anatomically-grounded reports that reference specific spinal levels","Support multi-level pathology detection and comparison across multiple studies"],"best_for":["Radiology departments requiring precise anatomical localization for surgical planning or follow-up studies","Spine surgeons who need quantitative measurements of stenosis or disc degeneration","Research studies requiring standardized anatomical annotations for large imaging datasets"],"limitations":["Anatomical variation (scoliosis, fusion hardware, post-surgical changes) can degrade landmark detection accuracy","Requires high-quality imaging with clear vertebral boundaries — poor image quality or metal artifacts may cause failures","No published evidence of landmark detection accuracy compared to manual radiologist annotation","Adds computational overhead (likely 1-2 seconds per study) for 3D segmentation"],"requires":["3D CNN architecture trained on annotated spinal MRI datasets with vertebral level labels","High-resolution DICOM input with adequate slice thickness and spacing","Sufficient GPU memory for 3D convolution operations"],"input_types":["DICOM spinal MRI volumes (T1, T2, or STIR sequences)"],"output_types":["Segmentation masks for vertebral bodies, discs, spinal cord (3D binary or multi-class masks)","Keypoint coordinates for vertebral landmarks (center, superior/inferior endplates)","Quantitative measurements (disc height, canal diameter, stenosis percentage)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_columbo__cap_5","uri":"capability://image.visual.comparative.study.analysis.and.interval.change.detection","name":"comparative study analysis and interval change detection","description":"Compares current spinal MRI studies with prior imaging (weeks to years prior) to detect interval changes in pathology severity, new findings, or resolution of previously identified abnormalities. Uses image registration (rigid or deformable) to align current and prior studies in 3D space, then applies difference detection algorithms to highlight regions of change. Enables longitudinal tracking of degenerative disc disease progression, post-surgical healing, or treatment response.","intents":["Automatically detect interval changes in spinal pathology severity between studies","Track progression or improvement of degenerative disc disease or post-surgical healing","Highlight new findings that may have been missed in prior studies","Generate comparative reports that quantify change over time"],"best_for":["Radiology departments managing patients with chronic spinal conditions requiring serial imaging","Spine surgeons tracking post-operative healing and fusion consolidation","Research studies analyzing natural history of degenerative disc disease or treatment outcomes"],"limitations":["Image registration accuracy depends on imaging protocol consistency — different sequences, slice thickness, or patient positioning can degrade alignment","Requires prior studies to be available in PACS and properly linked to current study (metadata matching)","No published evidence of change detection sensitivity/specificity compared to radiologist assessment","Computational overhead for image registration (likely 5-10 seconds per comparison)"],"requires":["Prior spinal MRI study available in PACS with correct patient/study linkage","Image registration algorithm (rigid or deformable) trained on spinal anatomy","Difference detection model trained on paired imaging datasets"],"input_types":["Current DICOM spinal MRI study","Prior DICOM spinal MRI study (weeks to years prior)"],"output_types":["Registered image pairs (aligned 3D volumes)","Difference maps highlighting regions of change","Quantitative metrics (change in disc height, stenosis degree, lesion volume)","Comparative report with interval change summary"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_columbo__cap_6","uri":"capability://data.processing.analysis.structured.data.extraction.and.standardized.reporting","name":"structured data extraction and standardized reporting","description":"Converts AI-generated findings and radiologist-validated annotations into standardized structured data formats (HL7 FHIR, DICOM SR, or proprietary JSON) that can be ingested by downstream clinical systems (EHR, surgical planning software, research databases). Uses schema-based extraction with predefined ontologies for spinal pathologies, severity grades, and anatomical locations. Enables automated population of structured fields in EHR systems and supports clinical decision support rules (e.g., 'if severe stenosis at L4-L5, flag for neurosurgery consultation').","intents":["Export findings in standardized formats compatible with EHR and surgical planning systems","Enable automated clinical decision support rules based on structured findings","Support research data aggregation and outcomes tracking across multiple institutions","Reduce manual data entry and transcription errors in clinical workflows"],"best_for":["Hospital systems with mature EHR infrastructure seeking to integrate AI findings into clinical workflows","Research networks aggregating spinal imaging data across multiple institutions","Surgical planning systems requiring structured input about stenosis severity and anatomical location"],"limitations":["Schema design requires careful ontology development to capture clinical nuance without over-simplification","Mapping between AI outputs and standardized codes (SNOMED CT, LOINC) requires domain expertise and maintenance","EHR integration complexity varies by vendor — may require custom middleware or HL7 adapters","No published evidence of data quality or clinical utility of structured extraction"],"requires":["Standardized schema (FHIR, DICOM SR, or proprietary) for spinal pathology representation","Mapping tables between AI findings and clinical ontologies (SNOMED CT, LOINC)","EHR API or HL7 interface for structured data import"],"input_types":["AI-generated findings (JSON with pathology types, locations, severity scores)","Radiologist annotations and edits"],"output_types":["HL7 FHIR DiagnosticReport resources","DICOM Structured Report (SR) objects","Proprietary JSON with standardized field names and codes","EHR-compatible structured data for direct import"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["PACS system compatible with DICOM export/import (HL7 or DICOM API integration)","Spinal MRI studies in standard DICOM format (T1, T2, STIR sequences)","Licensed radiologist or spine specialist to validate AI-generated findings","Likely requires FDA 510(k) clearance or equivalent regulatory approval for clinical deployment","PACS system with DICOM API or HL7 v2/FHIR messaging capability","RIS (Radiology Information System) with structured report import functionality","Network connectivity and firewall rules for secure DICOM/HL7 communication","HIPAA-compliant infrastructure (encryption, access controls, audit logging)","Likely requires IT governance approval and regulatory compliance review before deployment","Web browser or desktop application with DICOM viewer capability"],"failure_modes":["Model accuracy and sensitivity/specificity metrics not publicly disclosed — regulatory clearance status unclear, limiting clinical confidence","Likely requires high-quality DICOM input with standard imaging protocols; non-standard sequences or poor image quality may degrade detection performance","No evidence of multi-institutional validation studies published in peer-reviewed literature","Requires radiologist oversight for final diagnosis — cannot replace human interpretation, only augment it","PACS integration complexity varies by vendor (GE, Philips, Siemens) — may require custom adapters or middleware","Asynchronous processing introduces latency (likely 2-5 minutes per study) — not suitable for real-time emergency triage","Requires robust error handling and fallback mechanisms if AI model fails or produces low-confidence results","Audit logging and compliance overhead adds operational complexity and storage requirements","Radiologist review time may offset some of the AI efficiency gains if interface is poorly designed or requires excessive clicking","Feedback loop for model retraining requires careful data governance to avoid bias or overfitting to institutional preferences","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=columbo","compare_url":"https://unfragile.ai/compare?artifact=columbo"}},"signature":"7Bprs9FeT7fmd3MGAO2HFZyxDyzeZ5B1+iMenoMDx83+WJ23Il9hV0FJitItra5vIZ3CfdQrv7d3pO0WliB5DA==","signedAt":"2026-06-22T10:30:34.436Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/columbo","artifact":"https://unfragile.ai/columbo","verify":"https://unfragile.ai/api/v1/verify?slug=columbo","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}