{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_proscia","slug":"proscia","name":"Proscia","type":"product","url":"https://www.proscia.com","page_url":"https://unfragile.ai/proscia","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_proscia__cap_0","uri":"capability://pathology.gigapixel.whole.slide.image.processing","name":"gigapixel whole slide image processing","description":"Efficiently processes and analyzes gigapixel-scale whole slide images (WSI) from digital pathology scanners without requiring manual tiling or preprocessing. Handles the computational complexity of high-resolution histopathology images at scale.","intents":["I need to analyze large digital pathology scans without manual image preparation","I want to process hundreds of whole slide images in batch without performance degradation","I need to work with gigapixel images that are too large for standard image analysis tools"],"best_for":["academic medical centers","reference laboratories","research institutions","large hospital systems"],"limitations":["requires WSI scanner infrastructure","requires significant storage capacity","processing time varies with image complexity"],"requires":["whole slide image scanner","digital pathology infrastructure","cloud or on-premise computing resources"],"input_types":["whole slide images (WSI format)","histopathology slide scans"],"output_types":["segmentation masks","quantitative measurements","analysis reports"],"categories":["pathology","medical imaging","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_1","uri":"capability://pathology.tissue.segmentation.and.classification","name":"tissue segmentation and classification","description":"Automatically identifies and segments different tissue types, cellular structures, and pathological features within whole slide images using deep learning models. Classifies tissue regions into categories relevant to diagnostic assessment.","intents":["I need to automatically identify tumor vs. normal tissue regions in pathology slides","I want to segment specific tissue compartments for quantitative analysis","I need to classify cellular and tissue structures without manual annotation"],"best_for":["pathologists","research scientists","academic medical centers","reference labs"],"limitations":["accuracy varies by tissue type and stain","requires training data for new tissue types","may require pathologist validation"],"requires":["whole slide images","appropriate tissue staining","validated AI models for specific tissue types"],"input_types":["whole slide images","histopathology slides"],"output_types":["segmentation masks","tissue classification maps","region coordinates"],"categories":["pathology","medical imaging","AI analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_2","uri":"capability://pathology.quantitative.histological.feature.extraction","name":"quantitative histological feature extraction","description":"Extracts and quantifies measurable histological features such as cell density, nuclear morphology, glandular architecture, and tissue composition from whole slide images. Provides numerical metrics for objective assessment of tissue characteristics.","intents":["I need objective measurements of tumor cellularity and grade","I want to quantify stromal composition and immune cell infiltration","I need reproducible metrics for tissue analysis across multiple slides"],"best_for":["research scientists","pathologists","academic institutions","clinical researchers"],"limitations":["feature extraction depends on image quality and staining consistency","requires domain expertise to interpret metrics","some features may require manual validation"],"requires":["high-quality whole slide images","appropriate tissue preparation and staining","domain knowledge for interpretation"],"input_types":["whole slide images","segmented tissue regions"],"output_types":["numerical metrics","quantitative reports","statistical summaries","CSV/JSON data"],"categories":["pathology","research","data analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_3","uri":"capability://pathology.diagnostic.decision.support.generation","name":"diagnostic decision support generation","description":"Analyzes whole slide images and generates AI-assisted recommendations and insights to support pathologist decision-making. Provides contextual analysis to highlight areas of interest and potential diagnostic considerations without replacing pathologist judgment.","intents":["I need AI-assisted recommendations to guide my diagnostic review","I want the system to highlight suspicious or significant regions for my attention","I need decision support that reduces diagnostic variability and improves accuracy"],"best_for":["pathologists","diagnostic laboratories","academic medical centers","reference labs"],"limitations":["positioned as decision support, not autonomous diagnosis","requires pathologist validation","regulatory status limits clinical deployment","slower adoption than marketed"],"requires":["whole slide images","pathologist review and validation","appropriate clinical context"],"input_types":["whole slide images","clinical history"],"output_types":["diagnostic recommendations","highlighted regions","confidence scores","decision support reports"],"categories":["pathology","clinical decision support","medical AI"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_4","uri":"capability://pathology.remote.expert.collaboration.and.case.review","name":"remote expert collaboration and case review","description":"Enables pathologists and experts to review and collaborate on complex cases remotely through digital pathology infrastructure. Facilitates sharing of whole slide images and annotations across distributed teams without physical slide transport.","intents":["I need to get expert opinion on a difficult case without sending physical slides","I want to enable remote consultation between pathologists at different locations","I need to streamline case review workflows for complex diagnostics"],"best_for":["academic medical centers","reference laboratories","multi-site hospital systems","research institutions"],"limitations":["requires digital pathology infrastructure at all sites","network bandwidth requirements for large images","regulatory compliance needed for remote diagnosis"],"requires":["digital pathology platform","WSI scanners","secure network infrastructure","appropriate regulatory approvals"],"input_types":["whole slide images","clinical annotations","diagnostic notes"],"output_types":["shared case reviews","expert annotations","collaborative reports"],"categories":["pathology","collaboration","digital pathology"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_5","uri":"capability://pathology.diagnostic.reproducibility.assessment","name":"diagnostic reproducibility assessment","description":"Measures and quantifies diagnostic consistency and reproducibility across pathologists and cases. Identifies sources of diagnostic variability and provides metrics to track improvement in diagnostic agreement and standardization.","intents":["I need to measure diagnostic variability across our pathology team","I want to track whether our diagnostic accuracy is improving over time","I need to identify cases where pathologists disagree and standardize approaches"],"best_for":["academic medical centers","quality assurance teams","research institutions","large reference labs"],"limitations":["requires multiple pathologist reviews for comparison","time-intensive to implement","requires standardized case selection"],"requires":["multiple pathologist reviews","case database","statistical analysis capabilities"],"input_types":["diagnostic reports","pathologist annotations","case data"],"output_types":["reproducibility metrics","agreement statistics","variability reports","quality dashboards"],"categories":["pathology","quality assurance","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_6","uri":"capability://pathology.batch.slide.analysis.and.workflow.automation","name":"batch slide analysis and workflow automation","description":"Processes multiple whole slide images in batch mode with automated analysis pipelines. Reduces manual review time and turnaround times for high-volume pathology operations by automating repetitive analysis tasks.","intents":["I need to process hundreds of slides efficiently without manual intervention","I want to reduce turnaround time for pathology reports","I need to automate routine analysis tasks to free up pathologist time"],"best_for":["reference laboratories","high-volume diagnostic centers","academic medical centers","research institutions"],"limitations":["requires upfront workflow configuration","batch processing may have latency","quality control still needed"],"requires":["digital pathology infrastructure","WSI scanners","computing resources","workflow configuration"],"input_types":["multiple whole slide images","batch processing parameters"],"output_types":["batch analysis reports","quantitative summaries","automated annotations"],"categories":["pathology","workflow automation","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_7","uri":"capability://research.research.grade.tissue.analysis.for.studies","name":"research-grade tissue analysis for studies","description":"Provides validated AI-powered analysis tools specifically designed for research applications including cohort studies, biomarker discovery, and histological feature quantification. Includes published validation studies demonstrating accuracy across multiple pathology domains.","intents":["I need validated AI tools for my research study on tissue characteristics","I want to quantify histological features across a large cohort of samples","I need published evidence that the analysis method is accurate for my research"],"best_for":["research scientists","academic institutions","clinical researchers","biomarker discovery teams"],"limitations":["primarily positioned for research, not clinical diagnosis","requires research-grade image quality","may require custom validation for specific studies"],"requires":["whole slide images","research protocol","appropriate tissue types","statistical analysis expertise"],"input_types":["whole slide images","research cohort data","study parameters"],"output_types":["quantitative analysis reports","statistical summaries","research datasets","publication-ready metrics"],"categories":["research","pathology","biomarker discovery"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_8","uri":"capability://digital.pathology.digital.pathology.infrastructure.integration","name":"digital pathology infrastructure integration","description":"Integrates with existing digital pathology workflows and laboratory information systems. Connects WSI scanners, image storage, and analysis tools into a unified platform for streamlined pathology operations.","intents":["I need to integrate AI analysis into our existing digital pathology system","I want to connect our WSI scanners and image storage to analysis tools","I need a unified platform for our entire digital pathology workflow"],"best_for":["academic medical centers","large reference laboratories","hospital systems","research institutions"],"limitations":["significant upfront infrastructure investment required","requires IT resources for integration","ongoing maintenance and support needed"],"requires":["WSI scanners","image storage infrastructure","LIS/EHR integration capability","IT support","cloud or on-premise computing"],"input_types":["WSI scanner data","LIS/EHR systems","image archives"],"output_types":["integrated workflow","unified analysis platform","connected data systems"],"categories":["digital pathology","infrastructure","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proscia__cap_9","uri":"capability://image.processing.stain.normalization.and.image.preprocessing","name":"stain normalization and image preprocessing","description":"Automatically normalizes variations in histological staining and image quality across different slides and scanners. Preprocesses whole slide images to ensure consistent input for downstream AI analysis regardless of staining variability.","intents":["I need to handle staining variations across different batches of slides","I want consistent analysis results despite differences in slide preparation","I need to normalize images from different scanners and labs"],"best_for":["reference laboratories","multi-site institutions","research studies","quality assurance teams"],"limitations":["effectiveness depends on staining consistency","may require calibration for new stain types","extreme variations may not be fully correctable"],"requires":["whole slide images","reference standards","stain information"],"input_types":["whole slide images","stain type metadata"],"output_types":["normalized images","preprocessed WSI data"],"categories":["image processing","pathology","quality control"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["whole slide image scanner","digital pathology infrastructure","cloud or on-premise computing resources","whole slide images","appropriate tissue staining","validated AI models for specific tissue types","high-quality whole slide images","appropriate tissue preparation and staining","domain knowledge for interpretation","pathologist review and validation"],"failure_modes":["requires WSI scanner infrastructure","requires significant storage capacity","processing time varies with image complexity","accuracy varies by tissue type and stain","requires training data for new tissue types","may require pathologist validation","feature extraction depends on image quality and staining consistency","requires domain expertise to interpret metrics","some features may require manual validation","positioned as decision support, not autonomous diagnosis","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.82,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.544Z","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=proscia","compare_url":"https://unfragile.ai/compare?artifact=proscia"}},"signature":"Q9z+Caz/e4lKwRTP2Pd0tZa4FPMTJ+ajuMEyOmPcjAz9q0LJcjTFV6DPIjDhUHxbIVmpFJ2jHTU2x74Iy6S7BQ==","signedAt":"2026-06-21T15:06:19.082Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/proscia","artifact":"https://unfragile.ai/proscia","verify":"https://unfragile.ai/api/v1/verify?slug=proscia","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"}}