{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_azyri","slug":"azyri","name":"Azyri","type":"webapp","url":"https://azyri.com","page_url":"https://unfragile.ai/azyri","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_azyri__cap_0","uri":"capability://image.visual.automated.bone.age.assessment.from.radiographic.images","name":"automated bone age assessment from radiographic images","description":"Processes pediatric hand/wrist X-ray images through a deep learning model trained on skeletal maturity datasets to automatically compute bone age in months, eliminating manual Greulich-Pyle or Tanner-Whitehouse chart interpretation. The system likely uses convolutional neural networks (CNNs) to detect epiphyseal plates, carpal bones, and metacarpal morphology, then maps detected features to standardized bone age scales. Outputs a quantitative age estimate with confidence metrics, reducing inter-observer variability inherent in radiologist manual assessment.","intents":["I need to quickly assess skeletal maturity in a pediatric patient without waiting for radiologist availability","I want to reduce subjective interpretation variability when multiple clinicians assess the same X-ray","I need to screen large populations of children for growth disorders or endocrine conditions at point-of-care"],"best_for":["Pediatric radiologists in under-resourced clinics seeking to accelerate turnaround time","Endocrinologists managing growth hormone deficiency cases who need rapid skeletal maturity confirmation","Orthopedic surgeons in developing regions planning growth-modulation procedures"],"limitations":["Accuracy depends on image quality, positioning, and patient age range — poor-quality or non-standard radiographs may degrade predictions","Model trained on specific populations may show reduced accuracy for underrepresented ethnic groups or skeletal variants","No built-in confidence thresholding mechanism — unclear when predictions fall outside reliable operating range","Requires regulatory clearance (FDA 510(k), CE marking) before clinical deployment in regulated markets"],"requires":["Digital X-ray image in DICOM or standard raster format (JPEG, PNG)","Mobile device or web browser with internet connectivity for cloud inference","Azyri API key or account credentials for authentication"],"input_types":["medical-image (DICOM, JPEG, PNG)","radiographic-metadata (patient age, sex, clinical indication)"],"output_types":["structured-data (bone age in months, confidence score, percentile)","clinical-report (text summary with assessment)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_1","uri":"capability://data.processing.analysis.multi.standard.bone.age.scale.mapping.and.reporting","name":"multi-standard bone age scale mapping and reporting","description":"Translates raw CNN predictions into multiple standardized bone age assessment frameworks (Greulich-Pyle, Tanner-Whitehouse, Fels method) through a post-processing layer that maps detected skeletal features to each scale's reference data. The system maintains lookup tables or regression models for each standard, allowing clinicians to receive bone age estimates in their preferred clinical framework. Output includes age estimate, standard error, and percentile ranking relative to healthy reference populations.","intents":["I need bone age reported in Greulich-Pyle format because my institution's protocols use that standard","I want to compare this patient's skeletal maturity across multiple assessment methods to validate consistency","I need percentile data to communicate growth status to families in a clinically meaningful way"],"best_for":["Pediatric endocrinology clinics using institution-specific bone age standards in clinical protocols","Research teams comparing bone age assessment methods across populations","Clinicians in international settings where different standards are preferred by region"],"limitations":["Conversion between standards introduces additional error — no single standard is universally accurate across all populations","Percentile data depends on reference population used; may not reflect diversity of patient demographics","Requires manual selection of assessment standard by user — no automatic standard recommendation based on clinical context"],"requires":["Underlying bone age prediction from automated assessment capability","Reference datasets for each supported standard (Greulich-Pyle, Tanner-Whitehouse, Fels)","Demographic data (age, sex) to select appropriate reference population"],"input_types":["structured-data (raw bone age prediction, patient demographics)"],"output_types":["structured-data (bone age in months per standard, standard error, percentile)","clinical-report (formatted assessment in selected standard)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_2","uri":"capability://tool.use.integration.mobile.responsive.web.interface.for.point.of.care.image.upload.and.assessment","name":"mobile-responsive web interface for point-of-care image upload and assessment","description":"Delivers a responsive web application optimized for mobile devices (iOS, Android) and tablets that enables clinicians to capture or upload radiographic images directly from the point-of-care environment without requiring PACS integration or desktop workstations. The interface includes image preview, annotation tools for marking regions of interest, and real-time assessment results displayed on-device. Architecture likely uses progressive web app (PWA) patterns with service workers for offline capability and local caching of assessment results.","intents":["I need to assess a child's bone age immediately in the clinic without returning to the radiology department","I want to upload X-rays from my mobile device without installing specialized software or connecting to hospital IT infrastructure","I need to access assessment results offline in areas with intermittent internet connectivity"],"best_for":["Pediatric clinics in developing regions with limited IT infrastructure and no PACS systems","Mobile health (mHealth) programs conducting population screening in remote areas","Orthopedic or endocrinology practices seeking to reduce dependency on radiology department workflows"],"limitations":["Mobile browsers have variable support for high-resolution medical image handling — may compress or degrade DICOM metadata","Offline capability limited by device storage; cannot cache large model weights on typical smartphones","Touch-based annotation tools less precise than mouse-based interfaces for marking small anatomical structures","No HIPAA-compliant local storage on consumer devices — assessment results must be encrypted in transit and on server"],"requires":["Mobile device or tablet with modern web browser (iOS Safari 12+, Android Chrome 80+)","Internet connectivity for initial assessment (offline viewing of cached results only)","Camera or image upload capability (native file picker or camera integration)"],"input_types":["image-file (JPEG, PNG, or DICOM via web upload)","camera-capture (direct radiograph photography from mobile device)"],"output_types":["web-ui (interactive assessment results, downloadable report)","structured-data (JSON assessment data for integration with EHR)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_3","uri":"capability://data.processing.analysis.batch.processing.and.population.screening.workflows","name":"batch processing and population screening workflows","description":"Enables bulk assessment of multiple radiographic images in a single workflow, processing dozens or hundreds of pediatric X-rays sequentially with aggregated reporting and statistical summaries. The system queues images, distributes inference across available compute resources, and generates population-level reports showing age distribution, outliers, and screening outcomes. Likely implements asynchronous job queuing with progress tracking and webhook callbacks for integration with external systems.","intents":["I need to screen 500 children in a growth disorder study and assess their bone ages in bulk rather than one-by-one","I want to generate a population report showing bone age distribution across age groups for epidemiological analysis","I need to identify outliers (children with significantly advanced or delayed skeletal maturity) for follow-up clinical evaluation"],"best_for":["Research teams conducting large-scale pediatric growth studies with hundreds of radiographs","Public health programs screening populations for endocrine disorders or malnutrition","Retrospective studies analyzing historical radiograph archives for skeletal maturity trends"],"limitations":["Batch processing introduces latency — results not immediately available, typically 1-24 hours depending on queue depth","No real-time progress visibility into individual image processing status within batch","Aggregated reporting assumes homogeneous population — may mask subgroup differences if demographic stratification not requested","Requires API key or authenticated batch submission endpoint — not available through web UI"],"requires":["Azyri API access with batch processing endpoint","CSV or JSON manifest file listing image paths or URLs","Authentication credentials (API key)","Webhook endpoint for asynchronous result delivery (optional but recommended)"],"input_types":["structured-data (CSV/JSON manifest with image references and metadata)","image-files (batch of DICOM or raster images)"],"output_types":["structured-data (JSON array of individual assessments with batch statistics)","report (population-level summary with age distribution, percentiles, outlier flags)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_4","uri":"capability://text.generation.language.clinical.report.generation.with.standardized.formatting.and.export","name":"clinical report generation with standardized formatting and export","description":"Automatically generates formatted clinical reports from bone age assessments that include patient demographics, assessment timestamp, bone age estimate with confidence intervals, comparison to age-matched norms, and clinical interpretation guidance. Reports are exportable in multiple formats (PDF, HL7 CDA, plain text) suitable for integration into electronic health records (EHRs) or printing for paper charts. The system uses templating to ensure consistent formatting and includes optional fields for clinician notes and recommendations.","intents":["I need a formatted report to include in the patient's medical record that meets our institution's documentation standards","I want to export assessment results in HL7 format to integrate directly into our EHR system","I need to print a report for the patient's family explaining the bone age assessment and what it means for their child's growth"],"best_for":["Pediatric clinics with EHR systems requiring structured clinical documentation","Hospitals needing to integrate bone age assessments into existing radiology reporting workflows","Clinicians communicating assessment results to families in non-technical language"],"limitations":["Report templates are fixed — limited customization for institution-specific formatting or branding","HL7 CDA export requires manual mapping of Azyri data model to institution's HL7 schema — no automatic EHR integration","Clinical interpretation guidance is generic and not personalized to patient's specific clinical context or comorbidities","No audit trail or digital signature capability for regulatory compliance (FDA 21 CFR Part 11)"],"requires":["Completed bone age assessment with confidence metrics","Patient demographics (name, DOB, medical record number)","Selected report format (PDF, HL7 CDA, text)","Optional: institution-specific report template or branding"],"input_types":["structured-data (assessment results, patient demographics)"],"output_types":["document (PDF report)","structured-data (HL7 CDA XML, JSON)","text (plain text report)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_5","uri":"capability://safety.moderation.confidence.scoring.and.uncertainty.quantification.for.assessment.reliability","name":"confidence scoring and uncertainty quantification for assessment reliability","description":"Provides per-assessment confidence scores and uncertainty estimates that indicate the reliability of the bone age prediction, derived from model ensemble disagreement, input image quality metrics, and distance from training data distribution. The system flags assessments with low confidence (e.g., poor image quality, unusual skeletal anatomy) that may require radiologist review. Confidence scores are calibrated against radiologist agreement rates to provide clinically meaningful reliability metrics rather than raw model probabilities.","intents":["I need to know which assessments are reliable enough to use clinically versus which ones need radiologist review","I want to understand the uncertainty in the bone age estimate so I can communicate confidence to the patient's family","I need to identify cases where the AI model is uncertain due to image quality or unusual anatomy"],"best_for":["Clinicians using AI assessments as decision support rather than autonomous diagnosis","Quality assurance teams monitoring AI system performance and identifying failure modes","Researchers validating AI predictions against radiologist gold standard"],"limitations":["Confidence calibration depends on training data distribution — may overestimate confidence on out-of-distribution cases","No explicit mechanism to flag specific anatomical anomalies (e.g., skeletal dysplasia) that violate model assumptions","Confidence thresholds for clinical acceptance are not defined — clinicians must determine their own acceptance criteria","Ensemble-based uncertainty estimation adds computational cost and latency (~20-30% overhead)"],"requires":["Completed bone age assessment from automated model","Image quality metrics (sharpness, contrast, positioning score)","Ensemble predictions from multiple model variants or checkpoints"],"input_types":["structured-data (model predictions, image quality scores)"],"output_types":["structured-data (confidence score 0-100, uncertainty bounds, flags for review)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_6","uri":"capability://data.processing.analysis.demographic.stratified.reference.population.selection.and.norm.comparison","name":"demographic-stratified reference population selection and norm comparison","description":"Automatically selects age- and sex-matched reference populations from diverse demographic cohorts to compute percentile rankings and growth norms, rather than using a single universal reference. The system maintains separate reference datasets for different ethnic groups, geographic regions, and nutritional status categories, allowing bone age estimates to be contextualized within the patient's specific demographic group. Percentile output indicates whether skeletal maturity is advanced, normal, or delayed relative to peers.","intents":["I need to know if this child's bone age is normal for their age and sex, not just a raw number","I want to compare this patient's skeletal maturity to children from their own ethnic/geographic background, not a generic reference","I need percentile data to identify children with significantly advanced or delayed skeletal development for follow-up"],"best_for":["Pediatric endocrinologists managing growth disorders across diverse populations","Population health programs in specific geographic regions seeking locally-relevant norms","Researchers studying skeletal maturity variation across ethnic groups"],"limitations":["Reference datasets for underrepresented populations may be sparse or unavailable, forcing fallback to generic norms","Demographic selection requires accurate patient metadata (age, sex, ethnicity, geographic origin) — missing data reduces accuracy","Percentile interpretation assumes normal distribution within reference group — may not hold for small or skewed populations","No built-in mechanism to flag when reference population is too small to provide reliable percentiles"],"requires":["Patient demographics (age, sex, ethnicity, geographic region)","Reference datasets for selected demographic strata","Bone age estimate from automated assessment"],"input_types":["structured-data (patient demographics, bone age estimate)"],"output_types":["structured-data (percentile ranking, z-score, comparison to demographic norms)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_7","uri":"capability://image.visual.image.quality.assessment.and.preprocessing.validation","name":"image quality assessment and preprocessing validation","description":"Analyzes input radiographic images for technical quality metrics (sharpness, contrast, positioning, artifact presence) before processing, rejecting or flagging images that fall below clinical standards. The system computes quality scores across multiple dimensions (anatomical positioning, exposure adequacy, motion blur, foreign objects) and provides feedback to guide image recapture if needed. Preprocessing includes automatic rotation correction, contrast normalization, and artifact detection to optimize input for the bone age assessment model.","intents":["I need to know if the X-ray image is good enough quality to produce a reliable bone age assessment","I want feedback on how to retake the radiograph if the first attempt is too poor quality","I need to automatically correct image orientation and contrast to optimize assessment accuracy"],"best_for":["Point-of-care settings where radiographs are captured by non-specialist staff without formal radiology training","Mobile health programs with variable image acquisition equipment and conditions","Clinics seeking to reduce failed assessments due to poor image quality"],"limitations":["Quality thresholds are fixed and may not adapt to different imaging equipment or clinical contexts","No mechanism to detect clinically significant findings (e.g., fractures, bone lesions) that affect assessment validity","Preprocessing (contrast normalization, rotation) may alter subtle anatomical details important for accurate assessment","Quality feedback is generic — does not provide specific guidance on how to improve positioning or exposure"],"requires":["Input radiographic image (DICOM or raster format)","Quality assessment model trained on reference radiographs"],"input_types":["image-file (DICOM, JPEG, PNG radiograph)"],"output_types":["structured-data (quality score, per-dimension quality metrics, flags)","image-file (preprocessed image with corrections applied)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_8","uri":"capability://memory.knowledge.longitudinal.tracking.and.growth.trajectory.analysis","name":"longitudinal tracking and growth trajectory analysis","description":"Stores historical bone age assessments for individual patients and computes growth velocity and skeletal maturity trajectories over time, enabling detection of abnormal growth patterns or changes in skeletal development rate. The system compares current assessment to prior assessments, calculates bone age advancement relative to chronological age progression, and flags cases with unusual acceleration or deceleration. Longitudinal data supports clinical decision-making for growth hormone therapy, orthopedic interventions, or endocrine workup.","intents":["I need to track how this child's skeletal maturity is changing over time to detect abnormal growth patterns","I want to know if the child's bone age is advancing faster or slower than expected based on prior assessments","I need to identify children whose growth trajectory has changed significantly since the last assessment"],"best_for":["Pediatric endocrinologists managing long-term growth hormone therapy who need to monitor skeletal maturity progression","Orthopedic surgeons planning growth-modulation procedures who need baseline and follow-up skeletal maturity data","Research teams studying longitudinal skeletal development in cohorts with repeated imaging"],"limitations":["Requires multiple assessments over time — not useful for single-point evaluation","Longitudinal analysis assumes consistent image quality and positioning across time points — variation may confound growth velocity estimates","No built-in mechanism to account for clinical interventions (growth hormone therapy, orthopedic surgery) that may affect skeletal maturity trajectory","Requires secure patient data storage and linkage across assessments — raises HIPAA and data governance concerns"],"requires":["Multiple bone age assessments for same patient over time (minimum 2, ideally 3+)","Consistent patient identifier linking assessments across time","Secure data storage with patient privacy protections","Timestamps for each assessment"],"input_types":["structured-data (historical assessments with timestamps)"],"output_types":["structured-data (growth velocity, trajectory analysis, anomaly flags)","visualization (growth curve plot showing bone age vs. chronological age over time)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_azyri__cap_9","uri":"capability://tool.use.integration.integration.with.electronic.health.records.ehr.via.standardized.apis.and.hl7.messaging","name":"integration with electronic health records (ehr) via standardized apis and hl7 messaging","description":"Provides RESTful API and HL7 v2/FHIR messaging interfaces for bidirectional integration with hospital EHR systems, enabling automated order placement, result delivery, and clinical workflow embedding. The system supports HL7 ORM (order) messages for receiving bone age assessment orders from EHR, returns results via ORU (observation result) messages, and supports FHIR DiagnosticReport resources for modern EHR systems. Integration includes patient demographic lookup, order tracking, and result status notifications.","intents":["I want to order bone age assessments directly from our EHR without manually logging into a separate system","I need assessment results to automatically populate into the patient's EHR chart without manual data entry","I want to integrate bone age assessments into our radiology workflow so they appear in the radiologist's worklist"],"best_for":["Large hospital systems with existing EHR infrastructure (Epic, Cerner, Meditech) seeking to embed AI assessments into clinical workflows","Radiology departments automating result delivery and reducing manual transcription","Health systems pursuing interoperability and FHIR-based data exchange"],"limitations":["EHR integration requires custom development for each EHR vendor — no single universal connector","HL7 message mapping must be customized to institution's specific data model and coding standards (ICD-10, LOINC)","No built-in audit trail or digital signature for regulatory compliance (FDA 21 CFR Part 11) — requires EHR's audit capabilities","API rate limiting and latency may impact real-time order processing in high-volume settings"],"requires":["RESTful API endpoint or HL7 messaging gateway","API authentication (OAuth 2.0, API key, or mutual TLS)","EHR system with HL7 v2 or FHIR capability","Custom integration development or middleware (e.g., Mirth Connect, Boomi)"],"input_types":["structured-data (HL7 ORM order messages, FHIR ServiceRequest resources)","patient-demographics (MRN, DOB, name from EHR)"],"output_types":["structured-data (HL7 ORU result messages, FHIR DiagnosticReport resources)","clinical-data (results populated into EHR chart)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Digital X-ray image in DICOM or standard raster format (JPEG, PNG)","Mobile device or web browser with internet connectivity for cloud inference","Azyri API key or account credentials for authentication","Underlying bone age prediction from automated assessment capability","Reference datasets for each supported standard (Greulich-Pyle, Tanner-Whitehouse, Fels)","Demographic data (age, sex) to select appropriate reference population","Mobile device or tablet with modern web browser (iOS Safari 12+, Android Chrome 80+)","Internet connectivity for initial assessment (offline viewing of cached results only)","Camera or image upload capability (native file picker or camera integration)","Azyri API access with batch processing endpoint"],"failure_modes":["Accuracy depends on image quality, positioning, and patient age range — poor-quality or non-standard radiographs may degrade predictions","Model trained on specific populations may show reduced accuracy for underrepresented ethnic groups or skeletal variants","No built-in confidence thresholding mechanism — unclear when predictions fall outside reliable operating range","Requires regulatory clearance (FDA 510(k), CE marking) before clinical deployment in regulated markets","Conversion between standards introduces additional error — no single standard is universally accurate across all populations","Percentile data depends on reference population used; may not reflect diversity of patient demographics","Requires manual selection of assessment standard by user — no automatic standard recommendation based on clinical context","Mobile browsers have variable support for high-resolution medical image handling — may compress or degrade DICOM metadata","Offline capability limited by device storage; cannot cache large model weights on typical smartphones","Touch-based annotation tools less precise than mouse-based interfaces for marking small anatomical structures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:29.134Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=azyri","compare_url":"https://unfragile.ai/compare?artifact=azyri"}},"signature":"zguukC414EqFHLgI0PSd2LHtjUi2jcTG2wn8OknyAppa8SIh2yGpuohNySCL+R8xxGXv4fWNNrjfxCjHXsoTCA==","signedAt":"2026-06-21T17:55:55.906Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/azyri","artifact":"https://unfragile.ai/azyri","verify":"https://unfragile.ai/api/v1/verify?slug=azyri","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"}}