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| Pricing | Paid | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Mobile-first deployment architecture enables offline-capable or low-bandwidth operation in resource-limited settings, contrasting with cloud-only competitors; likely uses edge inference or lightweight model quantization to run on commodity smartphones without requiring specialized PACS infrastructure
vs alternatives: Faster than manual Greulich-Pyle assessment (seconds vs. 5-10 minutes per case) and more consistent than inter-observer radiologist interpretation, but lacks published validation data against gold-standard cohorts that competitors like Carestream or Agfa have published
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.
Unique: Implements multi-standard mapping layer that allows single CNN model to output results in Greulich-Pyle, Tanner-Whitehouse, and Fels frameworks simultaneously, rather than training separate models per standard; reduces model maintenance burden and ensures consistency across standards
vs alternatives: Provides flexibility across clinical standards that single-standard tools lack, but adds complexity and potential for inter-standard conversion error that specialized single-standard tools avoid
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.
Unique: Progressive web app architecture with service worker caching enables offline assessment viewing and result persistence without requiring native app installation, contrasting with traditional mobile app competitors that require app store distribution and updates
vs alternatives: More accessible than desktop PACS-integrated solutions in resource-limited settings, but less precise image handling and annotation capability than specialized medical imaging software
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.
Unique: Implements asynchronous batch job queuing with webhook callbacks for result delivery, enabling integration into research data pipelines without polling; contrasts with single-image-at-a-time competitors that require sequential API calls
vs alternatives: Dramatically faster than manual assessment for large cohorts (hours vs. weeks of radiologist time), but introduces latency and requires API integration that single-image web UI tools avoid
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.
Unique: Generates multi-format reports (PDF, HL7 CDA, text) from single assessment data structure, enabling flexible integration with diverse EHR systems; includes clinical interpretation guidance templates that contextualize bone age relative to age-matched norms
vs alternatives: More comprehensive reporting than raw API output that competitors provide, but lacks deep EHR integration that specialized radiology reporting systems (Nuance, Agfa) offer through native connectors
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.
Unique: Calibrates confidence scores against radiologist agreement rates rather than raw model probabilities, providing clinically interpretable reliability metrics; flags low-confidence cases for mandatory radiologist review rather than silently returning unreliable predictions
vs alternatives: More transparent uncertainty quantification than black-box competitors, but requires ongoing calibration against radiologist ground truth to maintain clinical validity
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.
Unique: Maintains separate reference datasets for diverse demographic groups rather than using single universal norms, enabling equitable assessment across populations; automatically selects appropriate reference based on patient demographics
vs alternatives: More equitable than single-reference competitors for diverse populations, but requires ongoing curation of demographic-specific reference data that generic tools avoid
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.
Unique: Implements multi-dimensional quality scoring (positioning, exposure, sharpness, artifacts) with automated preprocessing (rotation, contrast normalization) rather than simple pass/fail validation; provides actionable feedback for image recapture
vs alternatives: More robust to variable image acquisition conditions than competitors that assume high-quality PACS images, but adds preprocessing latency and may introduce artifacts through normalization
+2 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Azyri scores higher at 44/100 vs Supabase at 42/100. Azyri leads on adoption and quality, while Supabase is stronger on ecosystem. However, Supabase offers a free tier which may be better for getting started.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities