bone-marrow-ai-agent

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---

name: 'bone-marrow-ai-agent'

description: 'AI-powered bone marrow morphology analysis, cell classification, and hematologic disorder diagnosis using deep learning on aspirate and biopsy images.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

Bone Marrow AI Agent

The Bone Marrow AI Agent provides comprehensive AI-driven analysis of bone marrow aspirate and biopsy specimens. It performs automated cell identification, differential counting, morphological assessment, and pattern recognition for hematologic disease diagnosis.

When to Use This Skill

• When performing automated bone marrow differential counts from aspirate smears.
• To identify morphological abnormalities (dysplasia, blasts, abnormal cells).
• For pattern recognition in myelodysplastic syndromes (MDS), leukemias, and other disorders.
• When assessing cellularity, fibrosis, and infiltration in trephine biopsies.
• To standardize morphological assessment across institutions.

Core Capabilities

1. Cell Classification: Deep learning identification and classification of 15+ bone marrow cell types with >95% accuracy.

1. Automated Differential: Rapid 500-cell differential counts from digital aspirate images.

1. Dysplasia Detection: AI recognition of dyserythropoiesis, dysgranulopoiesis, and dysmegakaryopoiesis.

1. Blast Quantification: Accurate blast percentage enumeration for AML/MDS classification.

1. Biopsy Analysis: Cellularity estimation, fibrosis grading, and infiltration pattern recognition.

1. Quality Assessment: Automated specimen adequacy and hemodilution detection.

Cell Types Classified

| Lineage | Cell Types | Key Features |

|---------|------------|--------------|

| Erythroid | Pronormoblast, basophilic, polychromatic, orthochromatic | Size, chromatin, cytoplasm color |

| Myeloid | Myeloblast, promyelocyte, myelocyte, metamyelocyte, band, seg | Granules, nuclear shape |

| Monocytic | Monoblast, promonocyte, monocyte | Nuclear folding, cytoplasm |

| Lymphoid | Lymphocyte, plasma cell | Size, chromatin density |

| Megakaryocytic | Megakaryocytes (all stages) | Size, nuclear lobation |

| Other | Mast cells, osteoblasts, osteoclasts | Distinctive morphology |

Workflow

1. Input: Bone marrow aspirate images (Wright-Giemsa stained) or biopsy sections (H&E).

1. Preprocessing: Color normalization, focus stacking, region of interest selection.

1. Cell Detection: Instance segmentation to identify individual cells.

1. Classification: CNN/CoAtNet model assigns cell type labels.

1. Differential: Aggregate counts and calculate percentages.

1. Pattern Recognition: Identify disease-associated morphological patterns.

1. Output: Differential count, morphology report, diagnostic suggestions.

Example Usage

User: "Analyze this bone marrow aspirate smear and provide a differential count with morphological assessment."

Agent Action:

python3 Skills/Hematology/Bone_Marrow_AI_Agent/bm_analyzer.py \
    --image aspirate_smear.tiff \
    --stain wright_giemsa \
    --target_cells 500 \
    --assess_dysplasia true \
    --model coatnet_bm_v2 \
    --output bm_report.json

Model Architecture

CoAtNet Hybrid Model:

• Combines CNN (local features) with Transformer (global context)
• Pre-trained on 100,000+ annotated bone marrow cells
• Achieves >95% accuracy on cell classification
• Real-time inference (<1 second per cell)

Training Data Sources:

• Munich AML Morphology Dataset (Matek et al.)
• Multi-institutional bone marrow collections
• Expert hematopathologist annotations

Diagnostic Pattern Recognition

| Pattern | Associated Conditions | AI Features |

|---------|----------------------|-------------|

| Increased blasts | AML, MDS, ALL | Blast%, CD34 correlation |

| Dysplastic features | MDS, AML-MRC | Hypolobation, ring sideroblasts |

| Left shift | Infection, CML, recovery | Myeloid maturation pyramid |

| Plasma cell infiltration | Myeloma, MGUS | Plasma cell%, morphology |

| Lymphoid aggregates | CLL, lymphoma | Pattern, location |

FDA-Cleared and Research Systems

| System | Approval | Application |

|--------|----------|-------------|

| CellaVision | FDA cleared | Peripheral blood and BM |

| Scopio Labs X100 | FDA cleared | Full-field digital morphology |

| Techcyte | Research | AI-powered hematology |

| Morphogo | Research | Deep learning cytology |

Quality Metrics

Performance Benchmarks:

• Cell classification accuracy: >95%
• Blast detection sensitivity: >98%
• Dysplasia recognition: >90% concordance with experts
• Processing speed: 500-cell differential in <2 minutes

Quality Flags:

• Hemodilution detection
• Specimen adequacy assessment
• Staining quality evaluation
• Artifacts and debris identification

Prerequisites

• Python 3.10+
• PyTorch with CoAtNet/ViT models
• OpenCV for image processing
• Digital pathology scanner or microscope camera

Related Skills

• Flow_Cytometry_AI - For immunophenotyping correlation
• AML_Classification - For WHO/ICC AML subtyping
• MDS_Diagnosis - For MDS-specific analysis

Clinical Integration

1. LIS Interface: HL7/FHIR export of results
2. Quality Assurance: Flagging for pathologist review
3. Documentation: Automated report generation
4. Audit Trail: All AI decisions logged

Author

AI Group - Biomedical AI Platform

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