--- name: bone-marrow-ai-agent description: "---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. license: MIT metadata: author: AI Group version: \"1.0.0\" created: \"2026-01-19\" compatibility:" --- --- 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. 2. **Automated Differential**: Rapid 500-cell differential counts from digital aspirate images. 3. **Dysplasia Detection**: AI recognition of dyserythropoiesis, dysgranulopoiesis, and dysmegakaryopoiesis. 4. **Blast Quantification**: Accurate blast percentage enumeration for AML/MDS classification. 5. **Biopsy Analysis**: Cellularity estimation, fibrosis grading, and infiltration pattern recognition. 6. **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). 2. **Preprocessing**: Color normalization, focus stacking, region of interest selection. 3. **Cell Detection**: Instance segmentation to identify individual cells. 4. **Classification**: CNN/CoAtNet model assigns cell type labels. 5. **Differential**: Aggregate counts and calculate percentages. 6. **Pattern Recognition**: Identify disease-associated morphological patterns. 7. **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**: ```bash 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 --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Hematology/Bone_Marrow_AI_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Hematology/Bone_Marrow_AI_Agent/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Data & Analytics, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Health Informatics, Medical Writer Specialties: API, Testing Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 06faf4e13d437eff03ba6557efc126ffa11bfc757bfe65b080edd5bf534dcffe Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.